DOI: 10.3897/jucs.150245

Authors: Fernando Ornelas, Alejandro H. García, Alejandro Santiago, Salvador Ibarra Martínez, José Antonio Castán Rocha, Fausto Balderas, Julio Laria-Menchaca, Mayra Guadalupe Treviño-Berrones

Abstract: The Internet Shopping Optimization Problem with multiple item Units (ISHOP-U) is a recently proven NP-Hard variant of the classical ISHOP, which considers buying more than one unit of the same product. In this work, we propose a new set of instances where the prices of the products follow a Gaussian distribution, which is more realistic in a competitive market than the original instances with random uniform prices. We compute the optimal values of the previous uniform and new Gaussian instances using an Integer Programming formulation in CPLEX. In addition, we also propose two new low-complexity heuristics, the first not metaheuristics approaches proposed for the ISHOP-U, which use a linear representation instead of the original matrix candidate solution, achieving better results than the previous Evolutionary Algorithms for the ISHOP-U from the state-of-the-art.

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DOI: 10.3897/jucs.114357

Authors: Samit Bhanja, Banani Ghose, Abhishek Das

Abstract: Recently time series forecasting has become one of the prime application areas of climatology, economics and industries. Many research works are conducted to forecast the time series more accurately. But few of them are concentrated on predicting the time series over an extended future horizon, and there is also a scope to improve their forecasting accuracy. This work proposes a multi-step-ahead foresting method to produce a stable and accurate forecasting result for the extended future horizon. Firstly, a deep learning-based forecasting model is proposed to predict the time series. Secondly, a fuzzy time series-based error correction model is implemented to enhance the prediction performance of the deep learning model. Here to optimize all the fuzzy time series (FTS) parameters in an integrated way, an integrated butterfly optimization (FTS-IBO) algorithm is proposed. In this study, two different types of real-world multivariate time series datasets are used to analyze the forecasting performance of the proposed model. The performance of the proposed FTS-IBO algorithm is compared with the traditional butterfly optimization (FTS-BO) algorithm. The experimental results show that the FTS-IBO technique is superior to the FTS-BO technique. The forecasting performance of the proposed model has also compared the other state-of-the-art models, and the simulation results exhibit that the proposed model produces a more accurate prediction performance for multi-step-ahead time series forecasting problems compared to other models.

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DOI: 10.3897/jucs.75357

Authors: Jorge Ruiz Quiñones, Antonio J. Fernández Leiva

Abstract: Usually, human participation is required in order to provide feedback during the game tuning or balancing process. Moreover, this is commonly an iterative process in which play-testing is required as well as human interaction for gathering all important information to improve and tune the game components’ specification. In this paper, a mechanism is proposed to accelerate this process and reduce significantly the costs of it, contributing with a solution to perform the game parameter tuning and game balancing using search algorithms and artificial intelligence (AI) techniques. The process is executed in a fully automated way, and just requires a game specification written in a particular video game description language. Automated play-testing, and game’s feedback information analysis, are related to perform game parameters’ tuning and balancing, leading to offer a solution for the problem of optimizing a video game specification. Recently, XVGDL, a new language for specifying video games which is based on the eXtensible Markup Language (XML), has been presented. This paper uses XVGDL+, an extension of this lan- guage that incorporates new components to specify, within the video game specification, desirable goals or requirements to be evaluated after each game execution. A prototypical implementation of a Game Engine (termed XGE+) was also presented. This game engine not only enables the execution of an XVGDL+ game specification but also provides feedback information once the game has finished.The paper demonstrates that the combination of XVGDL+ with XGE+ offers a powerful mechanism for helping solving game AI research problems, in this case, the game tuning of video game parameters, with respect to initial optimization goals. These goals, as one of the particularities of the proposal presented here, are included within the game specification, minimizing the input of the process.As a practical proof of it, two experiments have been conducted to optimize a game specification written in XVGDL via a hill climbing local search method, in a fully automated way.

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DOI: 10.3897/jucs.94162

Authors: Zhiling Yang

Abstract: Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In educational settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and learning have received increased attention from the relevant education departments as an integral aspect of the new curriculum reform. The environment of English teaching and learning is undergoing extraordinary changes as a result of the constant improvement and extension of teaching level and scale, as well as the growth of Internet information technology. As a result, the current research aims to look into techniques for efficiently using AI (artificial intelligence) apps to teach and learn English from the perspective of university students. This research can measure the levels as well as effectiveness of the employment of AI applications for teaching English based on deep learning techniques. There, the NLP based language enhancement has been carried out using Character-level recurrent neural network with back Propagation neural network (Cha_RNN_BPNN) based classification. With the help of this DL (deep learning) technique, it is possible to use AI methods to assist teachers in analysing and diagnosing students' English learning behaviour, replacing teachers in part to answer students' questions in a timely manner, and automatically grading assignments during the English teaching process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level for Readability, Cosine Similarity for Semantic Coherence, gradient change of NN, validation accuracy, and training accuracy of the proposed technique.

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DOI: 10.3897/jucs.94160

Authors: Xin Ma

Abstract: Higher vocational education is a self-contained method of higher education that is aligned with global productivity and economic development. Its goal is to develop talented workers who contribute significantly to the economy and industry. Teaching analysis, teaching strategy, teaching practice, and assessment are all part of the course design process in high vocational education. Teaching assessment is one of the most effective methods for improving the quality of course teaching among teaching processes. This research proposes novel techniques in English teaching based on artificial intelligence for course selection based on students' feedback. Here, the dataset has been collected based on the students’ feedback on courses for Higher Vocational Education in English teaching. This dataset has been processed to remove invalid data, missing values, and noise. The processed data features have been dimensionality reduction integrated with K-means neural network. And the extracted features have been classified with higher accuracy using recursive elimination-based convolutional neural network. Based on this feedback data classification, recommendation for courses in Higher Vocational Education in English teaching has been suggested. The experimental analysis shows various students' feedback dataset validation and training in terms of accuracy of 96%, precision of 92%, recall of 93%, RMSE of 68%, and computational time of 65%.

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DOI: 10.3897/jucs.72900

Authors: Mohd Khaled Y. Shambour, Esam A. Khan

Abstract: About three million Muslims are traveling annually to Makkah in Saudi Arabia to perform the rituals of Hajj (i.e. the pilgrimage), the fifth pillar of Islam. It requires the pilgrims to move to several holy sites while performing the Hajj ritual, including Mina, Arafat, and Muzdalifah sites. However, pilgrims spend most of their time in prepared tent-camps at the Mina site during the days of Hajj. Among the challenges that the organizers face in the Hajj is the distribution of pilgrims over the camps of Mina while considering a range of constraints, which is considered a real-world optimization problem. This paper introduces a hyper-heuristic approach to optimize the distribution process of pilgrims over Mina tent-camps in an efficient manner, named the hyper-heuristic Mina tents distribution algorithm (HyMTDA). The proposed algorithm, iteratively, selects one heuristic among four predefined low-level heuristics to produce a new solution; thereafter the late move acceptance strategy is applied as a judgment to accept or reject the new solution. The performed simulations show that the proposed HyMTDA can effectively explore the search space and avoid falling into local minima during the iterations process. Moreover, comparisons show that HyMTDA outperforms other heuristic algorithms in the literature in terms of solution quality and convergence rate.

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DOI: 10.3897/jucs.70484

Authors: Canan Tastimur, Erhan Akin

Abstract: Deep Learning has been widely used in image-based applications such as object classification, object detection, and object recognition in recent years. Classifying highly similar objects is a very difficult problem. It is difficult to classify datasets in this situation where object similarity between classes and differences between classes are high. In this study, Siamese Convolution Neural Network, which is a similarity measurement-based network, has been practiced to classify 6 types of screws, 5 types of nuts, and 7 types of bolts that are very similar to each other. In addition, this neural network formed with the One-Shot Learning technique is trained. Thanks to the OSL technique, there is no need to use large data sets. Also, there is no need to use large amounts of data from each class. Adding a new class to be classified is also made easier by the use of the OSL technique. The performance results of the proposed method are manifested in detail in the article.

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DOI: 10.3897/jucs.65202

Authors: Adem Tuncer

Abstract: The N-puzzle problem is one of the most classical problems in mathematics. Since the number of states in the N-puzzle is equal to the factorial of the number of tiles, traditional algorithms can only provide solutions for small-scale ones, such as 8-puzzle. Various uninformed and informed search algorithms have been applied to solve the N-puzzle, and their performances have been evaluated. Apart from traditional methods, artificial intelligence algorithms are also used for solutions. This paper introduces a new approach based on a meta-heuristic algorithm with a solving of the 15-puzzle problem. Generally, only Manhattan distance is used as the heuristic function, while in this study, a linear conflict function is used to increase the effectiveness of the heuristic function. Besides, the puzzle was divided into subsets named pattern database, and solutions were obtained for the subsets separately with the artificial bee colony (ABC) algorithm. The proposed approach reveals that the ABC algorithm is very successful in solving the 15-puzzle problem.

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DOI: 10.3897/jucs.2020.040

Authors: Łukasz Apiecionek, Jacek Czerniak, Dawid Ewald, Mateusz Biedziak

Abstract: There is currently an era of Internet of Things in the computer systems, which consists in connecting all possible devices to the Internet in order to provide them with new functionalities and thus { to improve the user's life standard. One of such solutions could be Smart Home. The possibility of monitoring inner environment is required for such solutions. Such monitoring provides potential for e.g. better heating control. The authors of this paper propose some heating control method with Fuzzy Logic. The proposed method was tested in a special climate chamber. The authors provided conclusions at the end of the paper.

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DOI: 10.3217/jucs-025-06-0647

Authors: Waseem Rawat, Zenghui Wang

Abstract: In recent years, deep convolutional neural networks (DCNNs) have delivered notable successes in visual tasks, and in particular, image classification related applications. However, they are sensitive to the selection of their architectural and learning hyperparameters, which impose an exponentially large search space on modern DCNN models. Traditional hyperparameter selection methods include manual model tuning, grid, or random search but these require expert domain knowledge or are computationally burdensome. On the other hand, Bayesian optimization and evolutionary inspired techniques have surfaced as viable alternatives to the hyperparameter problem. In this work, an alternative automated system that combines the advantages of evolutionary processes and state-of-the-art Bayesian optimization is proposed. Specifically, the search space is first partitioned into separate discrete-architectural, and continuous and categorical learning parameter subspaces, which are then efficiently traversed by a stochastic genetic search applied to the former, combined with a genetic-Bayesian search of the latter. Several sequential experiments on prominent image classification tasks reveal that the proposed method results in overall classification accuracy improvements over several well-established techniques, and significant computational costs reductions compared to brute force computation.

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DOI: 10.3217/jucs-024-06-0711

Authors: Quan Zou, Chi-Wei Chen, Hao-Chen Chang, Yen-Wei Chu

Abstract: Gelatinases proteases with the ability to cleave the extracellular matrix (ECM). Two types of gelatinases exist: Gelatinase A, also referred to as matrix metalloproteinase-2 (MMP-2), and gelatinase B, also referred to as matrix metalloproteinase-9 (MMP-9). MMP-2 and MMP-9 degrade ECM, which is highly expressed during tumor metastasis. The poor therapeutic effects of inhibitors can be attributed to the high structural homology shared by members of the matrix metalloproteinase family. The highly similar structures of these proteases preclude the specific binding of inhibitor drugs. Moreover, the regulatory pathways of MMP-2 and MMP-9 remain poorly understood. An accurate model for the prediction of substrates and the cleavage sites of gelatinases should be developed to enable screening and exploring the physiological and pathological mechanisms of these enzymes. Prediction is based on various types of information on binary integration, physical-chemical properties, protein stability, solvent accessibility, and protein secondary structure. In this study, the first level of the prediction model was constructed on the basis of intergroup differences and support vector machine. Predictive probability was then taken as the characteristic of the second level of the prediction model, which was constructed using different machine-learning methods. The Mathews correlation coefficients of the MMP-2 and MMP-9 prediction models were 89.4% and 64.4%, respectively. The physical-chemical properties of the active sites of MMP-2 and MMP-4 were selected for analysis. The completion of this prediction system will aid the discovery of regulatory paths and novel applications of MMP-2 and MMP-9, as well as provide references for drug design.

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DOI: 10.3217/jucs-024-04-0460

Authors: Dawid Połap

Abstract: The increasing need to implement systems as identity verification meant that safe and effective verification systems have become one of the main branches of modern scientiffic research. In this paper, the idea of identity verification support system is presented. The main novelty of this work is to present the technique of feature extraction that can be implemented simultaneously for processed image files as well sound ones. Proposed method was tested due to achieve the highest possible accuracy of the selected neural classifier. The obtained results were discussed in terms of advantages and disadvantages for practical applications.

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DOI: 10.3217/jucs-024-03-0322

Authors: O-Joun Lee, Yunhu Kim, Hoang Nguyen, Jai Jung

Abstract: The goal of this paper is to discover latent social events from social media for sensitively understanding social opinions that appeared within a city. The latent social event indicates a regional and inconspicuous social event which is mostly buried under macroscopic trends or issues. To detect the latent social event, we propose three methods: i) discovering areas-ofinterest (AOIs), ii) allocating social texts to the AOIs, and iii) detecting social events in each AOI. The AOIs can be composed by grouping social texts which are topically and spatially homogeneous. To make the AOIs dynamic and incremental, we use windows for allocating a social text to an adequate AOI. Lastly, the latent social events are detected from the AOI on the basis of keywords and temporal distribution of the social texts. Although, in this study, we limited the proposed method into analyzing social media, it could be extended to detecting events among agents/things/sensors.

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DOI: 10.3217/jucs-023-07-0652

Authors: Jianhua Xiao, Ying Liu, Shuai Zhang, Ping Chen

Abstract: In this paper, an adaptive membrane evolutionary algorithm (AMEA) is proposed, which combines a dynamic membrane structure and a differential evolution with the adaptive mutation factor. In the AMEA, the feasibility proportion method is used to dynamically adjust the size of the elementary membrane in the optimization process. The results of the experimental indicate that the proposed algorithm outperforms other evolutionary algorithms on five well-known constrained engineering optimization problems.

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DOI: 10.3217/jucs-023-07-0619

Authors: Jianfeng Qiu, Juan Xie, Fan Cheng, Xuefeng Zhang, Lei Zhang

Abstract: Abstract Social Spider Optimization (SSO) algorithm is a swarm intelligence optimizationalgorithm based on the mating behavior of social spiders. Numerical simulation results have shown that SSO outperformed some classical swarm intelligence algorithms such as ParticleSwarm Optimization (PSO) algorithm and Artificial Bee Colony (ABC) algorithm and so on. However, there are still some deficiencies about SSO algorithm, such as the poor balancebetween exploration and exploitation. To this end, an improved SSO algorithm named wDESSO is proposed for global optimization, which can balance exploration and exploitation effectively.Specifically, a weighting factor changing with iteration is introduced to control and adjust the search scope of SSO algorithm dynamically. After social-spiders have completed their search,a mutation operator is then suggested for jumping out of the potential local optimization, thus can further strengthen the ability of global search. The experimental results on a set of standardbenchmark functions demonstrate the effectiveness of wDESSO in solving complex numerical optimization problems.

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DOI: 10.3217/jucs-023-05-0479

Authors: Ademir Constantino, Candido Ferreira Xavier De Mendonca Neto, Silvio De Araujo, Dario Landa-Silva, Rogério Calvi, Allainclair Flausino dos Santos

Abstract: The bus driver scheduling problem (BDSP) under study consists in finding a set of duties that covers the bus schedule from a Brazilian public transportation bus company with the objective of minimizing the total cost. A deterministic 2-phase heuristic algorithm is proposed using multiple assignment problems that arise from a model based on a weighted multipartite graph. In the first phase, the algorithm constructs an initial feasible solution by solving a number of assignment problems. In the second phase, the algorithm attempts to improve the solution by two different procedures. One procedure takes the whole set of duties and divides them in a set of partial duties which are recombined. The other procedure seeks to improve single long duties by eliminating the overtime time and inserting it into another duty. Computational tests are performed using large-scale real-world data with more than 2,300 tasks and random instances extracted from real data. Three different objective functions are analyzed. The overall results indicate that the proposed approach is competitive to solve large BDSP.

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DOI: 10.3217/jucs-022-10-1298

Authors: Virginia Yannibelli, Marcelo Armentano, Franco Berdun, Anala Amandi

Abstract: Collaborative learning team building is a fundamental, difficult and time-consuming task in educational environments. In this paper, we address a collaborative learning team building problem that considers two valuable grouping criteria usually considered by teachers. One of these criteria considers the understanding levels of the students with respect of the topics of a given course, and is based on building well-balanced teams in terms of the understanding levels of their members. The other criterion considers the interest levels of the students with respect of the topics of a given course, and is based on building well-balanced teams in terms of the interest levels of their members. The problem addressed has been recognised as an NP-Hard optimization problem. To solve the problem, we propose a steady-state evolutionary algorithm. This algorithm aims to organize the students taking a given course into teams in such a way that the two grouping criteria of the problem are optimized. The performance of the algorithm is evaluated on nine problem instances with different levels of complexity, and is compared with that of the only algorithm previously proposed for solving the addressed problem. The obtained results show that the steady-state evolutionary algorithm significantly outperforms the previous algorithm.

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DOI: 10.3217/jucs-022-06-0874

Authors: Piotr Jędrzejowicz, Aleksander Skakovski

Abstract: Differential evolution (DE) is a stochastic global optimization method, that has been under continuous development during the past two decades. It has been recognized that preserving the diversification of population can significantly improve the performance of DE. Although, several results and approaches to population diversification have been proposed, it seems that this issue still has a potential for development. In this paper we have studied experimentally the possibility of increasing the performance of DE. Our investigation aims at identifying how the performance of DE depends on such factors as population diversity, size and number of fitness function evaluations carried out by DE to yield a solution. In our experiments we diversified the population in an intensive manner using the proposed decloning procedure carried out in cycles, and also through increasing the population size. The choice of how to preserve the diversification may depend on restrictions imposed on the population size, response time, and the quality of solutions that should be met by a specific implementation of the algorithm. The obtained results allowed us to propose a performance improvement policy that might noteworthy improve both the efficacy and response time of the algorithm. The discrete-continuous scheduling with continuous resource discretisation was used as the test problem.

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DOI: 10.3217/jucs-022-06-0802

Authors: Bay Vo, Tam Tran, Tzung-Pei Hong, Nguyen Minh

Abstract: Using soft set theory for mining maximal association rules based on the concept of frequent maximal itemsets which appear maximally in many records has been developed in recent years. This method has been shown to be very effective for mining interesting association rules which are not obtained by using methods for regular association rule mining. There have been several algorithms developed to solve the problem, but overall, they retain weaknesses related to the use of memory as well as mining time. In this paper, we propose an effective strategy for maximal rules mining based on soft set theory that consists of the following steps: 1) Build tree Max_IT_Tree where each node contains maximal itemsets X, the category of X, the set of transactions in which X is maximal, and the support of the maximal itemsets X for each category. 2) From the tree Max_IT_Tree built in previous steps, build a tree Max_Item_IT_Tree so that each maximal itemset has child nodes where each node contains items with categories different from the category of maximal itemsets. 3) Generate maximal association rules which satisfy predefined minimum M-support (min M-sup) and minimum M-confidence (min M-conf) thresholds.

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DOI: 10.3217/jucs-021-09-1143

Authors: Gerald De Grancy

Abstract: Distribution planning in urban areas faces a lack of available parking space at customer sites. One approach to mitigate the issue is to cluster nearby customers around known parking locations. Deliveries from each parking location to its assigned customers occur by a second mode of transport (for example by foot). These lead to long service times at each of the clusters. However, long service times in conjunction with time windows can lead to inefficient routes as nearby customer clusters with overlapping service times may not be connected. As a consequence, assigning additional service workers to each vehicle is a strategy to reduce service times. The additional workers can do the last mile deliveries in parallel to reduce the service time of a cluster and hence permit more efficient routing. The trade-off between paying additional workers to reduce costs for vehicles and driving creates a new decision problem called the vehicle routing problem with time windows and multiple service workers (VRPTWMS).

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DOI: 10.3217/jucs-019-09-1257

Authors: Pilar Garrido, Irene Ruiz, Miguel Gómez-Nieto

Abstract: This paper describes an alert system for people monitoring based on multi-agent using maps. This system monitors the users' physical context using their mobile phone. The data acquisition is made using the available sensors on mobile phone. A set of agents on mobile phones are responsible for collecting, processing and sending data to the server. Another set of agents on server stores the data and checks the preconditions of the restrictions associated with the user, in order to trigger the appropriate alarms. These alarms are sent not only to the user that violates a restriction, but also to the one responsible for supervising the person monitored. The supervisor can control all the supervised people through a map interface with functionality such as sending a SMS or making a call directly from the map. The applicability of the system will be illustrated with an example for Alzheimer patient monitoring. These patients will carry on normal activity in the home environment or home for the elderly, monitored by their family or by nurses.

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DOI: 10.3217/jucs-019-04-0502

Authors: Tomasz Kajdanowicz, Przemyslaw Kazienko

Abstract: Multi-label classification is a machine learning task that assumes that a data instance may be assigned with multiple number of class labels at the same time. Modelling of this problem has become an important research topic recently. This paper revokes AdaBoostSeq multi-label classification algorithm and examines it in order to check its robustness properties. It can be stated that AdaBoostSeq is able to result with quite stable Hamming Loss evaluation measure regardless of the size of input and output space.

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DOI: 10.3217/jucs-018-14-1906

Authors: Antonio Bajuelos, Santiago Canales, Gregorio Hernández, Mafalda Martins

Abstract: In this paper we focus on approximate solutions to solve a new class of Art Gallery Problems inspired by wireless localization. Instead of the usual guards we consider wireless devices whose signal can cross a certain number, k, of walls. These devices are called k-transmitters. We propose an algorithm for constructing the visibility region of a k-transmitter located on a point of a simple polygon. Then we apply a hybrid metaheuristic strategy to tackle the problem of minimizing the number of k-transmitters, located at vertices, that cover a given simple polygon, and compare its performance with two pure metaheuristics. We conclude that the approximate solutions obtained with the hybrid strategy, for 2-transmitters and 4-transmitters, on simple polygons, monotone polygons, orthogonal polygons and monotone orthogonal polygons, are better than the solutions obtained with the pure strategies.

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DOI: 10.3217/jucs-018-13-1821

Authors: Gexiang Zhang, Fen Zhou, Xiaoli Huang, Jixiang Cheng, Marian Gheorghe, Florentin Ipate, Raluca Lefticaru

Abstract: This paper presents the application of membrane algorithms to broadcasting problems, which are regarded as NP-hard combinatorial optimization problems. A membrane algorithm, called HPSOPS, is proposed by appropriately combining membrane systems and a hybrid particle swarm optimization with wavelet mutation (HPSOWM). HPSOPS is designed with the hierarchical membrane structure and transformation/communication-like rules of membrane systems, the representation of individuals and the evolutionary mechanism of HPSOWM. Experimental results from various broadcasting problems show that HPSOPS performs better than its counterpart HPSOWM and genetic algorithms reported in the literature, in terms of search capability, efficiency, solution stability and precision.

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DOI: 10.3217/jucs-018-07-0917

Authors: Luneque Junior, Nadia Nedjah, Luiza Mourelle

Abstract: Network-on-Chip (NoC) have been used as an interesting option in design of communication infrastructures for embedded systems, providing a scalable structure and balancing the communication between cores. Because several data packets can be transmitted simultaneously through the network, an efficient routing strategy must be used in order to avoid congestion delays. In this paper, ant colony algorithms were used to find and optimize routes in a mesh-based NoC. The routing optimization is driven by the minimization of total latency in packets transmission. The simulation results show the effectiveness of the ant colony inspired routing by comparing it with general purpose algorithms for deadlock free routing under different traffic patterns.

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DOI: 10.3217/jucs-017-14-2048

Authors: Chen-Fang Tsai, Weidong Li, Anne James

Abstract: This paper presents a new methodology for improving the efficiency and generality of Genetic Algorithms (GA). The methodology provides the novel function of adaptive parameter adjustment during each evolution generation of GA. The important characteristics of the methodology are mainly from the following two aspects: (1) superior performance members in GA are preserved and inferior performance members are deteriorated to enhance search efficiency towards optimal solutions; (2) adaptive crossover and mutation management is applied in GA based on the transformation functions to explore wider spaces so as to improve search effectiveness and algorithm robustness. The research was successfully applied for a luggage design chain to generate optimal solutions (minimized lifecycle cost). Experiments were conducted to compare the work with the prior art to demonstrate the characteristics and advantages of the research.

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DOI: 10.3217/jucs-016-14-1882

Authors: Pablo Rabanal, Ismael Rodriguez, Fernando Rubio

Abstract: River Formation Dynamics (RFD) is an evolutionary computation methodbased on copying how drops form rivers by eroding the ground and depositing sediments. Given a cost-evaluated graph, we apply RFD to find a way to connect a givenset of origins with a given destination in such a way that distances from origins to the destination are minimized (thus improving the quality of service) but costs to build theconnecting infrastructure are minimized (thus reducing investment expenses). After we prove the NP-completeness of this problem, we apply both RFD and an Ant ColonyOptimization (ACO) approach to heuristically solve it, and some experimental results are reported.

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DOI: 10.3217/jucs-016-06-0956

Authors: Behrang Zadjabbari, Pornpit Wongthongtham, Farookh Hussain

Abstract: For many years, physical asset indicators were the main evidence of an organization’s successful performance. However, the situation has changed following the revolution of information technology in the knowledge-based economy and in the new ideas in economy; knowledge assets are a critical strategic resource in economy. Knowledge management [KM] tools have become very important and in order to gain a competitive advantage, it is necessary to create, store, share and apply knowledge. Knowledge sharing is one of the key issues in knowledge management. One of the main challenges facing pioneer firms is to provide an effective strategy to exchange knowledge formally or informally. In this paper, we will discuss the effectiveness of knowledge sharing and our proposal for an effective knowledge sharing strategy. Based on a review of knowledge sharing literature, we will focus more on the trust and knowledge contexts as key issues in knowledge sharing. Trust is the most important issue when creating a relationship, knowledge sharing and partnership. Moreover, there are a number of forms that trust can take in these relationships and the most regularly cited forms are competence and benevolence trust. In this paper, we will explore these two forms of trust and will examine their role in knowledge sharing and how they can be defined and measured. On the other hand, we will apply ontologies to explore the knowledge context. Ontologies are used in widespread application areas particularly to provide a semantically shared domain knowledge in a declarative formalism for intelligent reasoning. Even ontology enables knowledge sharing; however, the complexity of knowledge being conceptualized in the ontology is critical to the success of knowledge sharing efforts. Other factors like trust in the source of knowledge can also affect knowledge transfer. In this paper, we propose metrics to measure the complexity of ontology for knowledge sharing. Finally, the effectiveness of our proposed knowledge sharing methodology is presented both using a fuzzy-inference engine and a crisp system.

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DOI: 10.3217/jucs-015-13-2547

Authors: Yi-Chung Chen, Jeen-Shing Wang

Abstract: This paper presents a Hammerstein-Wiener recurrent neural network (HWRNN) with a systematic identification algorithm for identifying unknown dynamic nonlinear systems. The proposed HWRNN resembles the conventional Hammerstein-Wiener model that consists of a linear dynamic subsystem that is sandwiched in between two nonlinear static subsystems. The static nonlinear parts are constituted by feedforward neural networks with nonlinear functions and the dynamic linear part is approximated by a recurrent network with linear activation functions. The novelties of our network include: 1) the structure of the proposed recurrent neural network can be mapped into a state-space equation; and 2) the state-space equation can be used to analyze the characteristics of the identified network. To efficiently identify an unknown system from its input-output measurements, we have developed a systematic identification algorithm that consists of parameter initialization and online learning procedures. Computer simulations and comparisons with some existing models have been conducted to demonstrate the effectiveness of the proposed network and its identification algorithm.

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DOI: 10.3217/jucs-015-13-2446

Authors: Dunwei Gong, Xin Yao, Jie Yuan

Abstract: Interactive genetic algorithms (IGAs) are effective methods to solve optimization problems with implicit or fuzzy indices. But human fatigue problem, resulting from evaluation on individuals and assignment of their fitness, is very important and hard to solve in IGAs. Aiming at solving the above problem, an interactive genetic algorithm with an individual fitness not assigned by human is proposed in this paper. Instead of assigning an individual fitness directly, we record time to choose an individual from a population as a satisfactory or unsatisfactory one according to sensitiveness to it, and its fitness is automatically calculated by a transformation from time space to fitness space. Then subsequent genetic operation is performed based on this fitness, and offspring is generated. We apply this algorithm to fashion design, and the experimental results validate its efficiency.

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DOI: 10.3217/jucs-015-05-1119

Authors: Naixue Xiong, Jing He, Jong Park, Donald Cooley, Yingshu Li

Abstract: Pervasive smart computing environments make people get accustomed to convenient and secure services. The overall goal of this research is to classify vehicles along the I215 freeway in Salt Lake City, USA. This information will be used to predict future roadway needs and the expected life of a roadway. The classification of vehicles will be performed by a synthesis of multiple sets of features. All feature sets have not yet been determined; however, one such set will be the reduced wavelet transform of the image of a vehicle. In order to use such a feature, it is necessary that the image be normalized with respect to size, position, and so on. For example, a car in the right most lane in an image will appear smaller than one in the left most lane, because the right most lane is closest to the camera. Likewise, a vehicle’s size will vary depending on where in a lane its image is captured. In our case, the image capture area for each lane is approximately 100 feet of roadway. A goal of this paper is to normalize the image of a vehicle so that regardless of its lane or position in a lane, the features will be approximately the same. The wavelet transform itself will not be used directly for recognition. Instead, it will be input to a neural network and the output of the neural network will be one element of the feature set used for recognition.

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DOI: 10.3217/jucs-015-04-0765

Authors: Qun Niu, Taijin Zhou, Shiwei Ma

Abstract: This paper presents a quantum-inspired immune algorithm (QIA) for Hybrid flow shop problems (HFSP) to minimize makespan. Since HFSP have been proved to be NP-hard in a strong sense when the objective is to minimize the makespan, an effective immune algorithm (IA) is used to solve the problems. IA is a kind of evolutional computation strategies, which is developed on the basis of a real immune mechanism in the human body, and has been employed to tackle complex scheduling problems and produce a reasonable manufacturing schedule. In order to achieve better results, the standard IA is combined with quantum algorithm (QA), which is based on Q-bit and uses quantum rotation gate to update. A real number representation is proposed to convert the Q-bit representation to job permutation for evaluating value of solutions. The proposed QIA can overcome the limitations of IA, quicken up convergence speed and improve the solution. Forty one benchmarks are examined to validate the efficiency of the proposed algorithm. The computational experiments show that the proposed QIA can also obtain both better and more robust results than IA and QA.

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DOI: 10.3217/jucs-015-04-0722

Authors: Xuesong Wang, Minglin Hao, Yuhu Cheng, Ruhai Lei

Abstract: Differential evolution (DE) algorithm puts emphasis particularly on imitating the microscopic behavior of individuals, while estimation of distribution algorithm (EDA) tries to estimate the probabilistic distribution of the entire population. DE and EDA can be extended to multi-objective optimization problems by using a Pareto-based approach, called Pareto DE (PDE) and Pareto EDA (PEDA) respectively. In this study, we describe a novel combination of PDE and PEDA (PDE-PEDA) for multi-objective optimization problems by taking advantage of the global searching ability of PEDA and the local optimizing ability of PDE, which can, effectively, maintain the balance between exploration and exploitation. The basic idea is that the offspring population of PDE-PEDA is composed of two parts, one part of the trial solution generated originates from PDE and the other part is sampled in the search space from the constructed probabilistic distribution model of PEDA. A scaling factor Pr used to balance contributions of PDE and PEDA can be adjusted in an on-line manner using a simulated annealing method. At an early evolutionary stage, a larger Pr should be adopted to ensure PEDA is used more frequently, whereas at later stage, a smaller Pr should be adopted to ensure that offspring is generated more often using PDE. The hybrid algorithm is evaluated on a set of benchmark problems and the experimental results show that PDE-PEDA outperforms the NSGA-II and PDE algorithms.

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DOI: 10.3217/jucs-014-15-2582

Authors: Yudel Gómez, Rafael Bello, Amilkar Puris, María García, Ann Nowe

Abstract: In this paper we propose a new approach to Swarm Intelligence called Two-Step Swarm Intelligence. The basic idea is to split the heuristic search performed by agents into two stages. In the first step the agents build partial solutions which, are used as initial states in the second step. We have studied the performance of this new approach for the Feature Selection Problem by using Ant Colony Optimization and Particle Swarm Optimization. The feature selection is based on the reduct concept of the Rough Set Theory. Experimental results obtained show that Two-step approach improves the performance of ACO and PSO metaheuristics when calculating reducts in terms of computation time cost and the quality of reducts.

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DOI: 10.3217/jucs-014-15-2566

Authors: Khadiza Tahera, Raafat Ibrahim, Paul Lochert

Abstract: T his paper proposes a variant of genetic algorithm - GADYM, Genetic Algorithm with Gender-Age structure, DYnamic parameter tuning and Mandatory self perfection scheme. The motivation of this algorithm is to increase the diversity throughout the search procedure and to ease the difficulties associated with the tuning of GA parameters and operators. To promote diversity , GADYM combines the concept of gender and age in individuals of a traditional Genetic Algorithm and implements the self perfection scheme through sharing. To ease the parameter tuning process, the proposed algorithm uses dynamic environment in which heterogeneous crossover and selection techniques are used and parameters are updated based on deterministic rules. Thus, GADYM uses a combination of genetic operators and variable parameter values whereas a traditional GA uses fixed values of those. The experim ental results of the proposed algorithm based on a mechanical design problem show promising result.

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DOI: 10.3217/jucs-014-15-2549

Authors: Guillermo Molina, Enrique Alba, El-Ghazali Talbi

Abstract: Wireless Sensor Networks (WSN) allow, thanks to the use of small wireless devices known as sensor nodes, the monitorization of wide and remote areas with precision and liveness unseen to the date without the intervention of a human operator. For many WSN applications it is fundamental to achieve full coverage of the terrain monitored, known as sensor field. The next major concerns are the energetic efficiency of the network, in order to increase its lifetime, and having the minimum possible number of sensor nodes, in order to reduce the network cost. The task of placing the sensor nodes while addressing these objectives is known as WSN layout problem. In this paper we address a WSN layout problem instance in which full coverage is treated as a constraint while the other two objectives are optimized using a multiobjective approach. We employ a set of multi-objective optimization algorithms for this problem where we define the energy efficiency and the number of nodes as the independent optimization objectives. Our results prove the efficiency of multi-objective metaheuristics to solve this kind of problem and encourage further research on more realistic instances and more constrained scenarios.

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DOI: 10.3217/jucs-014-15-2491

Authors: Elizabeth Ferreira Gouvêa Goldbarg, Marco Goldbarg, Cristine Schmidt

Abstract: Evolutionary algorithms are effective search tools for tackling difficult optimization problems. In this paper an algorithm based on living processes where cooperation is the main evolutionary strategy is applied to the Prize Collecting Steiner Tree Problem, an NP-hard combinatorial optimization problem. The Transgenetic Algorithm presented here is hybridized with path-relinking. Computational results of an experiment performed with benchmark instances are reported. The results obtained for the Prize Collecting Steiner Tree Problem with the application of the hybrid Transgenetic Algorithm are compared with the results of three effective approaches presented previously. The computational experiment shows that the proposed approach is very competitive concerning both quality of solution and processing time.

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DOI: 10.3217/jucs-014-15-2471

Authors: Sadiq Sait, Khawar Khan, Mustafa Ali

Abstract: This paper discusses the parallelization of Stochastic Evolution (StocE) metaheuristic, for a distributed parallel environment. VLSI cell placement is used as an optimization problem. A comprehensive set of parallelization approaches are tested and an effective strategy is identified in terms of two underlying factors: workload division and the effect of parallelization on metaheuristic's search intelligence. The strategies are compared with parallelization of another similar evolutionary metaheuristic called Simulated Evolution (SimE). The role of the two mentioned underlying factors is discussed in parallelization of StocE.

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DOI: 10.3217/jucs-013-10-1449

Authors: Fangmin Dong, Renbin Xiao, Yifang Zhong, Yong Liu

Abstract: An approach to polygonal approximation of regular digital curves based on PSO algorithm is presented. In this paper, each particle corresponds to a candidate solution to the polygonal approximation problem, which is represented as a binary vector. The offset error of centroid between the original curve and the approximation polygon, and the variance of distance error for each approximation segment are adopted in the fitness function to evaluate the feasibility degree of the candidate solution. The sigmoid function of iteration times is used as the acceleration factors instead of the constant factors to improve the global searching characteristics. Experimental results show that the proposed approach can get suitable approximation results for preserving the features of original curves.

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DOI: 10.3217/jucs-013-09-1309

Authors: Hongbo Liu, Ajith Abraham

Abstract: Recently, Particle Swarm Optimization (PSO) algorithm has exhibited good performance across a wide range of application problems. A quick review of the literature reveals that research for solving the Quadratic Assignment Problem (QAP) using PSO approach has not much been investigated. In this paper, we design a hybrid meta-heuristic fuzzy scheme, called as variable neighborhood fuzzy particle swarm algorithm (VNPSO), based on fuzzy particle swarm optimization and variable neighborhood search to solve the QAP. In the hybrid fuzzy scheme, the representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. A new mapping is introduced between the particles in the swarm and the problem space in an efficient way. We also attempt to theoretically prove that the variable neighborhood particle swarm algorithm converges with a probability of 1 towards the global optimal. The performance of the proposed approach is evaluated and compared with other four different algorithms. Empirical results illustrate that the approach can be applied for solving quadratic assignment problems effectively.

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DOI: 10.3217/jucs-013-06-0701

Authors: Jacques Robin, Jairson Vitorino, Armin Wolf

Abstract: Most automated reasoning tasks with prac tical applications can be automatically reformulated into a constraint solving task. A constraint programming platform can thus act as a unique, underlying engine to be reused for mu ltiple automated reasoning tasks in intelligent agents and systems. We identify six key requirements for such platform: expressive task modeling language, rapid solving method custom ization and combination, adaptive solving method, user-friendly solution explanation, efficient execution, and seamless integration within larger systems and practical applications. We then propose a novel, model-driven, component and rule-based architecture for such a platform that better satisfies as a whole this set of requirements than those of currently available platforms.

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DOI: 10.3217/jucs-012-04-0432

Authors: Tonio Biondi, Angelo Ciccazzo, Vincenzo Cutello, Santo Antona, Giuseppe Nicosia, Salvatore Spinella

Abstract: The paper concerns the design of evolutionary algorithms and pattern search methods on two circuit design problems: the multi-objective optimization of an Operational Transconductance Amplifier and of a fifth-order leapfrog filter. The experimental results obtained show that evolutionary algorithms are more robust and effective in terms of the quality of the solutions and computational effort than classical methods. In particular, the observed Pareto fronts determined by evolutionary algorithms has a better spread of solutions with a larger number of nondominated solutions when compared to the classical multi-objective techniques.

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DOI: 10.3217/jucs-011-10-1645

Authors: Fei Cao, Barrett Bryant, Rajeev Raje, Andrew Olson, Mikhail Auguston, Wei Zhao, Carol Burt

Abstract: Component-based software composition offers a development approach with reduced time-to-market and cost while achieving enhanced productivity, quality and maintainability. Existent work on the composition paradigm focuses on static composition, which is not sufficient in a distributed environment, in which both constituent components and the assembled distributed system are subject to dynamic adaptation. This paper presents two types of dynamic composition for distributed components: assertive and autonomous over a .NET based Web Services environment. Three case studies are provided to illustrate the use of assertive and autonomous composition.

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DOI: 10.3217/jucs-011-01-0193

Authors: Jisun Park, K. Barber

Abstract: Information quality assurance under the existence of uncertainty can be investigated in the context of soft security, where an agent maintains trustworthiness evaluations of its information sources to assist in the evaluation of incoming information quality from those sources. Since dependency inherently exists in a system where agents do not have self-sufficient sensing or data collection capabilities, finding an appropriate set of information sources is important for assuring the quality of information and for increasing the agent's goal achievement. This research proposes an approach for selecting information sources as partners. In order to increase the efficiency and the accuracy, we use trustworthiness, information cost and goal coverage as the metrics for information valuation while adopting a lazy exploration of information sources combination space. Experimental results show that the proposed approach increases the efficiency and results in quality information acquisition.

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DOI: 10.3217/jucs-010-12-1597

Authors: Rajat Arora, Michael Hsiao

Abstract: We propose a novel technique to improve SAT-based Combinational Equivalence Checking (CEC). The idea is to perform a low-cost preprocessing that will statically induce global signal relationships into the original CNF formula of the miter circuit under verification, and hence reduce the complexity of the SAT instance. This efficient and effective preprocessing quickly builds up the implication graph for the miter circuit under verification, yielding a large set of direct, indirect and extended backward implications. These two-node implications spanning the entire circuit are converted into binary clauses, and they are added to the miter CNF formula. The added clauses constrain the search space of the SAT solver and provide correlation among the different variables, which enhances the Boolean Constraint Propagation (BCP). Experimental results on large and difficult ISCAS'85, ISC AS'89 (full scan) and ITC'99 (full scan) CEC instances show that our approach is independent of the state-of-the-art SAT solver used, and that the added clauses help to achieve not eworthy speedup for each of the cases. Also, comparison with Hyper-Resolution (Hypre), Non-Increasing Variable Elimination Resolution (NIVER) and the propositional formula checker HeerHugo, suggests that our technique is more powerful, yielding non-trivial clauses that significantly simplify the SAT instance complexity.

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DOI: 10.3217/jucs-010-12-1562

Authors: Fadi Aloul, Igor Markov, Karem Sakallah

Abstract: The increasing popularity of SAT and BDD techniques in formal hardware verification and automated synthesis of logic circuits encourages the search for additional speedups. Since typical SAT and BDD algorithms are exponential in the worst-case, the structure of realworld instances is a natural source of improvements. While SAT and BDD techniques are often presented as mutually exclusive alternatives, our work points out that both can be improved via the use of the same structural properties of instances. Our proposed methods are based on efficient problem partitioning and can be easily applied as pre-processing with arbitrary SAT solvers and BDD packages without modifying the source code of SAT/BDD tools. Finding a better variable ordering is a well recognized problem for both SAT solvers and BDD packages. Currently, the best variable-ordering algorithms are dynamic, in the sense that they are invoked many times in the course of the host algorithm that solves SAT or manipulates BDDs. Examples include the DLCS ordering for SAT solvers and variable sifting during BDD manipulations. In this work we propose a universal variable-ordering algorithm MINCE (MIN Cut Etc.) that pre-processes a given Boolean formula in CNF. MINCE is completely independent from target SAT algorithms and in some cases outperforms both the variable state independent decaying sum (VSIDS) decision heuristic for SAT and variable sifting for BDDs. We argue that MINCE tends to capture structural properties of Boolean functions arising from real-world applications. Our contribution is validated on the ISCAS circuits and the DIMACS benchmarks. Empirically, our technique often outperforms existing SAT/BDD techniques by a factor of two or more. Our results motivate the search for better dynamic ordering heuristics and combined static/dynamic techniques.

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DOI: 10.3217/jucs-008-08-0739

Authors: Michael Barley, Hans Guesgen, Gareth Karl

Abstract: This paper describes the architecture of a route finding system that computes an optimal route between two given locations efficiently and that considers user preferences when doing so. The basis of the system is an A* algorithm that applies heuristics such as the air distance heuristic or the Manhattan heuristic to compute the shortest path between the two locations. Since A* is not tractable in general, island search is used to divide the problem into smaller problems, which can be solved more easily. In addition to that, search control rules are introduced to express user preferences about the routes to be considered during the search.

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DOI: 10.3217/jucs-006-04-0422

Authors: Craig Eldershaw, Stephen Cameron

Abstract: Motion planning is a field of growing importance as more and more computer controlled devices are being used. Many different approaches exist to motion planning|none of them ideal in all situations. This paper considers how to convert a general motion planning problem into one of global optimisation. We regard the general problem as being the classical configuration space findpath problem, but assume that the configurations of the device can be bounded by a hierarchy of hyper-spheres rather than being explicitly computed. A program to solve this problem has been written employing Genetic Algorithms. This paper describes how this was done, and some preliminary results of using it.

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