Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00114
Pedro Roque, V. Pedro, Daniel Diaz, Salvador Abreu
Recently, we developed the Parallel Heterogeneous Architecture Constraint Toolkit (PHACT), which is a multi-threaded constraint solver capable of using all the available devices which are compatible with OpenCL, in order to speed up the constraint satisfaction process. In this article, we introduce an evolution of PHACT which includes the ability to execute FlatZinc and MiniZinc models, as well as architectural improvements which boost the performance in solving CSPs, especially when using GPUs.
{"title":"Improving Constraint Solving on Parallel Hybrid Systems","authors":"Pedro Roque, V. Pedro, Daniel Diaz, Salvador Abreu","doi":"10.1109/ICTAI.2018.00114","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00114","url":null,"abstract":"Recently, we developed the Parallel Heterogeneous Architecture Constraint Toolkit (PHACT), which is a multi-threaded constraint solver capable of using all the available devices which are compatible with OpenCL, in order to speed up the constraint satisfaction process. In this article, we introduce an evolution of PHACT which includes the ability to execute FlatZinc and MiniZinc models, as well as architectural improvements which boost the performance in solving CSPs, especially when using GPUs.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127183451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00157
Leonidas Akritidis, Athanasios Fevgas, P. Tsompanopoulou, Panayiotis Bozanis
In the big data era, the efficient processing of large volumes of data has became a standard requirement for both organizations and enterprises. Since single workstations cannot sustain such tremendous workloads, MapReduce was introduced with the aim of providing a robust, easy, and fault-tolerant parallelization framework for the execution of applications on large clusters. One of the most representative examples of such applications is the machine learning algorithms which dominate the broad research area of data mining. Simultaneously, the recent advances in hardware technology led to the introduction of high-performing alternative devices for secondary storage, known as Solid State Drives (SSDs). In this paper we examine the perfor-mance of several parallel data mining algorithms on MapReduce clusters equipped with such modern hardware. More specifically, we investigate standard dataset preprocessing methods including vectorization and dimensionality reduction, and two supervised classifiers, Naive Bayes and Linear Regression. We compare the execution times of these algorithms on an experimental cluster equipped with both standard magnetic disks and SSDs, by employing two different datasets and by applying several different cluster configurations. Our experiments demonstrate that the usage of SSDs can accelerate the execution of machine learning methods by a margin which depends on the cluster setup and the nature of the applied algorithms.
{"title":"Investigating the Efficiency of Machine Learning Algorithms on MapReduce Clusters with SSDs","authors":"Leonidas Akritidis, Athanasios Fevgas, P. Tsompanopoulou, Panayiotis Bozanis","doi":"10.1109/ICTAI.2018.00157","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00157","url":null,"abstract":"In the big data era, the efficient processing of large volumes of data has became a standard requirement for both organizations and enterprises. Since single workstations cannot sustain such tremendous workloads, MapReduce was introduced with the aim of providing a robust, easy, and fault-tolerant parallelization framework for the execution of applications on large clusters. One of the most representative examples of such applications is the machine learning algorithms which dominate the broad research area of data mining. Simultaneously, the recent advances in hardware technology led to the introduction of high-performing alternative devices for secondary storage, known as Solid State Drives (SSDs). In this paper we examine the perfor-mance of several parallel data mining algorithms on MapReduce clusters equipped with such modern hardware. More specifically, we investigate standard dataset preprocessing methods including vectorization and dimensionality reduction, and two supervised classifiers, Naive Bayes and Linear Regression. We compare the execution times of these algorithms on an experimental cluster equipped with both standard magnetic disks and SSDs, by employing two different datasets and by applying several different cluster configurations. Our experiments demonstrate that the usage of SSDs can accelerate the execution of machine learning methods by a margin which depends on the cluster setup and the nature of the applied algorithms.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131988639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00156
Usman Malik, Mukesh Barange, Julien Saunier, A. Pauchet
Automatic dialogue act annotation of speech utterances is an important task in human-agent interaction in order to correctly interpret user utterances. Speech utterances can be transcribed manually or via Automatic Speech Recognizer (ASR). In this article, several Machine Learning models are trained on manual and ASR transcriptions of user utterances, using bag of words and n-grams feature generation approaches, and evaluated on ASR transcribed test set. Results show that models trained using ASR transcriptions perform better than algorithms trained on manual transcription. The impact of irregular distribution of dialogue acts on the accuracy of statistical models is also investigated, and a partial solution to this issue is shown using multimodal information as input.
{"title":"Performance Comparison of Machine Learning Models Trained on Manual vs ASR Transcriptions for Dialogue Act Annotation","authors":"Usman Malik, Mukesh Barange, Julien Saunier, A. Pauchet","doi":"10.1109/ICTAI.2018.00156","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00156","url":null,"abstract":"Automatic dialogue act annotation of speech utterances is an important task in human-agent interaction in order to correctly interpret user utterances. Speech utterances can be transcribed manually or via Automatic Speech Recognizer (ASR). In this article, several Machine Learning models are trained on manual and ASR transcriptions of user utterances, using bag of words and n-grams feature generation approaches, and evaluated on ASR transcribed test set. Results show that models trained using ASR transcriptions perform better than algorithms trained on manual transcription. The impact of irregular distribution of dialogue acts on the accuracy of statistical models is also investigated, and a partial solution to this issue is shown using multimodal information as input.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134641744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00074
Angelos Fasoulis, M. Virvou, G. Tsihrintzis, C. Patsakis, Efthymios Alepis
Sentiment analysis is a rather intriguing subject that modern ICT tools enable us to explore and analyze. In this work we perform, to the best of our knowledge, the most wide analysis of sentiment mapping to geographic locations and time through smartphones, in an attempt to both visualize them and also reveal possible correlations and patterns. Our vast dataset consisting of more than 56.000 samples, from 100 individuals, for a time period of nine months, revealed patterns, both in space and time, that are directly linked to geographic locations of users and provide an aggregated real-time insight on how people feel, allowing for a wide range of applications.
{"title":"Sensus Vox: Sentiment Mapping Through Smartphone Multi-Sensory Crowdsourcing","authors":"Angelos Fasoulis, M. Virvou, G. Tsihrintzis, C. Patsakis, Efthymios Alepis","doi":"10.1109/ICTAI.2018.00074","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00074","url":null,"abstract":"Sentiment analysis is a rather intriguing subject that modern ICT tools enable us to explore and analyze. In this work we perform, to the best of our knowledge, the most wide analysis of sentiment mapping to geographic locations and time through smartphones, in an attempt to both visualize them and also reveal possible correlations and patterns. Our vast dataset consisting of more than 56.000 samples, from 100 individuals, for a time period of nine months, revealed patterns, both in space and time, that are directly linked to geographic locations of users and provide an aggregated real-time insight on how people feel, allowing for a wide range of applications.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133472724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00159
Davide Saggese, G. Cordasco, M. Maldonato, N. Bourbakis, A. Vinciarelli, A. Esposito
Humans are used to express their feelings of selfconfidence/ powerfulness or their distress/sadness through either expansive postures that occupy as much space as possible or closing postures occupying as less space as possible to avoid contact. This conduct suggests that feelings of selfconfidence/ powerfulness or distress/sadness change our body expressions/postures. It can be interesting to assess whether the reverse is also true, i.e. the way we arrange our body at a given moment would affect our feelings. The present research reports an investigation on such argument. To this aim, 50 subjects (25 females) aged between 23 and 31 years were requested to adopt either an expansive (high-powered) or contracted (low-powered) posture for as long as 3 minutes and then asked to bet money in a dice game. The results show that assuming high-power poses favors risk tolerant behaviors and rises feelings of powerfulness. This is not true in the case of low-power postures, which engender a sense of stress, sustained by a significant increase of skin conductance levels. Considerations are made on how to exploit these results for psychotherapy and rehabilitation purposes, as well as, for the implementation of artificial intelligent systems operating as tools for well-being and coaching.
{"title":"Power Poses Affect Risk Tolerance and Skin Conductance Levels","authors":"Davide Saggese, G. Cordasco, M. Maldonato, N. Bourbakis, A. Vinciarelli, A. Esposito","doi":"10.1109/ICTAI.2018.00159","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00159","url":null,"abstract":"Humans are used to express their feelings of selfconfidence/ powerfulness or their distress/sadness through either expansive postures that occupy as much space as possible or closing postures occupying as less space as possible to avoid contact. This conduct suggests that feelings of selfconfidence/ powerfulness or distress/sadness change our body expressions/postures. It can be interesting to assess whether the reverse is also true, i.e. the way we arrange our body at a given moment would affect our feelings. The present research reports an investigation on such argument. To this aim, 50 subjects (25 females) aged between 23 and 31 years were requested to adopt either an expansive (high-powered) or contracted (low-powered) posture for as long as 3 minutes and then asked to bet money in a dice game. The results show that assuming high-power poses favors risk tolerant behaviors and rises feelings of powerfulness. This is not true in the case of low-power postures, which engender a sense of stress, sustained by a significant increase of skin conductance levels. Considerations are made on how to exploit these results for psychotherapy and rehabilitation purposes, as well as, for the implementation of artificial intelligent systems operating as tools for well-being and coaching.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129094367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00086
Madalina Raschip, Cornelius Croitoru, Cristian Frasinaru
This paper proposes new randomized fitness functions for a genetic algorithm used to solve the satisfiability problem. The fitness functions follow the general idea of probability amplification. The first function is inspired by the Lovász Local Lemma, while the second one is based on a randomized 2-SAT approximation. The genetic algorithm uses some specific components derived from unit propagation. The crossover operator and the restart strategy are designed to benefit from the application of unit propagation. A local search algorithm is applied on the best solution at each step of the algorithm in order to improve it. Competitive results were obtained for different benchmarks when compared with state-of-the-art algorithms.
{"title":"New Evolutionary Approaches for SAT Solving","authors":"Madalina Raschip, Cornelius Croitoru, Cristian Frasinaru","doi":"10.1109/ICTAI.2018.00086","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00086","url":null,"abstract":"This paper proposes new randomized fitness functions for a genetic algorithm used to solve the satisfiability problem. The fitness functions follow the general idea of probability amplification. The first function is inspired by the Lovász Local Lemma, while the second one is based on a randomized 2-SAT approximation. The genetic algorithm uses some specific components derived from unit propagation. The crossover operator and the restart strategy are designed to benefit from the application of unit propagation. A local search algorithm is applied on the best solution at each step of the algorithm in order to improve it. Competitive results were obtained for different benchmarks when compared with state-of-the-art algorithms.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"462 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116411381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00072
Franck Gechter, Lauri Fabrice, Gussy Anthony, Staine Florian
Managing electrical energy is nowadays a challenge of paramount importance in many countries. One of the numerous problems of this challenge is the one that consists in determining (and managing) the power flows between consumers and producers in a micro-grid (i.e. a local electrical connected network nearly isolated from the main, national level, electricity network), so as to take advantage of the renewable sources, typically solar panel and wind generator, and solicit the main grid (i.e. the global network) the least possible in order to fulfill the demand, for instance. To manage the power flows, we propose in this paper an approach based on agents that represent consumers and producers. They are moved by attractive and repulsive forces, inspired by Newtonian Physics, whose intensities depend on the amount of electrical power available by the ones and required by the others. Experimental results obtained from simulations show that this approach can manage power flows in an open system by avoiding black-out. Moreover, the results obtained show adaptability skills (i.e. producers can be added and removed in runtime).
{"title":"Managing Power Flows in SmartGrids with Physically-Inspired Reactive Agents","authors":"Franck Gechter, Lauri Fabrice, Gussy Anthony, Staine Florian","doi":"10.1109/ICTAI.2018.00072","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00072","url":null,"abstract":"Managing electrical energy is nowadays a challenge of paramount importance in many countries. One of the numerous problems of this challenge is the one that consists in determining (and managing) the power flows between consumers and producers in a micro-grid (i.e. a local electrical connected network nearly isolated from the main, national level, electricity network), so as to take advantage of the renewable sources, typically solar panel and wind generator, and solicit the main grid (i.e. the global network) the least possible in order to fulfill the demand, for instance. To manage the power flows, we propose in this paper an approach based on agents that represent consumers and producers. They are moved by attractive and repulsive forces, inspired by Newtonian Physics, whose intensities depend on the amount of electrical power available by the ones and required by the others. Experimental results obtained from simulations show that this approach can manage power flows in an open system by avoiding black-out. Moreover, the results obtained show adaptability skills (i.e. producers can be added and removed in runtime).","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115399573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The demand for mining massive short-text data from the Internet has promoted researches on topic models. There exist many schemes trying to solve the sparsity problems brought by short texts, mainly based on data aggregation or model improvement. Among them, Biterm Topic Model changes the way of modeling topics, which is on document-level biterms and has shown creativity and effectiveness. However, this may ignore those semantically similar and rarely co-occurrent word pairs, which are denoted as global biterms in this paper. Inspired by the successful application of word embeddings in GPU-DMM, we exploit word embeddings to extract semantically similar word pairs from the whole corpus to help discover better topics. We call this model as GloSS, which takes advantages of both the approach to model topics and word embeddings. Experimental results on two open-source and real datasets are superior to state-of-the-art topic models for short texts.
{"title":"Exploiting Global Semantic Similarity Biterms for Short-Text Topic Discovery","authors":"Heng-yang Lu, Gao-Jian Ge, Yun Li, Chong-Jun Wang, Junyuan Xie","doi":"10.1109/ICTAI.2018.00151","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00151","url":null,"abstract":"The demand for mining massive short-text data from the Internet has promoted researches on topic models. There exist many schemes trying to solve the sparsity problems brought by short texts, mainly based on data aggregation or model improvement. Among them, Biterm Topic Model changes the way of modeling topics, which is on document-level biterms and has shown creativity and effectiveness. However, this may ignore those semantically similar and rarely co-occurrent word pairs, which are denoted as global biterms in this paper. Inspired by the successful application of word embeddings in GPU-DMM, we exploit word embeddings to extract semantically similar word pairs from the whole corpus to help discover better topics. We call this model as GloSS, which takes advantages of both the approach to model topics and word embeddings. Experimental results on two open-source and real datasets are superior to state-of-the-art topic models for short texts.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114398721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00052
Quratul-ain Mahesar
We present a novel automated approach for the computation and verification of preferences in an abstract argumentation system. Various argumentation semantics have been developed for identifying acceptable sets of arguments, however, there is a lack of explanatory justification for their acceptability based on preferences. We present an algorithm which takes an abstract argumentation framework and a single extension (conflict-free set of arguments) as input, and outputs preference relations that explain why a set of arguments are acceptable as opposed to their attackers. We also present an algorithm to verify that the output preferences when used with the given argumentation framework induce the input extension.
{"title":"Computing Argument Preferences and Explanations in Abstract Argumentation","authors":"Quratul-ain Mahesar","doi":"10.1109/ICTAI.2018.00052","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00052","url":null,"abstract":"We present a novel automated approach for the computation and verification of preferences in an abstract argumentation system. Various argumentation semantics have been developed for identifying acceptable sets of arguments, however, there is a lack of explanatory justification for their acceptability based on preferences. We present an algorithm which takes an abstract argumentation framework and a single extension (conflict-free set of arguments) as input, and outputs preference relations that explain why a set of arguments are acceptable as opposed to their attackers. We also present an algorithm to verify that the output preferences when used with the given argumentation framework induce the input extension.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115623600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-11-01DOI: 10.1109/ICTAI.2018.00146
Z. Peng, Z. Al-Chami, H. Manier, M. Manier
This paper studies a variant of vehicle routing problem called Selective Pickup and Delivery Problem with Time Windows and Paired Demands (SPDPTWPD). A visiting sequence of each assigned vehicle needs to be determined by respecting the imposed constraints. Like for other combinatorial problems, the optimal solution cannot be obtained in a reasonable time when the size increases. An approached method is thus chosen as an alternative to tackle this issue. The proposed method integrates particle swarm optimization (PSO) with local searches by considering the diversification of PSO and intensification of local search. To validate the method, experiments are made on the benchmarks from the literature. The experiments are divided into two parts. In the first part, a self-comparison is made to demonstrate the evolutionary capacity of PSO and the efficiency of proposed local searches. In the second part, the proposed method is compared with a genetic algorithm from the literature. The results show that the method is competitive and efficient.
{"title":"A Particle Swarm Optimization for Selective Pickup and Delivery Problem","authors":"Z. Peng, Z. Al-Chami, H. Manier, M. Manier","doi":"10.1109/ICTAI.2018.00146","DOIUrl":"https://doi.org/10.1109/ICTAI.2018.00146","url":null,"abstract":"This paper studies a variant of vehicle routing problem called Selective Pickup and Delivery Problem with Time Windows and Paired Demands (SPDPTWPD). A visiting sequence of each assigned vehicle needs to be determined by respecting the imposed constraints. Like for other combinatorial problems, the optimal solution cannot be obtained in a reasonable time when the size increases. An approached method is thus chosen as an alternative to tackle this issue. The proposed method integrates particle swarm optimization (PSO) with local searches by considering the diversification of PSO and intensification of local search. To validate the method, experiments are made on the benchmarks from the literature. The experiments are divided into two parts. In the first part, a self-comparison is made to demonstrate the evolutionary capacity of PSO and the efficiency of proposed local searches. In the second part, the proposed method is compared with a genetic algorithm from the literature. The results show that the method is competitive and efficient.","PeriodicalId":254686,"journal":{"name":"2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126081658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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