Pub Date : 2016-07-01DOI: 10.1109/CEC.2016.7744288
H. N. Alves
This paper presents a multi-objective algorithm for optimal placement of capacitor banks in distorted electrical distribution networks. A constrained multi-objective immune algorithm is proposed. The voltage profile constraints are used to define feasible solutions in the optimization process. Investments costs, power and energy losses costs and harmonic mitigation are considered in the solution. A 104-bus test system is presented and the results are compared to the solution given by a SPEA-II and a NSGA-II approach. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of planning in distribution feeders.
{"title":"Optimal placement of capacitor banks in distorted electrical distribution network based on a constrained multi-objective immune algorithm","authors":"H. N. Alves","doi":"10.1109/CEC.2016.7744288","DOIUrl":"https://doi.org/10.1109/CEC.2016.7744288","url":null,"abstract":"This paper presents a multi-objective algorithm for optimal placement of capacitor banks in distorted electrical distribution networks. A constrained multi-objective immune algorithm is proposed. The voltage profile constraints are used to define feasible solutions in the optimization process. Investments costs, power and energy losses costs and harmonic mitigation are considered in the solution. A 104-bus test system is presented and the results are compared to the solution given by a SPEA-II and a NSGA-II approach. The results confirm the efficiency of the proposed method which makes it promising to solve complex problems of planning in distribution feeders.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83766856","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 : 2016-01-01DOI: 10.1109/CEC.2016.7744421
Yan Yu, Guangming Dai, Liang Chen, Chong Zhou, L. Peng
{"title":"Robust design optimization based on multi-objective particle swarm optimization","authors":"Yan Yu, Guangming Dai, Liang Chen, Chong Zhou, L. Peng","doi":"10.1109/CEC.2016.7744421","DOIUrl":"https://doi.org/10.1109/CEC.2016.7744421","url":null,"abstract":"","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91195314","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 : 2016-01-01DOI: 10.1109/CEC.2016.7744198
H. Ge, Liang Sun
{"title":"Intelligent scheduling in flexible job shop environments based on artificial fish swarm algorithm with estimation of distribution","authors":"H. Ge, Liang Sun","doi":"10.1109/CEC.2016.7744198","DOIUrl":"https://doi.org/10.1109/CEC.2016.7744198","url":null,"abstract":"","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81687500","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 : 2015-05-25DOI: 10.1109/CEC.2015.7257131
F. O. França
Many practical problems are described by an objective-function with the intent to optimize a single goal. This leads to the important research topic of nonlinear optimization, that seeks to create algorithms and computational methods that are capable of finding a global optimum of such functions. But, many functions are multimodal, having many different global optima. Also, given the impossibility to create an exact model of a real-world problem, not every global (or local) optima is feaseable to be conceived. As such, it is interesting to find as many alternative optima in order to find one that is feaseable given unmodelled constraints. This paper proposes a methodology that, given a local optimum, it finds nearby local optima with similar objective-function values. This is performed by maximizing the approximation error of a Linear Interpolation of the function. The experiments show promising results regarding the number of detected peaks when compared to the state-of-the-art, though requiring a higher number of function evaluations on average.
{"title":"Maximization of a dissimilarity measure for multimodal optimization","authors":"F. O. França","doi":"10.1109/CEC.2015.7257131","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257131","url":null,"abstract":"Many practical problems are described by an objective-function with the intent to optimize a single goal. This leads to the important research topic of nonlinear optimization, that seeks to create algorithms and computational methods that are capable of finding a global optimum of such functions. But, many functions are multimodal, having many different global optima. Also, given the impossibility to create an exact model of a real-world problem, not every global (or local) optima is feaseable to be conceived. As such, it is interesting to find as many alternative optima in order to find one that is feaseable given unmodelled constraints. This paper proposes a methodology that, given a local optimum, it finds nearby local optima with similar objective-function values. This is performed by maximizing the approximation error of a Linear Interpolation of the function. The experiments show promising results regarding the number of detected peaks when compared to the state-of-the-art, though requiring a higher number of function evaluations on average.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74078088","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 : 2015-01-01DOI: 10.1109/CEC.2015.7257310
Xingtian Xu, X. Chen
{"title":"Elite Bias Genetic Algorithm for Optimal Control of Double-skin Facade","authors":"Xingtian Xu, X. Chen","doi":"10.1109/CEC.2015.7257310","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257310","url":null,"abstract":"","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84683580","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 : 2014-07-06DOI: 10.1109/CEC.2014.6900645
Saúl Zapotecas Martínez, C. Coello
In spite of the popularity of the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), its use in Constrained Multi-objective Optimization Problems (CMOPs) has not been fully explored. In the last few years, there have been a few proposals to extend MOEA/D to the solution of CMOPs. However, most of these proposals have adopted selection mechanisms based on penalty functions. In this paper, we present a novel selection mechanism based on the well-known e-constraint method. The proposed approach uses information related to the neighborhood adopted in MOEA/D in order to obtain solutions which minimize the objective functions within the allowed feasible region. Our preliminary results indicate that our approach is highly competitive with respect to a state-of-the-art MOEA which solves in an efficient way the constrained test problems adopted in our comparative study.
{"title":"A multi-objective evolutionary algorithm based on decomposition for constrained multi-objective optimization","authors":"Saúl Zapotecas Martínez, C. Coello","doi":"10.1109/CEC.2014.6900645","DOIUrl":"https://doi.org/10.1109/CEC.2014.6900645","url":null,"abstract":"In spite of the popularity of the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D), its use in Constrained Multi-objective Optimization Problems (CMOPs) has not been fully explored. In the last few years, there have been a few proposals to extend MOEA/D to the solution of CMOPs. However, most of these proposals have adopted selection mechanisms based on penalty functions. In this paper, we present a novel selection mechanism based on the well-known e-constraint method. The proposed approach uses information related to the neighborhood adopted in MOEA/D in order to obtain solutions which minimize the objective functions within the allowed feasible region. Our preliminary results indicate that our approach is highly competitive with respect to a state-of-the-art MOEA which solves in an efficient way the constrained test problems adopted in our comparative study.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85047754","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 : 2014-07-06DOI: 10.1109/CEC.2014.6900245
L. P. Cota, Matheus Nohra Haddad, M. Souza, V. N. Coelho
This paper deals with the Unrelated Parallel Machine Scheduling Problem with Setup Times (UPMSPST). The objective is to minimize the makespan. In order to solve it, we propose a heuristic algorithm, based on Iterated Local Search (ILS), Variable Neighborhood Descent (VND) and Path Relinking (PR). In this algorithm, named AIRP, an initial solution is constructed using the Adaptive Shortest Processing Time method. This solution is refined by the ILS, having an adaptation of the VND as local search method. The PR method is applied as a strategy of intensification and diversification during the search. The algorithm was tested in instances of the literature envolving up to 150 jobs and 20 machines. The computational experiments show that the proposed algorithm outperforms an algorithm from the literature, both in terms of quality and variability of the final solution. In addition, the algorithm established new best solutions for more than 80,5% of the test problems in average.
{"title":"AIRP: A heuristic algorithm for solving the unrelated parallel machine scheduling problem","authors":"L. P. Cota, Matheus Nohra Haddad, M. Souza, V. N. Coelho","doi":"10.1109/CEC.2014.6900245","DOIUrl":"https://doi.org/10.1109/CEC.2014.6900245","url":null,"abstract":"This paper deals with the Unrelated Parallel Machine Scheduling Problem with Setup Times (UPMSPST). The objective is to minimize the makespan. In order to solve it, we propose a heuristic algorithm, based on Iterated Local Search (ILS), Variable Neighborhood Descent (VND) and Path Relinking (PR). In this algorithm, named AIRP, an initial solution is constructed using the Adaptive Shortest Processing Time method. This solution is refined by the ILS, having an adaptation of the VND as local search method. The PR method is applied as a strategy of intensification and diversification during the search. The algorithm was tested in instances of the literature envolving up to 150 jobs and 20 machines. The computational experiments show that the proposed algorithm outperforms an algorithm from the literature, both in terms of quality and variability of the final solution. In addition, the algorithm established new best solutions for more than 80,5% of the test problems in average.","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74353341","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 : 2014-01-01DOI: 10.1109/CEC.2014.6900625
H. Handa
{"title":"Deep Boltzmann Machine for evolutionary agents of Mario AI","authors":"H. Handa","doi":"10.1109/CEC.2014.6900625","DOIUrl":"https://doi.org/10.1109/CEC.2014.6900625","url":null,"abstract":"","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86560896","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 : 2014-01-01DOI: 10.1109/CEC.2014.6900276
Ping Yan, Ming-hai Jiao
{"title":"A Chaotic Particle Swarm Optimization algorithm for the jobshop scheduling problem","authors":"Ping Yan, Ming-hai Jiao","doi":"10.1109/CEC.2014.6900276","DOIUrl":"https://doi.org/10.1109/CEC.2014.6900276","url":null,"abstract":"","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76344818","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 : 2013-01-01DOI: 10.1109/CEC.2013.6557549
A. Diveev, D. Khamadiyarov, E. Shmalko, E. Sofronova
{"title":"Intellectual evolution method for synthesis of mobile robot control system","authors":"A. Diveev, D. Khamadiyarov, E. Shmalko, E. Sofronova","doi":"10.1109/CEC.2013.6557549","DOIUrl":"https://doi.org/10.1109/CEC.2013.6557549","url":null,"abstract":"","PeriodicalId":6344,"journal":{"name":"2009 IEEE Congress on Evolutionary Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84801885","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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