A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA
Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal so...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/1792420 |
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| author | Alberto Pajares Xavier Blasco Juan M. Herrero Gilberto Reynoso-Meza |
| author_facet | Alberto Pajares Xavier Blasco Juan M. Herrero Gilberto Reynoso-Meza |
| author_sort | Alberto Pajares |
| collection | DOAJ |
| description | Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant. |
| format | Article |
| id | doaj-art-ca114892b3a141638c01686a2dca9e9b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ca114892b3a141638c01686a2dca9e9b2025-02-03T05:50:06ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/17924201792420A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGAAlberto Pajares0Xavier Blasco1Juan M. Herrero2Gilberto Reynoso-Meza3Instituto Universitario de Automática e Informática Industrial, Universitat Politecnica de Valencia, Valencia, SpainInstituto Universitario de Automática e Informática Industrial, Universitat Politecnica de Valencia, Valencia, SpainInstituto Universitario de Automática e Informática Industrial, Universitat Politecnica de Valencia, Valencia, SpainIndustrial and Systems Engineering Graduate Program-PPGEPS, Polytechnic School, Pontifical Catholic University of Paraná (PUCPR), Curitiba, PR, BrazilTraditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.http://dx.doi.org/10.1155/2018/1792420 |
| spellingShingle | Alberto Pajares Xavier Blasco Juan M. Herrero Gilberto Reynoso-Meza A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA Complexity |
| title | A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA |
| title_full | A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA |
| title_fullStr | A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA |
| title_full_unstemmed | A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA |
| title_short | A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA |
| title_sort | multiobjective genetic algorithm for the localization of optimal and nearly optimal solutions which are potentially useful nevmoga |
| url | http://dx.doi.org/10.1155/2018/1792420 |
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