Simple gravitational particle swarm algorithm for multimodal optimization problems.
In real world situations, decision makers prefer to have multiple optimal solutions before making a final decision. Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the grav...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248470&type=printable |
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| author | Yoshikazu Yamanaka Katsutoshi Yoshida |
| author_facet | Yoshikazu Yamanaka Katsutoshi Yoshida |
| author_sort | Yoshikazu Yamanaka |
| collection | DOAJ |
| description | In real world situations, decision makers prefer to have multiple optimal solutions before making a final decision. Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). Our GPSA is developed based on the concept of "particle clustering in the absence of clustering procedures". Specifically, it simply replaces the global feedback term in classical particle swarm optimization (PSO) with an inverse-square gravitational force term between the particles. The gravitational force mutually attracts and repels the particles, enabling them to autonomously and dynamically generate sub-swarms in the absence of algorithmic clustering procedures. Most of the sub-swarms gather at the nearby global optima, but a small number of particles reach the distant optima. The niching behavior of our GPSA was tested first on simple MMO problems, and then on twenty MMO benchmark functions. The performance indices (peak ratio and success rate) of our GPSA were compared with those of existing niching PSOs (ring-topology PSO and fitness Euclidean-distance ratio PSO). The basic performance of our GPSA was comparable to that of the existing methods. Furthermore, an improved GPSA with a dynamic parameter delivered significantly superior results to the existing methods on at least 60% of the tested benchmark functions. |
| format | Article |
| id | doaj-art-b9e7b12a6b1c43718d67c19f2838ff99 |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-b9e7b12a6b1c43718d67c19f2838ff992025-08-20T02:17:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024847010.1371/journal.pone.0248470Simple gravitational particle swarm algorithm for multimodal optimization problems.Yoshikazu YamanakaKatsutoshi YoshidaIn real world situations, decision makers prefer to have multiple optimal solutions before making a final decision. Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). Our GPSA is developed based on the concept of "particle clustering in the absence of clustering procedures". Specifically, it simply replaces the global feedback term in classical particle swarm optimization (PSO) with an inverse-square gravitational force term between the particles. The gravitational force mutually attracts and repels the particles, enabling them to autonomously and dynamically generate sub-swarms in the absence of algorithmic clustering procedures. Most of the sub-swarms gather at the nearby global optima, but a small number of particles reach the distant optima. The niching behavior of our GPSA was tested first on simple MMO problems, and then on twenty MMO benchmark functions. The performance indices (peak ratio and success rate) of our GPSA were compared with those of existing niching PSOs (ring-topology PSO and fitness Euclidean-distance ratio PSO). The basic performance of our GPSA was comparable to that of the existing methods. Furthermore, an improved GPSA with a dynamic parameter delivered significantly superior results to the existing methods on at least 60% of the tested benchmark functions.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248470&type=printable |
| spellingShingle | Yoshikazu Yamanaka Katsutoshi Yoshida Simple gravitational particle swarm algorithm for multimodal optimization problems. PLoS ONE |
| title | Simple gravitational particle swarm algorithm for multimodal optimization problems. |
| title_full | Simple gravitational particle swarm algorithm for multimodal optimization problems. |
| title_fullStr | Simple gravitational particle swarm algorithm for multimodal optimization problems. |
| title_full_unstemmed | Simple gravitational particle swarm algorithm for multimodal optimization problems. |
| title_short | Simple gravitational particle swarm algorithm for multimodal optimization problems. |
| title_sort | simple gravitational particle swarm algorithm for multimodal optimization problems |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248470&type=printable |
| work_keys_str_mv | AT yoshikazuyamanaka simplegravitationalparticleswarmalgorithmformultimodaloptimizationproblems AT katsutoshiyoshida simplegravitationalparticleswarmalgorithmformultimodaloptimizationproblems |