Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms
Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and...
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| Format: | Article |
| Language: | English |
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Springer US
2016
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| Online Access: | http://hdl.handle.net/1721.1/103318 https://orcid.org/0000-0001-5833-5178 |
| _version_ | 1826206336065470464 |
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| author | Deb, Kalyanmoy Padhye, Nikhil |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Deb, Kalyanmoy Padhye, Nikhil |
| author_sort | Deb, Kalyanmoy |
| collection | MIT |
| description | Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and dissimilarities. This paper establishes a clear and fundamental algorithmic linking between particle swarm optimization (PSO) algorithm and genetic algorithms (GAs). Specifically, we select the task of solving unimodal optimization problems, and demonstrate that key algorithmic features of an effective Generalized Generation Gap based Genetic Algorithm can be introduced into the PSO by leveraging this algorithmic linking while significantly enhance the PSO’s performance. However, the goal of this paper is not to solve unimodal problems, neither is to demonstrate that the modified PSO algorithm resembles a GA, but to highlight the concept of algorithmic linking in an attempt towards designing efficient optimization algorithms. We intend to emphasize that the evolutionary and other optimization researchers should direct more efforts in establishing equivalence between different genetic, evolutionary and other nature-inspired or non-traditional algorithms. In addition to achieving performance gains, such an exercise shall deepen the understanding and scope of various operators from different paradigms in Evolutionary Computation (EC) and other optimization methods. |
| first_indexed | 2024-09-23T13:27:34Z |
| format | Article |
| id | mit-1721.1/103318 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:27:34Z |
| publishDate | 2016 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1033182022-09-28T14:26:45Z Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms Deb, Kalyanmoy Padhye, Nikhil Massachusetts Institute of Technology. Department of Mechanical Engineering Padhye, Nikhil Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and dissimilarities. This paper establishes a clear and fundamental algorithmic linking between particle swarm optimization (PSO) algorithm and genetic algorithms (GAs). Specifically, we select the task of solving unimodal optimization problems, and demonstrate that key algorithmic features of an effective Generalized Generation Gap based Genetic Algorithm can be introduced into the PSO by leveraging this algorithmic linking while significantly enhance the PSO’s performance. However, the goal of this paper is not to solve unimodal problems, neither is to demonstrate that the modified PSO algorithm resembles a GA, but to highlight the concept of algorithmic linking in an attempt towards designing efficient optimization algorithms. We intend to emphasize that the evolutionary and other optimization researchers should direct more efforts in establishing equivalence between different genetic, evolutionary and other nature-inspired or non-traditional algorithms. In addition to achieving performance gains, such an exercise shall deepen the understanding and scope of various operators from different paradigms in Evolutionary Computation (EC) and other optimization methods. 2016-06-23T22:44:42Z 2016-06-23T22:44:42Z 2013-10 2012-03 2016-05-23T12:15:39Z Article http://purl.org/eprint/type/JournalArticle 0926-6003 1573-2894 http://hdl.handle.net/1721.1/103318 Deb, Kalyanmoy, and Nikhil Padhye. “Enhancing Performance of Particle Swarm Optimization through an Algorithmic Link with Genetic Algorithms.” Computational Optimization and Applications 57.3 (2014): 761–794. https://orcid.org/0000-0001-5833-5178 en http://dx.doi.org/10.1007/s10589-013-9605-0 Computational Optimization and Applications Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer Science+Business Media New York application/pdf Springer US Springer US |
| spellingShingle | Deb, Kalyanmoy Padhye, Nikhil Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms |
| title | Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms |
| title_full | Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms |
| title_fullStr | Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms |
| title_full_unstemmed | Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms |
| title_short | Enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms |
| title_sort | enhancing performance of particle swarm optimization through an algorithmic link with genetic algorithms |
| url | http://hdl.handle.net/1721.1/103318 https://orcid.org/0000-0001-5833-5178 |
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