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A Novel Diversity-A...
A Novel Diversity-Aware Inertia Weight and Velocity Control for Particle Swarm Optimization
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Morales-Castañeda, Bernardo (author)
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Oliva, Diego (author)
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Casas-Ordaz, Angel (author)
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Arturo, Valdivia (author)
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Mario, A Navarro (author)
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Alfonso, Ramos-Michel (author)
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Erick, Rodríguez-Esparza (author)
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- Seyed Jalaleddin, Mousavirad (author)
- Universidade da Beira Interior, Covilhã, Portugal
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(creator_code:org_t)
- IEEE Press, 2023
- 2023
- English.
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In: 2023 IEEE Congress on Evolutionary Computation (CEC). - : IEEE Press. - 9798350314588 - 9798350314588
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
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- Particle Swarm Optimization (PSO) has efficiently solved several real-world applications and optimization problems. However, it has shortcomings, such as premature convergence and stagnation at local minima. Inertia weight is a parameter of this algorithm that controls the global and local exploration and exploitation capability by determining the influence of the previous velocity on its current motion. Therefore, this article proposes a PSO with a Diversity-aware Inertia and Velocity Control (PSOIVC) algorithm to improve the PSO performance. The PSOIVC employs a novel diversity-aware inertia weight and velocity control approach to tune the parameters to produce a trade-off between exploration and exploitation of the algorithm using the dimension-wise diversity. The PSOIVC algorithm is compared with eight algorithms, including variants of the PSO, on a set of 30 benchmark functions for a single objective real parameter in 30 and 50 dimensions. Based on the results, the proposal presents significant outcomes according to the average values obtained for both comparisons; because it performed similarly or better than the other algorithms in 23/30 and 16/30 for 30 and 50 dimensions, respectively.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
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- ref (subject category)
- kon (subject category)
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