Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization Article Swipe
Martín Montes Rivera
,
Carlos Guerrero-Méndez
,
Daniela López-Betancur
,
Tonatiuh Saucedo-Anaya
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11204339
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/math11204339
Optimizing large-scale numerical problems is a significant challenge with numerous real-world applications. The optimization process is complex due to the multi-dimensional search spaces and possesses several locally optimal regions. In response to this issue, various metaheuristic algorithms and variations have been developed, including evolutionary and swarm intelligence algorithms and hybrids of different artificial intelligence techniques. Previous studies have shown that swarm intelligence algorithms like PSO perform poorly in high-dimensional spaces, even with algorithms focused on reducing the search space. However, we propose a modified version of the PSO algorithm called Dynamical Sphere Regrouping PSO (DSRegPSO) to avoid stagnation in local optimal regions. DSRegPSO is based on the PSO algorithm and modifies inertial behavior with a regrouping dynamical sphere mechanism and a momentum conservation physics effect. These behaviors maintain the swarm’s diversity and regulate the exploration and exploitation of the search space while avoiding stagnation in optimal local regions. The DSRegPSO mechanisms mimic the behavior of birds, moving particles similar to birds when they look for a new food source. Additionally, the momentum conservation effect mimics how birds react to collisions with the boundaries in their search space or when they are looking for food. We evaluated DSRegPSO by testing 15 optimizing functions with up to 1000 dimensions of the CEC’13 benchmark, a standard for evaluating Large-Scale Global Optimization used in Congress on Evolutionary Computation, and several journals. Our proposal improves the behavior of all variants of PSO registered in the toolkit of comparison for CEC’13 and obtains the best result in the non-separable functions against all the algorithms.
Related Topics
Concepts
Particle swarm optimization
Swarm intelligence
Premature convergence
Benchmark (surveying)
Swarm behaviour
Mathematical optimization
Convergence (economics)
Computer science
Metaheuristic
Evolutionary computation
Local optimum
Local search (optimization)
Scale (ratio)
Multi-swarm optimization
Evolutionary algorithm
Computational intelligence
Algorithm
Mathematics
Artificial intelligence
Physics
Geography
Economics
Geodesy
Quantum mechanics
Economic growth
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math11204339
- OA Status
- gold
- Cited By
- 7
- References
- 57
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387779867
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387779867Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/math11204339Digital Object Identifier
- Title
-
Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global OptimizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-19Full publication date if available
- Authors
-
Martín Montes Rivera, Carlos Guerrero-Méndez, Daniela López-Betancur, Tonatiuh Saucedo-AnayaList of authors in order
- Landing page
-
https://doi.org/10.3390/math11204339Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/math11204339Direct OA link when available
- Concepts
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Particle swarm optimization, Swarm intelligence, Premature convergence, Benchmark (surveying), Swarm behaviour, Mathematical optimization, Convergence (economics), Computer science, Metaheuristic, Evolutionary computation, Local optimum, Local search (optimization), Scale (ratio), Multi-swarm optimization, Evolutionary algorithm, Computational intelligence, Algorithm, Mathematics, Artificial intelligence, Physics, Geography, Economics, Geodesy, Quantum mechanics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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