Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization

Martín Montes Rivera (), Carlos Guerrero-Mendez (), Daniela Lopez-Betancur and Tonatiuh Saucedo-Anaya
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Martín Montes Rivera: Research and Postgraduate Studies Department, Universidad Politécnica de Aguascalientes, Aguascalientes 20342, Mexico
Carlos Guerrero-Mendez: Unidad Académica de Ciencia y Tecnologia de la Luz y la Materia, Universidad Autónoma de Zacatecas, Campus es Parque de Ciencia y Tecnología QUANTUM, Cto., Marie Curie S/N, Zacatecas 98160, Mexico
Daniela Lopez-Betancur: Unidad Académica de Ciencia y Tecnologia de la Luz y la Materia, Universidad Autónoma de Zacatecas, Campus es Parque de Ciencia y Tecnología QUANTUM, Cto., Marie Curie S/N, Zacatecas 98160, Mexico
Tonatiuh Saucedo-Anaya: Unidad Académica de Ciencia y Tecnologia de la Luz y la Materia, Universidad Autónoma de Zacatecas, Campus es Parque de Ciencia y Tecnología QUANTUM, Cto., Marie Curie S/N, Zacatecas 98160, Mexico

Mathematics, 2023, vol. 11, issue 20, 1-40

Abstract: 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.

Keywords: PSO; regrouping PSO; large-scale optimization; swarm optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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