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A New Multiobjective Particle Swarm Optimization Using Local Displacement and Local Guides

A New Multiobjective Particle Swarm Optimization Using Local Displacement and Local Guides
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摘要 This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors. This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.
作者 Saïd Charriffaini Rawhoudine Abdoulhafar Halassi Bacar Saïd Charriffaini Rawhoudine;Abdoulhafar Halassi Bacar(Laboratoire des Mathmatiques, Statistiques, Informatique et Applications (LMSIA), Dpartement des Mathmatiques, Physique-Chimie et Informatique, Facult des Sciences et Techniques, Universit des Comores, Moroni, Comoros)
出处 《Open Journal of Optimization》 2024年第2期31-49,共19页 最优化(英文)
关键词 Particle Swarm Optimization Multiobjective Optimization Attractor-Based Displacement Square Root Distance Crowding Distance Particle Swarm Optimization Multiobjective Optimization Attractor-Based Displacement Square Root Distance Crowding Distance
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