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<p>In sciences and engineering, the particle swarm optimization (PSO) methods are fundamentally important for solving many challenging problems. Repulsive functional constraints algorithm (RFC) is a recent method for solving constrained optimization problems iteratively. The standard version namely, generalized convergence repulsive functional constraint PSO algorithm uses local search techniques with a repulsion mechanism to explore the feasible region of the problem space and thereby enhancing exploration to find optimal solutions. In this article, we present RFC-PSO algorithm and investigate its stability and convergence. We discuss steps and or flows of the algorithm, basic components, influence of RFC on convergence and stability, blending or composing techniques, fine-tuning, evaluations and or comparisons of algorithms. To confirm the applicability of the proposed scheme, both unimodal and multimodal problems are solved. Comparison and performance results with some existing methods are provided in tables and graphically.</p>

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A Convergent Particle Swarm Optimization Method with Repulsive Functional Constraints for Solving Unimodal and Multimodal Problems

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Dereje Tarekegn,
  • Surafel Luleseged,
  • Tekle Gemechu

摘要

In sciences and engineering, the particle swarm optimization (PSO) methods are fundamentally important for solving many challenging problems. Repulsive functional constraints algorithm (RFC) is a recent method for solving constrained optimization problems iteratively. The standard version namely, generalized convergence repulsive functional constraint PSO algorithm uses local search techniques with a repulsion mechanism to explore the feasible region of the problem space and thereby enhancing exploration to find optimal solutions. In this article, we present RFC-PSO algorithm and investigate its stability and convergence. We discuss steps and or flows of the algorithm, basic components, influence of RFC on convergence and stability, blending or composing techniques, fine-tuning, evaluations and or comparisons of algorithms. To confirm the applicability of the proposed scheme, both unimodal and multimodal problems are solved. Comparison and performance results with some existing methods are provided in tables and graphically.