An Improved Bare Bones Particle Swarm Optimization Algorithm Based on Sequential Update Mechanism and a Modified Structure

Abstract

The past three decades have witnessed the rapid development of nature-inspired algorithms. Among these, population-based optimization algorithms have gained significant popularity due to their effectiveness in solving a wide range of problems. Particle Swarm Optimization (PSO) stands out as a pioneering algorithm in this domain. Bare-Bones Particle Swarm Optimization (BBPSO) is a simplified variant of PSO that eliminates the velocity term and additional parameters. This study introduces a novel sequential update rule for BBPSO, along with a modification to the standard algorithm. The proposed methods were evaluated on a comprehensive benchmark suite, including 36 benchmark problems from the literature, 30 benchmark problems from CEC2021, consisting of 10 basic and 20 transformed variants and 5 engineering optimization problems. Comparative analysis with standard BBPSO and other simplified PSO variants demonstrated the effectiveness of our proposed approach.

Keywords

• Bare bones particle swarm optimization
• engineering optimization problems
• modified bare bones particle swarm optimization
• particle swarm optimization