Multi-Strategy Improved Particle Swarm Optimization Algorithm and Gazelle Optimization Algorithm and Application

Abstract

In addressing the challenges associated with low convergence accuracy and unstable optimization results in the original gazelle optimization algorithm (GOA), this paper proposes a novel approach incorporating chaos mapping termed multi-strategy particle swarm optimization with gazelle optimization algorithm (MPSOGOA). In the population initialization stage, segmented mapping is integrated to generate a uniformly distributed high-quality population which enhances diversity, and global perturbation of the population is added to improve the convergence speed in the early iteration and the convergence accuracy in the late iteration. By combining particle swarm optimization (PSO) and GOA, the algorithm leverages individual experiences of gazelles, which improves convergence accuracy and stability. Tested on 35 benchmark functions, MPSOGOA demonstrates superior performance in convergence accuracy and stability through Friedman tests and Wilcoxon signed-rank tests, surpassing other metaheuristic algorithms. Applied to engineering optimization problems, including constrained implementations, MPSOGOA exhibits excellent optimization performance.

A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

“…Yet, based on the 'no free lunch' concept, it is impossible for a singular population intelligence optimization algorithm to address every optimization issue [23]. Every algorithm for optimizing population intelligence comes with its own set of limitations and constraints, leading numerous academics to suggest enhancements to these foundational algorithms [24][25][26]. These algorithms can compensate for the shortcomings of the original algorithms by integrating new strategies or the advantages of other algorithms to achieve better optimization results.…”