A Dual-Channel Particle Swarm Optimization Algorithm Based on Adaptive Balance Search

TL;DR

This paper introduces a dual-channel PSO algorithm with adaptive strategies to better balance exploration and exploitation, addressing issues with personal and global best behaviors to improve optimization performance.

Contribution

It systematically analyzes P and G behaviors in PSO, proposing a dual-channel framework with adaptive control to enhance balance and optimization effectiveness.

Findings

Outperforms state-of-the-art algorithms on benchmark functions.

Improves balance between exploration and exploitation.

Demonstrates stronger generalization performance.

Abstract

The balance between exploration (Er) and exploitation (Ei) determines the generalization performance of the particle swarm optimization (PSO) algorithm on different problems. Although the insufficient balance caused by global best being located near a local minimum has been widely researched, few scholars have systematically paid attention to two behaviors about personal best position (P) and global best position (G) existing in PSO. 1) P's uncontrollable-exploitation and involuntary-exploration guidance behavior. 2) G's full-time and global guidance behavior, each of which negatively affects the balance of Er and Ei. With regards to this, we firstly discuss the two behaviors, unveiling the mechanisms by which they affect the balance, and further pinpoint three key points for better balancing Er and Ei: eliminating the coupling between P and G, empowering P with controllable-exploitation and voluntary-exploration guidance behavior, controlling G's full-time and global guidance behavior. Then, we present a dual-channel PSO algorithm based on adaptive balance search (DCPSO-ABS). This algorithm entails a dual-channel framework to mitigate the interaction of P and G, aiding in regulating the behaviors of P and G, and meanwhile an adaptive balance search strategy for empowering P with voluntary-exploration and controllable-exploitation guidance behavior as well as adaptively controlling G's full-time and global guidance behavior. Finally, three kinds of experiments on 57 benchmark functions are designed to demonstrate that our proposed algorithm has stronger generalization performance than selected state-of-the-art algorithms.