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

2024-06-24

arXiv.org

AI Summary

In PSO, the guidance mechanisms of personal best (P) and global best (G) exhibit detrimental coupling: P induces uncontrolled exploitation and involuntary exploration, while G causes excessive, persistent global guidance—jointly disrupting the balance between exploration (E_r) and exploitation (E_i). This work proposes a dual-channel PSO framework that decouples P–G interaction; endows P with controllable exploitation and autonomous exploration capabilities; and introduces an adaptive balancing search strategy that dynamically constrains both the intensity and temporal scope of G’s guidance. Methodologically, it is the first to systematically uncover the underlying defect mechanisms of P/G guidance, achieve explicit P–G decoupling, and establish a multi-stage parameter adaptation mechanism. Evaluated on 57 benchmark functions, the algorithm significantly outperforms multiple state-of-the-art PSO variants, concurrently improving convergence accuracy, robustness, and generalization performance.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Balancing exploration and exploitation in PSO algorithm
• Addressing uncontrollable behaviors of personal and global best positions
• Improving generalization performance via dual-channel adaptive search

Innovation

• Dual-channel framework mitigates P and G interaction
• Adaptive balance search strategy regulates behaviors
• Empowers P with controllable-exploitation and voluntary-exploration