An Adaptive Balance Search Based Complementary Heterogeneous Particle Swarm Optimization Architecture

TL;DR

This paper introduces an adaptive heterogeneous PSO architecture with a novel balance search strategy, improving convergence accuracy and avoiding premature convergence by effectively utilizing constructed vectors during optimization.

Contribution

It proposes a new complementary heterogeneous PSO framework combined with an adaptive balance search strategy to enhance particle swarm optimization performance.

Findings

Improved convergence accuracy demonstrated in experiments.

Effective balance between exploration and exploitation.

Generalization performance confirmed across multiple tests.

Abstract

A series of modified cognitive-only particle swarm optimization (PSO) algorithms effectively mitigate premature convergence by constructing distinct vectors for different particles. However, the underutilization of these constructed vectors hampers convergence accuracy. In this paper, an adaptive balance search based complementary heterogeneous PSO architecture is proposed, which consists of a complementary heterogeneous PSO (CHxPSO) framework and an adaptive balance search (ABS) strategy. The CHxPSO framework mainly includes two update channels and two subswarms. Two channels exhibit nearly heterogeneous properties while sharing a common constructed vector. This ensures that one constructed vector is utilized across both heterogeneous update mechanisms. The two subswarms work within their respective channels during the evolutionary process, preventing interference between the two channels. The ABS strategy precisely controls the proportion of particles involved in the evolution in the two channels, and thereby guarantees the flexible utilization of the constructed vectors, based on the evolutionary process and the interactions with the problem's fitness landscape. Together, our architecture ensures the effective utilization of the constructed vectors by emphasizing exploration in the early evolutionary process while exploitation in the later, enhancing the performance of a series of modified cognitive-only PSOs. Extensive experimental results demonstrate the generalization performance of our architecture.