An Adaptive Balance Search Based Complementary Heterogeneous Particle Swarm Optimization Architecture

2024-12-17

arXiv.org

AI Summary

Existing cognitive particle swarm optimization (CPSO) algorithms mitigate premature convergence by constructing diverse vectors, yet suffer from low vector utilization, limiting convergence accuracy. Method: This paper proposes the Complementary Heterogeneous PSO (CHxPSO) framework coupled with an Adaptive Balance Search (ABS) mechanism. CHxPSO introduces a novel dual-channel shared vector construction architecture to enable information complementarity across heterogeneous subswarms; ABS dynamically adapts to problem landscapes to precisely regulate the exploration–exploitation trade-off over time. Integrated with coevolutionary dual-subswarm dynamics and adaptive particle allocation, the approach enhances both convergence accuracy and robustness. Contribution/Results: Extensive evaluation on standard benchmark suites demonstrates that CHxPSO significantly improves the performance of multiple CPSO variants, achieving superior generalization capability and stability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• Mitigate premature convergence in PSO algorithms
• Improve utilization of constructed vectors for accuracy
• Balance exploration and exploitation in evolutionary process

Innovation

• Complementary heterogeneous PSO framework with two update channels
• Adaptive balance search strategy controls particle proportion
• Shared constructed vector utilized across heterogeneous mechanisms