A Consensus-Based Particle Swarm Optimization Algorithm for Distributed Multi-Objective Optimization

Distributed optimization has been extensively studied in the past decades due to its wide applications. Most of them aim to optimizing single-objective function. However, in real-world applications and industrial production, it is often necessary to trade off between two or more objective functions,...

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Bibliographic Details
Published in:International Symposium on Autonomous Systems (Online) pp. 1 - 8
Main Authors: Li, Kaixuan, Zhao, Sen, Fan, Cheng, Hu, Qilong, Wang, Yi, Wang, Hui
Format: Conference Proceeding
Language:English
Published: IEEE 23.05.2025
Subjects:
Autonomous systems
Consensus algorithm
Consensus protocol
Distributed algorithms
Distributed Optimization
Evolutionary Multi-Objective Optimization
Linear programming
Optimization
Pareto optimization
Particle swarm optimization
Production
ISSN:2996-3850
Online Access:Get full text
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Summary:Distributed optimization has been extensively studied in the past decades due to its wide applications. Most of them aim to optimizing single-objective function. However, in real-world applications and industrial production, it is often necessary to trade off between two or more objective functions, requiring algorithms capable of optimizing multiple objectives simultaneously. In view of this, a consensus theory and particle swarm-based distributed multi-objective optimization algorithm (CTPS-DMOA) is proposed, which can achieve a consensus Pareto optimal solution in a distributed manner. In CTPS-DMOA, a population is assigned to each local multi-objective function. Then, an average consensus-based particle communicating operator is introduced, allowing neighboring particles at the same layer to exchange useful information over a connected communication graph, ensuring that particles at the same layer can reach consensus on their positions. To address the evaluation issue of particles, a average consensus-based global evaluation operator is proposed to ensure a global evaluation of particles only with local information. Additionally, to enhance the diversity of solutions and output a more complete Pareto optimal solution set, an extreme solutions guided Pareto front stretching operator is introduced. Finally, the overall framework of CTPS-DMOA is presented, and a series of experiments validate the superiority of CTPS-DMOA in solving distributed multi-objective optimization problems.
ISSN:2996-3850
DOI:10.1109/ICAISISAS64483.2025.11052165