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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| Vydané v: | International Symposium on Autonomous Systems (Online) s. 1 - 8 |
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| Hlavní autori: | , , , , , |
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| Jazyk: | English |
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IEEE
23.05.2025
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| ISSN: | 2996-3850 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Fan, Cheng Wang, Yi Hu, Qilong Li, Kaixuan Wang, Hui Zhao, Sen |
| Author_xml | – sequence: 1 givenname: Kaixuan surname: Li fullname: Li, Kaixuan email: kxli@ahu.edu.cn organization: Anhui University,Information Materials and Intelligent Sensing Laboratory of Anhui Province,Hefei,China – sequence: 2 givenname: Sen surname: Zhao fullname: Zhao, Sen email: 19917620916@163.com organization: Anhui University,Information Materials and Intelligent Sensing Laboratory of Anhui Province,Hefei,China – sequence: 3 givenname: Cheng surname: Fan fullname: Fan, Cheng email: chengfan@mail.ustc.edu.cn organization: Anhui University,Information Materials and Intelligent Sensing Laboratory of Anhui Province,Hefei,China – sequence: 4 givenname: Qilong surname: Hu fullname: Hu, Qilong email: 1978864910@qq.com organization: Anhui University,Information Materials and Intelligent Sensing Laboratory of Anhui Province,Hefei,China – sequence: 5 givenname: Yi surname: Wang fullname: Wang, Yi email: 1185093533@qq.com organization: Anhui University,Information Materials and Intelligent Sensing Laboratory of Anhui Province,Hefei,China – sequence: 6 givenname: Hui surname: Wang fullname: Wang, Hui email: m201570055@hust.edu.cn organization: Anhui Sanlian University,Faculty of Engineering,Hefei,China |
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| Snippet | Distributed optimization has been extensively studied in the past decades due to its wide applications. Most of them aim to optimizing single-objective... |
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| SubjectTerms | Autonomous systems Consensus algorithm Consensus protocol Distributed algorithms Distributed Optimization Evolutionary Multi-Objective Optimization Linear programming Optimization Pareto optimization Particle swarm optimization Production |
| Title | A Consensus-Based Particle Swarm Optimization Algorithm for Distributed Multi-Objective Optimization |
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