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A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Random Cooperative Learning Strategy

Published: 13 July 2022 Publication History

Abstract

Multi-swarm particle swarm optimization algorithms have received much attention in recent years due to their effectiveness in maintaining the diversity of population. However, during the evolution process, the algorithm often suffers from the aggregation of particles within the sub-swarm which could make the particles fall into the local optimum. In order to solve this problem, we propose a new multi-swarm particle swarm optimization algorithm: A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Cooperative Learning Strategy Algorithm (SMS-DCLS-PSO). In the SMS-DCLS-PSO algorithm, we use the number of stagnant local optimums in the sub-swarms as the aggregation flag. When the sub-swarms reach the aggregation condition, the dynamic random cooperative learning strategy is adopted to realize the adaptive periodic information exchange between sub-swarms. To further enhance the diversity of the population, the best particle of the main population and the worst particle of the sub-swarms are respectively mutated. When the sub-swarms don't meet the aggregation conditions, the sub-swarms evolve independently which means the particles only exchange the information within their own sub-swarm. The main-swarm evaluates the local optimal position of each sub-swarm and its own local optimal position in each iteration to update the velocity. The results of the comparison with SMS-DCLS-PSO and other PSO algorithms on the latest public test suite demonstrate that SMS-DCLS-PSO has a better performance on optimization.

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  1. A Subordinate Multi-Swarm Particle Swarm Optimization Algorithm based on the Dynamic Random Cooperative Learning Strategy

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    ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial Intelligence
    March 2022
    809 pages
    ISBN:9781450396110
    DOI:10.1145/3532213
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 13 July 2022

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    Author Tags

    1. Dynamic cooperative learning strategy
    2. Multi-swarm
    3. Mutation
    4. Particle swarm optimization

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    • National Natural Science Foundation of China
    • Beijing Outstanding Talent Training Foundation
    • 'Rixin Scientist' Foundation of Beijing University of Technology
    • Beijing Natural Science Foundation
    • National Basic Research Programme of China
    • International Science & Technology Cooperation Program of China

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