Quantum particle swarm optimization algorithm based on diversity migration strategy
Particle swarm optimization algorithm has been successfully applied to solve practical optimization problems due to its simplicity and efficiency. However, the traditional particle swarm optimization algorithm has inferior search performance in complicated high-dimensional optimization issues and is...
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| Veröffentlicht in: | Future generation computer systems Jg. 157; S. 445 - 458 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.08.2024
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Particle swarm optimization algorithm has been successfully applied to solve practical optimization problems due to its simplicity and efficiency. However, the traditional particle swarm optimization algorithm has inferior search performance in complicated high-dimensional optimization issues and is prone to falling into local optima. To address these problems, a new migration mechanism is introduced and a quantum particle swarm optimization method based on diversity migration is proposed. The strategy can capture different ranges of particles in the population, and the selection of migrating individuals depends not only on their fitness values but is also influenced by the positions within the population. The individual with the minimal average Hamming distance in the population can indicate the direction of iterative population optimization. After comparing the fitness values and the average Hamming distance between particles, the particles deviating from the central range of the population are replaced. The performance of the proposed algorithm is investigated under seven different sets of benchmark function optimization problems in the CEC2020 single-objective boundary-constrained optimization competition, and is compared with those of several other representative optimization algorithms. The quantum particle swarm optimization algorithm based on diversity migration strategy outperforms other typical optimization algorithms. Moreover, the proposed algorithm is convergent and stable.
•A quantum PSO algorithm is presented by introducing diversity migration strategy.•The DM-QOSO algorithm can accomplish particle migration via diversity guidance.•The DM-QPSO algorithm can achieve higher prediction accuracy in BP neural networks. |
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| AbstractList | Particle swarm optimization algorithm has been successfully applied to solve practical optimization problems due to its simplicity and efficiency. However, the traditional particle swarm optimization algorithm has inferior search performance in complicated high-dimensional optimization issues and is prone to falling into local optima. To address these problems, a new migration mechanism is introduced and a quantum particle swarm optimization method based on diversity migration is proposed. The strategy can capture different ranges of particles in the population, and the selection of migrating individuals depends not only on their fitness values but is also influenced by the positions within the population. The individual with the minimal average Hamming distance in the population can indicate the direction of iterative population optimization. After comparing the fitness values and the average Hamming distance between particles, the particles deviating from the central range of the population are replaced. The performance of the proposed algorithm is investigated under seven different sets of benchmark function optimization problems in the CEC2020 single-objective boundary-constrained optimization competition, and is compared with those of several other representative optimization algorithms. The quantum particle swarm optimization algorithm based on diversity migration strategy outperforms other typical optimization algorithms. Moreover, the proposed algorithm is convergent and stable.
•A quantum PSO algorithm is presented by introducing diversity migration strategy.•The DM-QOSO algorithm can accomplish particle migration via diversity guidance.•The DM-QPSO algorithm can achieve higher prediction accuracy in BP neural networks. |
| Author | Xia, Shuhua Zhou, Nanrun Huang, Shuiyuan Gong, Chen |
| Author_xml | – sequence: 1 givenname: Chen surname: Gong fullname: Gong, Chen email: ncugong@163.com organization: Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China – sequence: 2 givenname: Nanrun surname: Zhou fullname: Zhou, Nanrun email: znr21@163.com organization: Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China – sequence: 3 givenname: Shuhua surname: Xia fullname: Xia, Shuhua email: 1505348682@qq.com organization: Department of Electronic Information Engineering, Nanchang University, Nanchang, 330031, China – sequence: 4 givenname: Shuiyuan surname: Huang fullname: Huang, Shuiyuan email: huangshuiyuan@ncu.edu.cn organization: Department of Computer Science and Technology, Nanchang University, Nanchang, 330031, China |
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| Keywords | Diversity migration strategy Average Hamming distance Particle swarm optimization Quantum-behaved Optimization problem |
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