[1]QIAN Xiaoyu,GE Hongwei,CAI Ming.Decomposition and continuous mutation-based multi-objective particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2019,14(3):464-470.[doi:10.11992/tis.201711015]
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Decomposition and continuous mutation-based multi-objective particle swarm optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 3 Page number: 464-470 Column: 学术论文—机器学习 Public date: 2019-05-05
Title:
Decomposition and continuous mutation-based multi-objective particle swarm optimization
Author(s):
QIAN Xiaoyu1; 2;  GE Hongwei1; 2;  CAI Ming3
1. Ministry of Education Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;
2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
3. Information Construction and Ma
Keywords:
multi-objective optimization; particle swarm optimization algorithm; decomposition; sub-region; mutation; differential; Gaussian mutation; Cauchy mutation
CLC:
TP391.4
DOI:
10.11992/tis.201711015
Abstract:
In light of the poor convergence problems and the diversity of current multi-objective optimization algorithms, in this paper, we propose an objective-space decomposition and continuous mutation-based multi-objective particle-swarm-optimization algorithm. Its innovations are as follows:we use a space decomposition method to distribute the particle swarm into a predefined sub-region. During this process, we apply a new adaptive value formula to select and filter the particles in each sub-region and incorporate a fitness formula into the dominance factor. In the global search process, we apply differential, Gaussian, and Cauchy mutations to continuously mutate the position of the global guide particle. We compare the performance of this algorithm with those of current multi-objective optimization algorithms, and the results show that the proposed algorithm improves the convergence and diversity of the particles.
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