A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization

Wang X, Zhang B, Wang J, Zhang K, Jin Y (2022)
Swarm and Evolutionary Computation 71: 101083.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Wang, Xiangyu; Zhang, Bingran; Wang, Jian; Zhang, Kai; Jin, YaochuUniBi
Abstract / Bemerkung
Many different types of multi-objective optimization problems, e.g. multi-modal problems and large-scale prob-lems, have been solved with high performance by numbers of tailored multi-objective evolutionary algorithms. Little attention has been paid on sparse optimization problems, whose most decision variables are zero in the Pareto optimal solution set. Most recently, algorithms for solving sparse problems have been developed rapidly, and many sparse optimization problems in machine learning, such as the search for lightweight neural networks, can be solved with the help of multi-objective evolutionary algorithms. In this paper, we introduce a sparse trun-cation operator which uses the accumulative gradient value as a criterion for setting a decision variable to zero. In addition, to balance the exploration and exploitation, a cluster-based competitive particle swarm optimizer is pro-posed, which takes advantage of both particle swarm optimization and competitive swarm optimizer to search efficiently and escape from local optima. Consequently, aiming at solving sparse multi-objective optimization problems, a novel cluster-based competitive particle swarm optimizer with a sparse truncation operator is pro-posed, and experimental results show that the proposed algorithm outperforms its peers on sparse test instances and neural network training tasks.
Stichworte
Sparse Pareto optimal solutions; Particle swarm optimization (PSO); Competitive swarm optimization (CSO); Accumulative gradient; Neural; network
Erscheinungsjahr
2022
Zeitschriftentitel
Swarm and Evolutionary Computation
Band
71
Art.-Nr.
101083
ISSN
2210-6502
eISSN
2210-6510
Page URI
https://pub.uni-bielefeld.de/record/2964066

Zitieren

Wang X, Zhang B, Wang J, Zhang K, Jin Y. A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization. Swarm and Evolutionary Computation. 2022;71: 101083.
Wang, X., Zhang, B., Wang, J., Zhang, K., & Jin, Y. (2022). A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization. Swarm and Evolutionary Computation, 71, 101083. https://doi.org/10.1016/j.swevo.2022.101083
Wang, X., Zhang, B., Wang, J., Zhang, K., and Jin, Y. (2022). A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization. Swarm and Evolutionary Computation 71:101083.
Wang, X., et al., 2022. A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization. Swarm and Evolutionary Computation, 71: 101083.
X. Wang, et al., “A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization”, Swarm and Evolutionary Computation, vol. 71, 2022, : 101083.
Wang, X., Zhang, B., Wang, J., Zhang, K., Jin, Y.: A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization. Swarm and Evolutionary Computation. 71, : 101083 (2022).
Wang, Xiangyu, Zhang, Bingran, Wang, Jian, Zhang, Kai, and Jin, Yaochu. “A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization”. Swarm and Evolutionary Computation 71 (2022): 101083.

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