Adjustable mode ratio and focus boost search strategy for cat swarm optimization

Pei-Wei Tsai; Xingsi Xue; Jing Zhang; Vaci Istanda; Pei-Wei Tsai; Xingsi Xue; Jing Zhang; Vaci Istanda

doi:10.3934/aci.2021005

Applied Computing and Intelligence

2021, Volume 1, Issue 1: 75-94. doi: 10.3934/aci.2021005

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Research article

Adjustable mode ratio and focus boost search strategy for cat swarm optimization

1.
Swinburne University of Technology, Australia
2.
Fujian Provincial Key Laboratory of Big Data Mining and Applications, FJUT, China
3.
Center for Information Development and Management, FJUT, China
4.
Fujian University of Technology (FJUT), China
5.
Indigenous Peoples Commission, Taipei City Government, Taiwan (R.O.C.)

Academic Editor: Chih-Cheng Hung

Received: 09 December 2021 Accepted: 27 December 2021 Published: 30 December 2021

Evolutionary algorithm is one of the optimization techniques. Cat swarm optimization (CSO)-based algorithm is frequently used in many applications for solving challenging optimization problems. In this paper, the tracing mode in CSO is modified to reduce the number of user-defined parameters and weaken the sensitivity to the parameter values. In addition, a mode ratio control scheme for switching individuals between different movement modes and a search boosting strategy are proposed. The obtained results from our method are compared with the modified CSO without the proposed strategy, the original CSO, the particle swarm optimization (PSO) and differential evolution (DE) with three commonly-used DE search schemes. Six test functions from IEEE congress on evolutionary competition (CEC) are used to evaluate the proposed methods. The overall performance is evaluated by the average ranking over all test results. The ranking result indicates that our proposed method outperforms the other methods compared.
- evolutionary algorithm,
- cat swarm optimization,
- dynamic parameter control,
- swarm intelligence,
- particle swarm optimization,
- differential evolution
Citation: Pei-Wei Tsai, Xingsi Xue, Jing Zhang, Vaci Istanda. Adjustable mode ratio and focus boost search strategy for cat swarm optimization[J]. Applied Computing and Intelligence, 2021, 1(1): 75-94. doi: 10.3934/aci.2021005

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Abstract

Evolutionary algorithm is one of the optimization techniques. Cat swarm optimization (CSO)-based algorithm is frequently used in many applications for solving challenging optimization problems. In this paper, the tracing mode in CSO is modified to reduce the number of user-defined parameters and weaken the sensitivity to the parameter values. In addition, a mode ratio control scheme for switching individuals between different movement modes and a search boosting strategy are proposed. The obtained results from our method are compared with the modified CSO without the proposed strategy, the original CSO, the particle swarm optimization (PSO) and differential evolution (DE) with three commonly-used DE search schemes. Six test functions from IEEE congress on evolutionary competition (CEC) are used to evaluate the proposed methods. The overall performance is evaluated by the average ranking over all test results. The ranking result indicates that our proposed method outperforms the other methods compared.

References

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