Triangle Search Optimization Algorithm for Single-Objective Bound-Constrained Numerical Optimization

Real-parameter optimization has been a focus of the last decade. An inspired algorithm, triangle search optimization (TSO), is proposed for the Congress on Evolutionary Computation (CEC) 2020 competition. In this paper, the TSO algorithm is divided into two phases: the triangle vertex searching (TVS...

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Bibliographic Details
Published in2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE) pp. 1531 - 1539
Main Authors Wei, Zhenglei, Tang, Shangqin, Xie, Lei, Tang, Andi, Li, Yintong, Zhang, Peng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2020
Subjects
Benchmark testing
CEC2020
Covariance matrices
Evolutionary computation
numerical optimization
Optimization
Search problems
Sociology
Triangle search optimization
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Summary:Real-parameter optimization has been a focus of the last decade. An inspired algorithm, triangle search optimization (TSO), is proposed for the Congress on Evolutionary Computation (CEC) 2020 competition. In this paper, the TSO algorithm is divided into two phases: the triangle vertex searching (TVS) and triangle edge searching (TES) phases. In the TVS phase, the population is divided into two subpopulations, which can be enhanced by vertex searching operators and the covariance matrix adaptation evolution strategy (CMA-ES) for exploitation. In the TES phase, the differential evolution vector between superior and inferior solutions is employed to improve the exploration ability. The TSO algorithm is evaluated on the CEC 2020 benchmark problems and compared with the winners of the CEC competition and novel competitors. The experimental results show that TSO outperforms the compared algorithms.
DOI:10.1109/ICMCCE51767.2020.00336