Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

Symmetry

ChangGeun Song¹  Sun-Jeong Kim¹  Zong-Sheng Wang¹  Jung Lee¹ 

[1] Department of Convergence Software, Hallym University, Chuncheon-si, Gangwon-do 24252, Korea;

关键词: optimization methods;    chaos;    evolutionary computation;    iterative algorithms;    path planning;    imperialist competitive algorithm;   

DOI  :  10.3390/sym12040635

来源: DOAJ

【 摘 要 】

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

【 授权许可】

Unknown