Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2024.2420296
Imperialist competition algorithm with quasi-opposition-based learning for function optimization and engineering design problems
Dongge Lei
; College of Electrical and Information Engineering, Quzhou University, Quzhou, People’s Republic of China
Lulu Cai
; College of Electrical and Information Engineering, Quzhou University, Quzhou, People’s Republic of China
Fei Wu
; College of Electrical and Information Engineering, Quzhou University, Quzhou, People’s Republic of China
*
* Dopisni autor.
Sažetak
Imperialist competitive algorithm (ICA) is an efficient meta-heuristic algorithm by simulating the
competitive behaviour among imperialist countries. However, it still suffers from slow convergence and deficiency in exploration. To address these issues, an improved ICA is proposed by
combining ICA with a quasi-opposition-based learning (QOBL) strategy, which is named QOBLICA. The improvements include two aspects. First, the QOBL strategy is adopted to generate a
population of fitter individuals. Second, a QOBL-assisted assimilation strategy is proposed to
enhance the exploration ability of ICA. As a result, the proposed QOBL-ICA has more powerful
exploration ability than ICA as well as faster convergence speed. The effectiveness of the proposed QOBL-ICA is verified by testing on 20 benchmark functions and 3 engineering design
problems. Experimental results show that the performance of QOBL-ICA is superior to most
state-of-the-art meta-heuristic algorithms in terms of global optimum reached and convergence
speed.
Ključne riječi
Imperialist competition algorithm; quasi-opposition-based learning; function optimization; engineering design problem; Wilcoxon test
Hrčak ID:
326449
URI
Datum izdavanja:
28.10.2024.
Posjeta: 0 *