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Multi-strategy ensemble firefly algorithm with equilibrium of convergence and diversity.

Authors :
Zhao, Jia
Chen, Dandan
Xiao, Renbin
Cui, Zhihua
Wang, Hui
Lee, Ivan
Source :
Applied Soft Computing; Jul2022, Vol. 123, pN.PAG-N.PAG, 1p
Publication Year :
2022

Abstract

Balancing the convergence and diversity in the multi-objective firefly algorithm is essential for obtaining high precision and well distributed Pareto front. However, most existing algorithms cannot​ guarantee such balance, leading to a poor comprehensive performance. To address this limitation, this paper proposes a multi-strategy ensemble firefly algorithm with equilibrium of convergence and diversity (MEFA-CD). Firstly, an improved linear congruence method is used to generate the initial population with uniform distribution, to provide a good start for the subsequent population evolution and ensure the global search ability; Secondly, a hybrid learning strategy is utilized to identify the best elite solution according to the maximum fitness value. Combined with the current best solution, the firefly is guided to learn under the effect of compensation factor. On the one hand, it breaks through the population constraints, which yields a faster convergence to the Pareto optimal solution set. On the other hand, it expands the search range of the population, which improves the diversity and the accuracy of the Pareto optimal set; Finally, the crowding distance mechanism is used to delete the aggregation solution, which maintains the diversity of external files and ensures the local development ability of the population, and further improves the convergence of the algorithm. Experimental results show that, compared with other multi-objective optimization algorithms, the proposed algorithm has better performance in convergence and diversity, among which the optimization performance is improved by 61% compared with the standard MOFA. • A novel MOEA with equilibrium of convergence and diversity is proposed. • A strategy for generating uniformly distributed initial solutions is proposed. • A hybrid learning strategy for enhancing the convergence and diversity of the algorithm is proposed. • The collaboration between multiple strategies to balance the exploration and development of the algorithm. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15684946
Volume :
123
Database :
Supplemental Index
Journal :
Applied Soft Computing
Publication Type :
Academic Journal
Accession number :
157285369
Full Text :
https://doi.org/10.1016/j.asoc.2022.108938