A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm

被引:0
作者
Brajevic, Ivona [1 ,2 ]
Stanimirovic, Predrag S. [2 ]
Li, Shuai [3 ]
Cao, Xinwei [4 ]
机构
[1] Univ Business Acad, Fac Appl Management Econ & Finance, Jevrejska 24, Belgrade 11000, Serbia
[2] Univ Nis, Fac Sci & Math, Visegradska 33, Nish 18000, Serbia
[3] Swansea Univ, Coll Engn, Fabian Way, Swansea SA1 8EN, W Glam, Wales
[4] Shanghai Univ, Sch Management, 99 Shangda Rd, Shanghai 201900, Peoples R China
关键词
Firefly algorithm; Artificial bee colony; Multi-strategy; Hybrid algorithm; Global optimization; PARTICLE SWARM OPTIMIZATION; ENSEMBLE;
D O I
10.2991/ijcis.d.200612.001
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems. In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence. A novel multi-strategy ABC is employed to perform local search. The proposed algorithm incorporates a diversity measure to help in the switch criteria. The FA-MABC is tested on 40 benchmark functions with diverse complexities. Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC. (C) 2020 The Authors. Published by Atlantis Press SARL.
引用
收藏
页码:810 / 821
页数:12
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