Peizhong Liu1, Xiaofang Liu1, Yanming Luo2, Yongzhao Du1, Yulin Fan1, Hsuan‐Ming Feng3
Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 385-394, 2019, DOI:10.31209/2019.100000100
Abstract Aiming at the drawback of artificial bee colony algorithm (ABC) with slow
convergence speed and weak exploitation capacity, an enhanced exploitation
artificial bee colony algorithm is proposed, EeABC for short. Firstly, a
generalized opposition-based learning strategy (GOBL) is employed when initial
population is produced for obtaining an evenly distributed population.
Subsequently, inspired by the differential evolution (DE), two new search
equations are proposed, where the one is guided by the best individuals in the
next generation to strengthen exploitation and the other is to avoid premature
convergence. Meanwhile, the distinction between the employed bee and the
onlooker bee is not… More >
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