Concerning the issue that solution search equation of artificial bee colony (ABC) algorithm does well in exploration but badly in exploitation, a new method is proposed to gradually enhance the exploitation ability of ABC algorithm. In the proposed algorithm, employed bees learn from the local best individuals and proportion of the local best individuals is gradually enhanced. Onlooker bees learn from the local best and global best individuals and proportion of the global best individual is gradually enhanced. A forementioned measures can effectively balance the exploration and exploitation ability of ABC algorithm. The experiments are conducted on 36 benchmark functions, including CEC2014 problems. The performance of the proposed algorithm outperforms those of ABC with neighborhood search (ABC-NS) and composite DE (CoDE) algorithms significantly.
DU Zhenxin1,2,HAN Dezhi2,LIU Guangzhong2,JIA Jianxin2
. Artificial Bee Colony Algorithm with Gradually Enhanced Exploitation[J]. Journal of Shanghai Jiaotong University, 2018
, 52(1)
: 96
-102
.
DOI: 10.16183/j.cnki.jsjtu.2018.01.015
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