An enhanced hybridized artificial bee colony algorithm for optimization problems

Xingwang Huang; Xuewen Zeng; Rui HanXu Wang

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An enhanced hybridized artificial bee colony algorithm for optimization problems

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Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

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来源
《IAES International Journal of Artificial Intelligence》 |2019年第1期|87-94|共8页
作者
Xingwang Huang; Xuewen Zeng; Rui HanXu Wang;
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作者单位

National Network New Media Engineering Research Center, Institute of Acoustics, Chinese Academy of Sciences;

University of Chinese Academy of Sciences;

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原文格式 PDF
正文语种英语
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关键词
Artificial bee colony algorithm; Genetic algorithm; Population initialization; Search equation;

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An enhanced hybridized artificial bee colony algorithm for optimization problems

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