Abstract
As a popular swarm intelligence algorithm, artificial bee colony (ABC) achieves excellent optimization performance, but it has some shortcomings. In order to strengthen the performance of ABC, a new ABC with efficient search strategy based on random neighborhood structure (called RNSABC) is proposed. In RNSABC, a new random neighborhood structure (RNS) is constructed. Each solution has an independent and random neighborhood size. An improved search strategy is designed on the basic of RNS. Moreover, a depth first search method is utilized to enhance the role of the onlooker bee phase. To study the optimization capability of RNSABC, a set of 57 benchmark problems including classical problems, CEC 2013 complex problems, and polynomial problems are tested. Experimental results show RNSABC can obtain competitive performance when compared with nine other recent ABC variants.
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