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Artificial bee colony algorithm based on multiple neighborhood topologies
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DOI：10.1016/j.asoc.2021.107697.png)
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Abstract
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In recent years, as an effective global optimization technique, artificial bee colony (ABC) algorithm has attracted increasing attention for its good performance yet easy implementation. However, the solution search equation of ABC emphasizes exploration over exploitation, which greatly affects the convergence speed and accuracy of ABC. To solve this problem, many ABC variants have been developed to enhance exploitation by using the superior individuals. However, the concept of neighborhood topology has rarely been considered, which has significant effect on the dissemination of search information among individuals. Hence, in this work, we propose a new ABC variant based on multiple neighborhood topologies (named ABC-MNT). In ABC-MNT, three kinds of neighborhood topologies are used for different individuals to perform diverse abilities of disseminating search information, contributing to a better balance between exploration and exploitation. Considering the characteristics of different neighborhood topologies, three modified solution search equations are assigned to the three neighborhood topologies, respectively, to generate offspring. Furthermore, to preserve search experience of the scout bee phase, the global neighborhood search and opposition-based learning techniques are used. Extensive experiments are carried out on two widely used test suites, and eight well-established ABC variants and five other evolutionary algorithms are included in the performance comparison. The comparison results verify that ABC-MNT can obtain better or at least comparable performance on most of the benchmark functions. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Artificial bee colony
Solution search equation
Multiple neighborhood topologies
Multi-population technique
Search experience
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