Modified artificial bee colony optimization with block perturbation strategy |
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Authors: | Dongli Jia Xintao Duan Muhammad Khurram Khan |
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Affiliation: | 1. School of Information and Electronic Engineering, Hebei University of Engineering, PR Chinajwdsli@gmail.com;3. School of Computer and Information Technology, Henan Normal University, PR China;4. Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh, Kingdom of Saudi Arabia |
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Abstract: | As a newly emerged swarm intelligence-based optimizer, the artificial bee colony (ABC) algorithm has attracted the interest of researchers in recent years owing to its ease of use and efficiency. In this article, a modified ABC algorithm with block perturbation strategy (BABC) is proposed. Unlike basic ABC, in the BABC algorithm, not one element but a block of elements from the parent solutions is changed while producing a new solution. The performance of the BABC algorithm is investigated and compared with that of the basic ABC, modified ABC, Brest's differential evolution, self-adaptive differential evolution and restart covariance matrix adaptation evolution strategy (IPOP-CMA-ES) over a set of widely used benchmark functions. The obtained results show that the performance of BABC is better than, or at least comparable to, that of the basic ABC, improved differential evolution variants and IPOP-CMA-ES in terms of convergence speed and final solution accuracy. |
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Keywords: | artificial bee colony swarm intelligence evolution strategy differential evolution |
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