Artificial bee colony algorithm with gene recombination for numerical function optimization |
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| Affiliation: | 1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, PR China;2. College of Mathematics and Statistics, Shenzhen University, Shenzhen, PR China;1. School of Aerospace, Transport Systems and Manufacturing, Cranfield University, College Road, Bedfordshire MK43 0AL, UK;2. College of Engineering, Mathematics and Physical Systems, University of Exeter, EX4 4SB, UK;1. Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;2. Department of Computer Engineering, Hashtgerd Branch, Islamic Azad University, Alborz, Iran;1. Universidad Nacional de Córdoba – CONICET, Córdoba, Argentina;2. Universidad Nacional de San Luis, San Luis, Argentina;1. School of Computer and Information, Anqing Normal University, Anqing 246133, China;2. School of Engineering and Computer Science, Victoria University of Wellington, Kelburn 6012, New Zealand;3. USTC-Birmingham Joint Research Institute in Intelligent Computation and its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China;1. National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan, China;2. Hunan University of Humanities, Science and Technology, School of Energy and Mechanical-electronic Engineering, Loudi, China;1. School of Information Engineering, Xiangtan University, Hunan, China;2. School of Computer Science and Technology, Hengyang Normal University, Hunan, China;3. School of Mathematical and Computational Science, Xiangtan University, Hunan, China;4. School of Computer Science and IT, RMIT University, Melbourne, Australia |
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| Abstract: | Artificial bee colony (ABC) algorithm is a stochastic and population-based optimization method, which mimics the collaborative foraging behaviour of honey bees and has shown great potential to handle various kinds of optimization problems. However, ABC often suffers from slow convergence speed since its internal mechanism and solution search equation do well in exploration, but badly in exploitation. In order to solve this knotty issue, inspired by the natural phenomenon that the good individuals (solutions) always contain good genes (variables) and the effective combination of the superior genes from different good individuals could more easily produce better offspring, we introduce a novel gene recombination operator (GRO) into ABC to accelerate convergence. To be specific, in GRO, a part of good solutions in the current population are selected to produce candidate solutions by the gene combination. Especially, each good solution recombines with only one other good solution to generate only one candidate solution. In addition, GRO will be launched at the end of each generation. In order to validate its efficiency and effectiveness, GRO is embedded into nine versions of ABC, i.e., the original ABC, GABC, best-so-far ABC(BSFABC), MABC, CABC, ABCVSS, qABC, dABC and distABC, while yields GRABC, GRGABC, GRBSFABC, GRMABC, GRCABC, GRABCVSS, GRqABC, GRdABC and GRdistABC respectively. The experimental results on 22 benchmark functions demonstrate that GRO could enhance the exploitation ability of ABCs and accelerate convergence without loss of diversity. |
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| Keywords: | Swarm intelligence Artificial bee colony Gene recombination Numerical function optimization |
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