A dynamic multi-colony artificial bee colony algorithm for multi-objective optimization |
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Affiliation: | 1. School of Data Science and Computer, Sun Yat-sen University, Guangzhou 510275, PR China;2. Collaborative Innovation Center of High Performance Computing, Sun Yat-sen University, Guangzhou 510275, PR China;1. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Henan, 45000, PR China;2. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, 74078, USA;1. Department of Industrial Engineering, İzmir Bakırçay University, İzmir 35665, Turkey;2. Department of Industrial Engineering, Faculty of Engineering, Dokuz Eylül University, İzmir 35397, Turkey;1. School of Physics and Electronic Information, Huaibei Normal University, Huaibei, 235000, China;2. School of Automation, Guangdong University of Technology, Guangdong, 510006, China |
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Abstract: | This paper suggests a dynamic multi-colony multi-objective artificial bee colony algorithm (DMCMOABC) by using the multi-deme model and a dynamic information exchange strategy. In the proposed algorithm, K colonies search independently most of the time and share information occasionally. In each colony, there are S bees containing equal number of employed bees and onlooker bees. For each food source, the employed or onlooker bee will explore a temporary position generated by using neighboring information, and the better one determined by a greedy selection strategy is kept for the next iterations. The external archive is employed to store non-dominated solutions found during the search process, and the diversity over the archived individuals is maintained by using crowding-distance strategy. If a randomly generated number is smaller than the migration rate R, then an elite, defined as the intermediate individual with the maximum crowding-distance value, is identified and used to replace the worst food source in a randomly selected colony. The proposed DMCMOABC is evaluated on a set of unconstrained/constrained test functions taken from the CEC2009 special session and competition in terms of four commonly used metrics EPSILON, HV, IGD and SPREAD, and it is compared with other state-of-the-art algorithms by applying Friedman test on the mean of IGD. The test results show that DMCMOABC is significantly better than or at least comparable to its competitors for both unconstrained and constrained problems. |
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Keywords: | Multi-objective optimization Multi-colony model Artificial bee colony algorithm Migration strategy Friedman test |
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