Immune clonal algorithm based on directed evolution for multi-objective capacitated arc routing problem |
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| Affiliation: | 1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, China;2. School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, China;3. School of Computer and Software Extreme Robotics Lab, University of Birmingham, UK;1. Izmir Institute of Technology, İzmir, Turkey;2. Ozyegin University, İstanbul, Turkey;1. Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Duy Tân University, 254 Nguyen Van Linh Road, Da Nang, Viet Nam;3. ICTEAM, Université Catholique de Louvain, 4-6 Avenue G. Lemaître, B-1348 Louvain-La-Neuve, Belgium;1. Computer Science & Engineering Department, American University of Sharjah, P.O. Box 26666, Sharjah, United Arab Emirates;2. Computer Science & Engineering Department, American University of Sharjah, Sharjah, United Arab Emirates;3. University of Science and Technology Houari Boumediene, Algeria;4. Tomsk State University, Russia |
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| Abstract: | The capacitated arc routing problem is playing an increasingly important role in our society, engendering increasing attention from the research community. Among the various models, multi-objective capacitated arc routing problem comes much closer to real-world problems. Therefore, this paper proposes an immune clonal algorithm based on directed evolution to solve this problem. Firstly, the proposed algorithm adopts the framework of the immune clonal algorithm and expands the scale of the initial antibody population in the initialization process to increase the diversity of the antibodies. Secondly, the proposed algorithm is combined with a decomposition strategy in the operations of the immune gene. Antibodies are classified to perform the immune genetic operations, which helps the antibody populations to share the neighborhood information in a timely manner. Thirdly, the proposed algorithm applies a novel kind of comparison operator to build the total population, which helps it to evolve in the direction of a better population and improves the quality of the antibodies. Experimental results suggest that the proposed algorithm can generate better non-dominant solutions than several compared state-of-the-art algorithms, especially for large-scale sets. |
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| Keywords: | Immune clone Decomposition algorithm Comparison operator MO-CARP |
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