Abstract
In this paper, a new evolutionary algorithm, called immune clonal coevolutionary algorithm (ICCoA) for dynamic multiobjective optimization (DMO) is proposed. On the basis of the basic principles of artificial immune system, the proposed algorithm adopts the immune clonal selection to solve DMO problems. In addition, the theory of coevolution is incorporated in ICCoA in global operation to preserve the diversity of Pareto-fronts. Moreover, coevolutionary competitive and cooperative operation is designed to enhance the uniformity and the diversity of the solutions. In comparison with NSGA-II, immune clonal algorithm for DMO and direction-based method, the simulation results obtained on 5 difficult test problems and on related performance metrics suggest that ICCoA can achieve better distributed solutions and be very effective in maintaining the uniformity of Pareto-fronts.
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Acknowledgments
We would like to express our sincere appreciation to the anonymous reviewers for their insightful and valuable comments, which have greatly helped us in improving the quality of the paper. This work was partially supported by the National Basic Research Program (973 Program) of China under Grant 2013CB329402, the National Natural Science Foundation of China, under Grants 61371201, 61001202, 61203303, and 61272279, the National Research Foundation for the Doctoral Program of Higher Education of China, under Grants 20100203120008, the Fundamental Research Funds for the Central Universities, under Grant K5051302028, the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) under Grant B07048, and the Program for Cheung Kong Scholars and Innovative Research Team in University under Grant IRT1170.
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Shang, R., Jiao, L., Ren, Y. et al. Immune clonal coevolutionary algorithm for dynamic multiobjective optimization. Nat Comput 13, 421–445 (2014). https://doi.org/10.1007/s11047-014-9415-z
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DOI: https://doi.org/10.1007/s11047-014-9415-z