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Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

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Abstract

The existing algorithms to solve dynamic multiobjective optimization (DMO) problems generally have difficulties in non-uniformity, local optimality and non-convergence. Based on artificial immune system, quantum evolutionary computing and the strategy of co-evolution, a quantum immune clonal coevolutionary algorithm (QICCA) is proposed to solve DMO problems. The algorithm adopts entire cloning and evolves the theory of quantum to design a quantum updating operation, which improves the searching ability of the algorithm. Moreover, coevolutionary strategy is incorporated in global operation and coevolutionary competitive operation and coevolutionary cooperative operation are designed to improve the uniformity, the diversity and the convergence performance of the solutions. The results on test problems and performance metrics compared with ICADMO and DBM suggest that QICCA has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.

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Acknowledgments

We would like to express our sincere appreciation to the anonymous reviewers for their insightful 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 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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Authors and Affiliations

  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi’an, 710071, China

    Ronghua Shang, Licheng Jiao, Yujing Ren, Lin Li & Luping Wang

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  1. Ronghua Shang
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  2. Licheng Jiao
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  3. Yujing Ren
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  4. Lin Li
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  5. Luping Wang
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Correspondence to Ronghua Shang.

Additional information

Communicated by Y.-S. Ong.

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Shang, R., Jiao, L., Ren, Y. et al. Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization. Soft Comput 18, 743–756 (2014). https://doi.org/10.1007/s00500-013-1085-8

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