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Quantum-inspired multi-objective optimization evolutionary algorithm based on decomposition

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Abstract

As an important multi-objective optimization algorithm, multi-objective evolutionary algorithm based on decomposition (MOEA/D) attracts more and more attention recently. In this paper, some methods inspired from quantum behavior are integrated in MOEA/D. A new algorithm, quantum-inspired MOEA/D (QMOEA/D), is proposed and proved to be effective to improve the performance of MOEA/D. In the new algorithm, a global solution (GS) and a local solution (LS) are stored for each subproblem. The attractor and characteristic length in quantum-inspired method are designed with GS and LS. The LS is selected as the attractor for each subproblem. And the characteristic length is associated with the difference between the LS and GS. The algorithm based on nondominated sorting is used for comparing firstly. Then the original and some advanced versions of MOEA/D are used as the comparison algorithms. Through the comparison it can be found that GS and LS are helpful to retain the diversity of the solutions. A wide Pareto front can be obtained on most of the test suites. And the quantum-inspired generator is effective to obtain better solutions with GS and LS.

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Acknowledgments

This work was supported by the Cheung Kong Scholars Program of China (Grant No. K5051302050), the Program for New Century Excellent Talents in University (No. NCET-12-0920), the Program for New Scientific and Technological Star of Shaanxi Province (No. 2014KJXX-45), the National Natural Science Foundation of China (Nos. 61272279, 61272282, 61001202 and 61203303), the Fundamental Research Funds for the Central Universities (Nos. K5051302049, K5051302023, K50511020011, K5051302002 and K5051302028), the Provincial Natural Science Foundation of Shaanxi of China (No. 2011JQ8020), the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048) and EU IRSES project (No. 247619).

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Authors and Affiliations

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

    Yang Wang, Yangyang Li & Licheng Jiao

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  1. Yang Wang
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  2. Yangyang Li
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  3. Licheng Jiao
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Correspondence to Yang Wang.

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Communicated by V. Loia.

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Wang, Y., Li, Y. & Jiao, L. Quantum-inspired multi-objective optimization evolutionary algorithm based on decomposition. Soft Comput 20, 3257–3272 (2016). https://doi.org/10.1007/s00500-015-1702-9

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  • DOI: https://doi.org/10.1007/s00500-015-1702-9

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