期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 52, 期 3, 页码 1578-1587出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2020.3030792
关键词
Optimization; Sociology; Statistics; Biological cells; Convergence; Logic gates; Linear programming; Differential mutation; global optimization problems; multipopulation mutation evolution mechanism; optimization performance; quantum-inspired differential evolution (QDE); quantum rotation gate
资金
- National Natural Science Foundation of China [61771087, 51475065, 51605068]
- Open Project Program of State Key Laboratory of Mechanical Transmissions of Chongqing University [SKLMT-KFKT-201803]
- Traction Power State Key Laboratory of Southwest Jiaotong University [TPL 2002]
- Central University Basic Scientific Research Business Fee Project of Civil Aviation University of China [2000420534]
In this paper, an enhanced MSIQDE algorithm based on mixing multiple strategies, called EMMSIQDE, is proposed to overcome the limitations of QDE in optimization problems. EMMSIQDE achieves better optimization performance by using new mutation strategies and evolution mechanisms, as well as a feasible solution space transformation strategy.
Quantum-inspired differential evolution (QDE) is an evolutionary algorithm, which can effectively solve complex optimization problems. However, sometimes, it easily leads to premature convergence and low search ability and falls to local optima. To overcome these problems, based on the MSIQDE (improved QDE with multistrategies) algorithm, an enhanced MSIQDE algorithm based on mixing multiple strategies, namely, EMMSIQDE is proposed in this article. In the EMMSIQDE, a new differential mutation strategy of a difference vector is proposed to enhance the search ability and descent ability. Then, a new multipopulation mutation evolution mechanism is designed to ensure the relative independence of each subpopulation and the population diversity. The feasible solution space transformation strategy is used to achieve the optimal solution by mapping the quantum chromosome from a unit space to solution space. Finally, some multidimensional unimodal and multimodal functions are selected to demonstrate the optimization performance of EMMSIQDE. The results demonstrate that the EMMSIQDE is significantly better than the DE, QDE, QGA, and MSIQDE, and has better optimization ability, scalability, efficiency, and stability.
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