Abstract
Differential evolution (DE) is a remarkable evolutionary algorithm for global optimization over continuous search space, whose performance is significantly influenced by its mutation operator and control parameters (scaling factor and crossover rate). In order to enhance the performance of DE, we adopt a novel Gaussian mutation operator and a modified common mutation operator to collaboratively produce new mutant vectors, and employ a periodic function and a Gaussian function to generate the required values of scaling factor and crossover rate, respectively. In the proposed variant of DE (denoted by GPDE), the two adopted mutation operators are adaptively applied to generate the corresponding mutant vector of each individual based on their own cumulative scores, the periodic scaling factor can provide a better balance between exploration ability and exploitation ability, and the Gaussian function-based crossover rate will possess fluctuant value, which possibly enhance the population diversity. To verify the performance of proposed GPDE, a suite of thirty benchmark functions and four real-world problems are applied to conduct the simulation experiment. The simulation results demonstrate that the proposed GPDE performs significantly better than five state-of-the-art DE variants and other two meta-heuristics algorithms.
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Acknowledgements
The authors wish to thank the anonymous reviewers, whose valuable comments lead to an improved version of the paper. This work was supported by the National Natural Science Foundation of China under Grant Nos. 71701187, 71771166 and 71471126, and Research Project of Zhejiang Education Department under Grant No. Y201738184, and High Performance Computing Center of Tianjin University, China.
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Sun, G., Lan, Y. & Zhao, R. Differential evolution with Gaussian mutation and dynamic parameter adjustment. Soft Comput 23, 1615–1642 (2019). https://doi.org/10.1007/s00500-017-2885-z
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DOI: https://doi.org/10.1007/s00500-017-2885-z