Journal
SOFT COMPUTING
Volume 21, Issue 13, Pages 3759-3768Publisher
SPRINGER
DOI: 10.1007/s00500-016-2029-x
Keywords
Global optimization; Differential evolution; Self-adaptive; Neighborhood search
Categories
Funding
- National Natural Science Foundation of China [61563019, 61300127, 61402481]
- Natural Science Foundation of Jiangxi, China [20151BAB217010, 20151BAB201015]
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Differential evolution (DE) is a simple yet efficient stochastic search approach for numerical optimization. However, it tends to suffer from slow convergence when tackling complicated problems. In addition, its search ability is significantly influenced by its control parameters. To improve the performance of the basic DE, this paper proposes a self-adaptive differential evolution with global neighborhood search (NSSDE). In the proposed NSSDE, its control parameters are self-adaptively tuned according to the feedback from the search process, while the global neighborhood search strategy is incorporated to accelerate the convergence speed. To evaluate the performance of the proposed NSSDE, we compare it with several DE variants on a set of benchmark test functions. The experimental results show that NSSDE can achieve better results than its competitors on the majority of the benchmark test functions.
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