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Multiple Exponential Recombination for Differential Evolution
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DOI：10.1109/TCYB.2016.2536167.png)
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摘要
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Differential evolution (DE) is a popular populationbased metaheuristic approach for solving numerical optimization problems. In recent years, considerable research has been devoted to the development of new mutation strategies and parameter adaptation mechanisms. However, as one of the basic algorithmic components of DE, the crossover operation has not been sufficiently examined in existing works. Most of the main DE variants solely employ traditional binomial recombination, which has intrinsic limitations in handling dependent subsets of variables. To fill this research niche, we propose a multiple exponential recombination that inherits all the main advantages of existing crossover operators while possessing a stronger ability in managing dependent variables. Multiple segments of the involved solutions will be exchanged during the proposed operator. The properties of the new scheme are examined both theoretically and empirically. Experimental results demonstrate the robustness of the proposed operator in solving problems with unknown variable interrelations.
Keyword:
Crossover operator
differential evolution (DE)
variable interrelation
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IF:
10.5
论文数:
1.1W
被引数:
5.0W
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Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
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