Title:
ifferential-Evolution-Algorithms
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Description: : Combined differential evolution algorithm CoDE is a new competitive algorithm, but convergence speed and optimization performance needs to be improved. In order to solve the above problem, the pair differential evolution algorithm CoDE generation strategy and control parameters from the two aspects of improvement, we propose two improved versions MCoDE CoDE and MCoDE-P, and using six typical test Yong Number on improving performance tested. The results show that combines the best individual information MCoDE method can improve CoDE the optimization performance, while the use of the control parameter expansion MCoDE-P method is difficult to achieve the desired effect.
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改进的组合差分进化优化算法.pdf
