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中文摘要：
针对传统差分演化算法在演化过程中存在少数个体出现停滞的现象,提出一种基于精英云变异的差分演化算法.该算法在演化过程中统计出每个个体的停滞代数,当一个个体的停滞代数达到指定的阈值时,对该个体执行精英云变异操作,使其向最优个体靠近,从而加快收敛速度;同时以一定的概率对所有个体执行一般反向学习操作,以增加种群的多样性.对比实验结果表明该算法在收敛速度和求解精度上均具有一定的优势.
英文摘要：
Aiming at the disadvantage of traditional differential evolution, namely, existing some stagnating indi- viduals in the evolutionary process, a novel differential evolution algorithm based on elite-cloudy mutation （ECMDE） is proposed in this study. In the proposed algorithm, stagnation generation of each individual is counted in the evolu- tionary process. Moreover, an individual is executed by the elite-cloudy mutation to approach the best individual when the stagnation generation of the individual is more than a pre-defined threshold value. Thus, it can accelerate the con- vergence speed. Additionally, in order to increase the population diversity, it executes the opposition-based learning operator with a certain probability. Experimental results indicate that the proposed algorithm obtains promising per- formance in both solution precision and convergence speed.
同期刊论文项目
同项目期刊论文
An enhanced differential evolution algorithm for solving large scale optimisation problems on graphi
Parallel differential evolution with self-adapting control parameters and generalized opposition-bas
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