基于pareto最优集的快速双目标差分进化算法

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Software Science and Computational Intelligence-IJSSCI Pub Date : 2016-01-01 DOI:10.4018/IJSSCI.2016010104
Xu Yulong, Zhao Ling-dong
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引用次数: 0

摘要

本文研究了具有pareto最优集的双目标微分演化问题。首先,发现NSGA-II等经典多目标进化算法存在冗余计算;然后,基于pareto最优集的概念、非支配解排序及其潜在特征,提出了一种只处理当前种群中排名最高的个体的解排序方法。该方法的亮点在于,在排序过程中,个体可以同时被选入下一代。当获得下一代种群的个体时,将算法分解。排序过程的个体数量和时间复杂度都降低了。此外,本文还提出了一种均匀拥挤距离的计算方法。最后,将引入的排序法和均匀拥挤距离法引入到差分进化中,得到了一种快速的双目标差分进化算法。为了验证所提出的方法,他们使用经典的最优问题ZDTl~ZDT4和ZDT6进行测试。仿真结果表明,该方法在时间复杂度和性能上都比其他算法有了很大的提高。
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A Fast Two-objective Differential Evolutionary Algorithm based on Pareto-optimal Set
The two-objective differential evolution with Pareto-optimal set, which is researched in this paper. Firstly, it is found that there are some redundant computations in the classic multi-objective evolutionary algorithm, such as the NSGA-II. Then, based on the concept of Pareto-optimal set, the non-dominated solution sorted and its potential features, the authors propose a ranking method for solution that only handles the highest rank individuals in current population. The highlight of the proposed method is that during the ranking process, the individuals can be chosen into the next generation meanwhile. When the individuals of next generation population are obtained the algorithm is broken out. Both the number of individuals for sorting process and the time complexity are reduced. Furthermore, a method of uniform crowding distance calculation is provided in this work. Finally, the authors incorporate the introduced ranking method and uniform crowding distance method into differential evolution, a fast two-objective differential evolution algorithm is obtained. For verifying the proposed method, they use the classical optimal problems ZDTl~ZDT4 and ZDT6 for tesing. Simulation results show that the authors' method has greatly improved in terms of time complexity and performance than other algorithms.
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27.60%
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