A tri-objective differential evolution approach for multimodal optimization
A tri-objective differential evolution approach for multimodal optimization
复制标题
多模态优化的三目标差分进化方法
DOI：
10.1016/j.ins.2017.09.044
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发表时间：
2018
影响因子：
8.1
通讯作者：
Zhang Jun
中科院分区：
文献类型：
--
作者：
Yu Wei-Jie;Ji Jing-Yu;Gong Yue-Jiao;Yang Qiang;Zhang Jun
多峰优化问题（MMOP）需要同时找到多个最优值，因此群体多样性是设计MMOP进化优化算法时应考虑的关键问题。利用进化多目标优化在保持良好种群多样性方面的优势，本文提出了一种三目标差分进化（DE）方法来解决MMOP。给定一个 MMOP，我们首先将其转换为三目标优化问题（TOP）。这三个优化目标是基于 1）MMOP 的目标函数，2）由一组参考点测量的个体距离信息，以及 3）基于小生境技术的共享适应度构建的。前两个目标是相互冲突的，这样才能充分发挥进化多目标优化的优势。通过对生态位参数不敏感的生态位技术构建的第三个目标，大大提高了种群多样性。数学证明表明TOP的帕累托最优前沿包含了MMOP的所有全局最优值。随后，应用基于DE的多目标优化技术来求解转换后的TOP。此外，引入了改进的解决方案比较标准和DE的自适应排序策略，以提高解决方案的准确性。对 44 个基准函数进行了实验，以评估所提出方法的性能。结果表明，与几种最先进的多模态优化算法相比，所提出的方法实现了具有竞争力的性能。
The multimodal optimization problems (MMOPs) need to find multiple optima simultaneously, so the population diversity is a critical issue that should be considered in designing an evolutionary optimization algorithm for MMOPs. Taking advantage of evolutionary multiobjective optimization in maintaining good population diversity, this paper proposes a tri-objective differential evolution (DE) approach to solve MMOPs. Given an MMOP, we first transform it into a tri-objective optimization problem (TOP). The three optimization objectives are constructed based on 1) the objective function of an MMOP, 2) the individual distance information measured by a set of reference points, and 3) the shared fitness based on niching technique. The first two objectives are mutually conflicting so that the advantage of evolutionary multiobjective optimization can be fully used. The population diversity is greatly improved by the third objective constructed by the niching technique which is insensitive to niching parameters. Mathematical proofs are given to demonstrate that the Pareto-optimal front of the TOP contains all global optima of the MMOP. Subsequently, DE-based multiobjective optimization techniques are applied to solve the converted TOP. Moreover, a modified solution comparison criterion and an adaptive ranking strategy for DE are introduced to improve the accuracy of solutions. Experiments have been conducted on 44 benchmark functions to evaluate the performance of the proposed approach. The results show that the proposed approach achieves competitive performance compared with several state-of-the-art multimodal optimization algorithms.
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影响因子：
14.3
作者：
Jie Yao;N. Kharma;P. Grogono
通讯作者：
Jie Yao;N. Kharma;P. Grogono
通过检测有希望的区域改进多模态问题的分布算法估计
影响因子：
11.8
作者：
P. Yang;K. Tang;X. Lu
通讯作者：
X. Lu
影响因子：
11.8
作者：
Yang Qiang;Chen Wei-Neng;Li Yun;Chen C. L. Philip;Xu Xiang-Min;Zhang Jun
通讯作者：
Zhang Jun
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
一种廉价和昂贵的局部搜索方法的分布估计算法
影响因子：
14.3
作者：
Zhou Aimin;Sun Jianyong;Zhang Qingfu
通讯作者：
Zhang Qingfu