《计算机应用研究》|Application Research of Computers

基于策略自适应的多目标差分进化算法及其应用

Multi-objective differential evolution algorithm based on self-adaptive strategy and its application

免费全文下载 (已被下载 次)  
获取PDF全文
作者 毕超超,范勤勤,王维莉
机构 1.上海海事大学 物流研究中心,上海 201306;2.上海交通大学 电子信息与电气工程学院,上海 200240
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2020)07-019-2016-06
DOI 10.19734/j.issn.1001-3695.2018.12.0931
摘要 为提高多目标差分进化算法求解多目标优化问题的能力,提出一种基于策略自适应的多目标差分进化算法(multi-objective differential evolution algorithm based on self-adaptive strategy,MODE-SS)。该算法采用超体积(hyper-volume,HV)对变异策略进行性能评价,并实现变异策略的自动选择;使用动态调整的二项式交叉策略和模拟二进制交叉(simulated binary crossover,SBX)策略实现全局搜索与局部搜索的平衡。通过与其他六种多目标进化算法在10个测试函数上的性能比较,结果表明MODE-SS算法的整体性能要好于其他所比较算法。最后,将MODE-SS算法用于求解海铁联运能耗优化问题,所得结果能够为决策者提供多种可行方案。
关键词 差分进化; 多目标优化; 自适应; 海铁联运; 能耗优化
基金项目 国家重点研发计划资助项目(2016YFC0800200)
国家自然科学基金资助项目(61603244)
中国博士后科学基金资助项目(2018M642017)
本文URL http://www.arocmag.com/article/01-2020-07-019.html
英文标题 Multi-objective differential evolution algorithm based on self-adaptive strategy and its application
作者英文名 Bi Chaochao, Fan Qinqin, Wang Weili
机构英文名 1.Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China;2.School of Electronic Information & Electrical Engineering,Shanghai JiaoTong University,Shanghai 200240,China
英文摘要 To improve the capability of multi-objective differential algorithm, this paper proposed a multi-objective differential evolution algorithm based on self-adaptive strategy(MODE-SS). This algorithm not only used the hyper-volume to evaluate the performance of mutation strategies and selected a suitable mutation strategy automatically, but also employed binomial and si-mulated binary crossover strategies to balance global and local search capabilities. The results on 10 test functions show that MODE-SS outperforms the other six state-of-the-art multi-objective optimization algorithms. Finally, this paper used MODE-SS to solve the problem of energy consumption optimization of sea-rail intermodal transportation. The results can provide a set of available solutions to decision-makers.
英文关键词 differential evolution(DE); multi-objective optimization; self-adaptation; sea-rail intermodal transportation; energy optimization
参考文献 查看稿件参考文献
 
收稿日期 2018/12/19
修回日期 2019/2/26
页码 2016-2021
中图分类号 TP301.6
文献标志码 A