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A novel evolutionary root system growth algorithm for solving multi-objective optimization problems
Ma LB(马连博); Wang XW(王兴伟); Huang M(黄敏); Zhang H(张浩); Chen HN(陈瀚宁)
作者部门数字工厂研究室
关键词Multi-objective Optimization Root Growth Burdening Calculation
发表期刊Applied Soft Computing Journal
ISSN1568-4946
2017
卷号57页码:379-398
收录类别SCI ; EI
EI收录号20171703610553
WOS记录号WOS:000405457200025
产权排序3
资助机构National Natural Science Foundation of China under Grant No.61503373 and No. 61572123 ; National Science Foundation for Distinguished Young Scholars of China under Grant No. 71325002 ; Natural Science Foundation of Liaoning Province under Grand No.2015020002 ; and Fundamental Research Funds for the Central Universities No. N161705001.
摘要This paper proposes a novel multi-objective root system growth optimizer (MORSGO) for the copper strip burdening optimization. The MORSGO aims to handle multi-objective problems with satisfactory convergence and diversity via implementing adaptive root growth operators with a pool of multi-objective search rules and strategies. Specifically, the single-objective root growth operators including branching, regrowing and auxin-based tropisms are deliberately designed. They have merits of appropriately balancing exploring & exploiting and self-adaptively varying population size to reduce redundant computation. The effective multi-objective strategies including the fast non-dominated sorting and the farthest-candidate selection are developed for saving and retrieving the Pareto optimal solutions with remarkable approximation as well as uniform spread of Pareto-optimal solutions. With comprehensive evaluation against a suit of benchmark functions, the MORSGO is verified experimentally to be superior or at least comparable to its competitors in terms of the IGD and HV metrics. The MORSGO is then validated to solve the real-world copper strip burdening optimization with different elements. Computation results verifies the potential and effectiveness of the MORSGO to resolve complex industrial process optimization.
语种英语
WOS标题词Science & Technology ; Technology
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
关键词[WOS]MODEL ; OBJECTIVES ; SIMULATION ; SEARCH
WOS研究方向Computer Science
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/20427
专题数字工厂研究室
通讯作者Ma LB(马连博); Wang XW(王兴伟)
作者单位1.College of Software, Northeastern University, Shenyang, 110819, China
2.College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
4.School of Computer Science and Software, Tianjin Polytechnic University, Tianjin, 300387, China
推荐引用方式
GB/T 7714
Ma LB,Wang XW,Huang M,et al. A novel evolutionary root system growth algorithm for solving multi-objective optimization problems[J]. Applied Soft Computing Journal,2017,57:379-398.
APA Ma LB,Wang XW,Huang M,Zhang H,&Chen HN.(2017).A novel evolutionary root system growth algorithm for solving multi-objective optimization problems.Applied Soft Computing Journal,57,379-398.
MLA Ma LB,et al."A novel evolutionary root system growth algorithm for solving multi-objective optimization problems".Applied Soft Computing Journal 57(2017):379-398.
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