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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)
Department数字工厂研究室
Source PublicationApplied Soft Computing Journal
ISSN1568-4946
2017
Volume57Pages:379-398
Indexed BySCI ; EI
EI Accession number20171703610553
WOS IDWOS:000405457200025
Contribution Rank3
Funding OrganizationNational 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.
KeywordMulti-objective Optimization Root Growth Burdening Calculation
AbstractThis 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.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS KeywordMODEL ; OBJECTIVES ; SIMULATION ; SEARCH
WOS Research AreaComputer Science
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20427
Collection数字工厂研究室
Corresponding AuthorMa LB(马连博); Wang XW(王兴伟)
Affiliation1.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
Recommended Citation
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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