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Self-adaptive root growth model for constrained multi-objective optimization
Zhang H(张浩); Zhu YL(朱云龙); Zhang DY(张丁一)
Department信息服务与智能控制技术研究室
Conference Name2013 IEEE Congress on Evolutionary Computation, CEC 2013
Conference DateJune 20-23, 2013
Conference PlaceCancun, Mexico
Source Publication2013 IEEE Congress on Evolutionary Computation, CEC 2013
PublisherIEEE Computer Society
Publication PlaceWashington, United States
2013
Pages2360-2367
Indexed ByEI ; CPCI(ISTP)
EI Accession number20133416645342
WOS IDWOS:000326235302048
Contribution Rank1
ISBN978-1-4799-0454-9
KeywordBenchmarking Constrained Optimization
AbstractThis paper presents a general optimization model gleaned ideas from plant root growth behaviors in the soil. The purpose of the study is to investigate a novel biologically inspired methodology for complex system modelling and computation, particularly for constrained multi-objective optimization. A novel method called 'multi-objective root growth algorithm' (MORGA) for constrained multi-objective optimization is proposed based on the root growth model. A self-adaptive strategy is adopted to tie this model closer to plant root growth behaviors in nature, as well as improve the robustness of MORGA. Simulation experiments of MORGA on a set of benchmark test functions are compared with other nature inspired techniques for multi-objective optimization which includes nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate MORGA approach is a powerful search and optimization technique for constrained multi-objective optimization. © 2013 IEEE.
Language英语
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Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/12402
Collection信息服务与智能控制技术研究室
Recommended Citation
GB/T 7714
Zhang H,Zhu YL,Zhang DY. Self-adaptive root growth model for constrained multi-objective optimization[C]. Washington, United States:IEEE Computer Society,2013:2360-2367.
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