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基于粗糙集理论与差分进化的混合多目标优化算法 被引量：4

A hybrid algorithm based on rough set theory and differential evolution for multi-objective optimization
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摘要 针对传统算法求解约束多目标优化所得近似解精度不高、分布性能不好的问题，提出一种基于粗糙集理论与差分进化的混合算法．首先利用多目标差分进化生成一个初始的近似Pareto前沿；然后利用粗糙集理论提高Pareto前沿的分布质量．选取一组标准的多目标约束测试问题，采用混合算法与NSGA．II算法进行仿真求解，对比结果表叫，所提出的算法在求解约束多目标优化问题时具有更好的近似解分布和更优越的近似解性能． A hybrid algorithm based on the rough set theory and the differential evolution is proposed for constrained multi- objective optimization. Firstly, a multi-objective version of differential evolution is used to generate an initial approximation of the Pareto front. Then, rough set theory is used to improve the spread and quality of this initial approximation. A set of standard multi-objective constrained test problems are adopted to assess the performance of the proposed approach. The results are compared with those generated by NSGA-II, which indicates that the proposed approach is competitive and better MEOA for constrained multi-objective optimization problem.
作者 郭俊 桂卫华 陈晓方
机构地区 中南大学信息科学与工程学院
出处 《控制与决策》 EI CSCD 北大核心 2013年第5期736-740,共5页 Control and Decision
基金 教育部新世纪优秀人才计划项L]（NCET-08.0576） 教育部博士点基金项目（20100162120019） 湖南省科技计划项目（20l1ck3066）.
关键词 差分进化 粗糙集理论 混合算法 多目标优化 PARETO前沿 differential evolutiom rough set theory hybrid algorithms： multi-objective optimization： Pareto front
分类号 TP273 [自动化与计算机技术—检测技术与自动化装置]
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参考文献20

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同被引文献51

  • 1YOZGEC U,TORKER M, HOCALAR A. On-line evolution- ary optimization of an industrial fed-batch yeast fermentation process [ J ]. ISA Transactions, 2009,48 ( 1 ) :79 -92.
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  • 4LI L D,LI X D,YU X H. Power generation loading optimiza- tion using a multi-objective constraint-handling method via PSO algorithm [ C ]. The IEEE International Conference on Industry Informatics, Korea: IEEE,2008 : 1632-1637.
  • 5DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm. NSGA-II [ J ]. IEEE Trans- actions on Evolutionary Computation,2002,6(2):182-197.
  • 6HE Q ,WANG L. A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization [ J ]. Ap- plied Mathematics and Computation, 2007, 186 (2): 1407-1422.
  • 7LI L D, YU X H, LI X D, et al. A modified PSO algorithm for constrained multi-objective optimization [ C ]. Third In- ternational Conference on Network and System Security, Australia : IEEE, 2009:462-467.
  • 8BIROL G,tdNDEY C ,CINAR A. A modular simulation pack- age for fed-batch fermentation: penicillin production [ J ]. Computers and Chemical Engineering, 2002, 26 ( 11 ) : 1553-1565.
  • 9TSOU C S. Multi-objective inventory planning using MOP- SO and TOPSIS [ J ]. Expert Systems with Applications, 2008,35 ( 1-2 ) : 136-142.
  • 10Mehrabian A R, Lucas C. A novel numerical optimization algorithm in- spired from weed colonization [ J ]. Ecological Informatics,2006,1 (4) : 355 - 366.

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控制与决策
2013年 第5期

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