计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于差分进化与NSGA-Ⅱ的多目标优化算法

陶文华,刘洪涛   

  1. (辽宁石油化工大学 信息与控制工程学院,辽宁 抚顺 113001)
  • 收稿日期:2015-11-16 出版日期:2016-11-15 发布日期:2016-11-15
  • 作者简介:陶文华(1972—),女,教授,主研方向为生产过程建模与先进控制、智能算法;刘洪涛,硕士研究生。
  • 基金项目:
    国家自然科学基金(61473140,61203021)。

Multi-objective Optimization Algorithm Based on Differential Evolution and NSGA-Ⅱ

TAO Wenhua,LIU Hongtao   

  1. (School of Information and Control Engineering,Liaoning Shihua University,Fushun,Liaoning 113001,China)
  • Received:2015-11-16 Online:2016-11-15 Published:2016-11-15

摘要: 为避免多目标优化过程中子目标相互冲突,提高Pareto最优解的质量,提出一种基于差分进化(DE)和第二代非支配遗传算法(NSGA-Ⅱ)的混合算法。采用带有自适应参数的DE算法对初始种群进行变异和交叉操作,以提高种群的多样性。应用新种群标记策略对DE的初始种群和 测试种群进行支配得到新种群,并标记其中每个个体,使DE能够处理多目标问题。将新种群作为NSGA-Ⅱ的初始种群,通过NSGA-Ⅱ产生下一代种群,进一步提升Pareto最优解的质量。使用4个基准多目标函数进行测试,结果表明,与NSGA-Ⅱ和SADE算法相比,该算法的收敛速度更 快,Pareto最优解集空间分布更均匀。

关键词: 多目标优化, 混合算法, 自适应参数, Pareto最优解, 收敛速度, 空间分布

Abstract: In order to avoid conflict between the sub objectives and improve the quality of Pareto optimal solution for the multi-objective optimization problem,a Hybrid algorithm based on Differential Evolution(DE) and Non-dominated Sorting Genetic Algorithm Ⅱ(HDE-NSGA-Ⅱ) is proposed.First of all,DE algorithm with self-adaptive parameters is used for mutation and crossover operations of initial population,so that population diversity is improved.Secondly,a new population marking strategy is adopted to dominate the initial population and testing population of DE to obtain a new population whose individuals are marked.The strategy enables DE to dispose multi-objective problem.Finally,the new population,as the initial population of NSGA-Ⅱ,will generate the next generation population by NSGA-Ⅱ.The quality of Pareto optimal solutions will be further promoted by this step.Four multi-objective benchmark functions are tested by HDE-NSGA- Ⅱ,NSGA-Ⅱ and SADE.Experimental results show that the convergence rate of the proposed algorithm is faster and the spatial distributions of Pareto optimal solution set is more uniform than the other two algorithms.

Key words: multi-objective optimization, hybrid algorithm, self-adaptative parameter, Pareto optimal solution, convergence rate, spatial distribution

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