一种基于多策略差分进化的分解多目标进化算法
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中国民航大学

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TP39

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国家自然科学基金项目(面上项目:基于问题特征分析的量子混合协同进化算法及其应用研究);国家自然科学基金(地区项目:泛函极限学习机模型与算法及泛化能力研究)


A Novel Decomposition Multi-Objective Evolutionary Algorithm Based on Differential Evolution Model with Multi-Strategy
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Civil Aviation University of China

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    摘要:

    为了提高多目标优化问题非支配解集合的分布性和收敛性,根据不同差分进化策略特点,基于切比雪夫分解机制,本文提出一种基于多策略差分进化的分解多目标进化算法(MOEA/D-WMSDE)。该算法首先采用切比雪夫分解机制,将多目标优化问题转化为一系列单目标优化子问题;然后再引入小波基函数和正态分布实现差分进化算法的参数控制,探究一种基于五种变异策略优势互补的最优变异策略,提出一种基于参数控制和最优变异策略的多策略差分进化(WMSDE)算法;在此基础上,实现一种基于WMSDE的分解多目标进化算法。采用ZDT和DTLZ测试函数验证MOEA/D-WMSDE算法的有效性,实验结果表明,该算法在收敛性和分布性方面获得了较大的改进与提高,能有效求解多目标优化问题,并与其它算法对比分析表明所获得的解集整体质量更优,为多目标问题求解提供新方法。

    Abstract:

    In order to improve the distribution and convergence of the non-dominated solution set of multi-objective optimization problem, according to the characteristics of different differential evolution strategies, a novel decomposition multi-objective evolution algorithm based on Chebyshev decomposition mechanism and differential evolution model with multi-strategy, namely MOEA/D-WMSDE is proposed in this paper. The MOEA/D-WMSDE uses Chebyshev decomposition mechanism to transform multi-objective optimization problem into a series of single objective optimization subproblems. Then the wavelet basis function and normal distribution are used to control parameters. A new optimal mutation strategy based on complementary advantages of five mutation strategies is deeply studied in order to propose a new differential evolution (WMSDE) algorithm with multi-strategy. On this basis, a new MOEA/D-WMSDE algorithm is realized. Finally, ZDT and DTLZ benchmark functions are used to prove the optimization performance of the MOEA/D-WMSDE. The experimental results show that the MOEA/D-WMSDE has greatly improved the convergence and distribution, and can effectively solve the multi-objective optimization problem. Compared with the other algorithms, the overall quality of the obtained solution set is best among these algorithms. This study provides a new method to solve multi-objective optimization problem.

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  • 收稿日期:2020-08-27
  • 最后修改日期:2020-11-23
  • 录用日期:2020-12-03
  • 在线发布日期: 2021-01-04
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