DCDG-EA: Dynamic Convergence-Diversity Guided Evolutionary Algorithm for Many-Objective Optimization
首发时间:2018-04-16
Abstract:Maintaining a good balance between convergence and diversity is particularly crucial to the performance of the evolutionary algorithms (EAs). However, traditional multi-objective evolutionary algorithms, which have demonstrated their competitive performance in a variety of practical problems involving two or three objectives, face significant challenges in many-objective optimization problems (MaOPs). This paper proposes a dynamic convergence-diversity guided evolutionary algorithm (DCDG-EA) for MaOPs by employing decomposition technique. The objective space of MaOPs is divided into $K$ subspaces by a set of uniformly distributed reference vectors. Each subspace has its own subpopulation and evolves in parallel with the other subspaces. In DCDG-EA, the balance between convergence and diversity is achieved through convergence-diversity based operator selection (CDOS) strategy and convergence-diversity based individual selection (CDIS) strategy. In CDOS, each operator in a set of operators is assigned a selection probability which is related to its convergence and diversity reward. On the basis of selection probability, an appropriate operator is selected to generate offspring. Furthermore, CDIS greatly overcomes the inefficiency of Pareto dominance. It updates each subpopulation by using two independent distance measures that respectively represent convergence and control diversity. The experimental results on DTLZ benchmark problems with up to 15 objectives show that our algorithm is highly competitive in comparison with the selected four state-of-the-art evolutionary algorithms in terms of convergence and diversity.
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动态收敛性与多样性指导的高维多目标进化算法
摘要:保持收敛性和多样性之间的良好平衡对进化算法(EAs)的性能尤为重要。然而,传统的多目标优化算法已经在包含两个或三个目标的各种实际问题中展现出它们优势,但在高维多目标优化问题(MaOPs)中面临着巨大的挑战。本文提出了一个利用分解技术的动态收敛性和多样性指导的高维多目标优化进化算法(DCDG-EA)。 MaOPs 的目标空间被一组均匀分布的参考向量分成$K$个子空间。每个子空间都有其自己的子种群,并与其他子空间并行进化。在DCDG-EA中,收敛和多样性之间的平衡通过基于收敛性和多样性的算子选择(CDOS)策略和基于收敛性和多样性的个体选择(CDIS)策略来实现。在CDOS中,一组算子中的每个算子都被分配了一个选择概率,该选择概率与其收敛性和多样性回报有关。根据选择概率,选择适当的算子来生成后代。此外,CDIS极大地克服了Pareto占优的低效性。它通过两个距离度量分别代表收敛和控制多样性,据此来更新每个子种群。在多达15个目标的DTLZ基准测试问题上的实验结果表明,我们的算法与所选择的四种主流的多目标优化算法相比,在收敛性和多样性方面都具有很大的优势。
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