论文标题
粒子群优化和差异进化的模块化杂交
A Modular Hybridization of Particle Swarm Optimization and Differential Evolution
论文作者
论文摘要
在群智能中，粒子群优化（PSO）和差异演化（DE）已成功地应用于许多优化任务中，并且已经引入了大量新型算法运算符或组件，以提高经验性能。在本文中，我们首先建议通过模块化每种算法并将其变体作为相应模块的不同选项结合起来，将PSO或DE的变体组合起来。然后，考虑到PSO和DE的内部运作之间的相似性，我们通过分别与PSO和DE的变异运算符创建两个人群，并从这两个人群中选择个体来杂交算法。所得的新型杂交（称为PSODE）涵盖了两侧的最新变体，更重要的是，通过其中的模块的不同实例，会产生大量未见的群算法。 详细介绍，我们考虑了16个源自现有的PSO和DE算法的不同变异操作员，这些变体与4个不同的选择操作员相结合，允许杂交框架生成800个新算法。所得的一组混合算法以及可以与所考虑的运算符一起生成的组合的30个PSO和DE算法，对跨多个功能组和尺寸的24个问题进行了测试。
In swarm intelligence, Particle Swarm Optimization (PSO) and Differential Evolution (DE) have been successfully applied in many optimization tasks, and a large number of variants, where novel algorithm operators or components are implemented, has been introduced to boost the empirical performance. In this paper, we first propose to combine the variants of PSO or DE by modularizing each algorithm and incorporating the variants thereof as different options of the corresponding modules. Then, considering the similarity between the inner workings of PSO and DE, we hybridize the algorithms by creating two populations with variation operators of PSO and DE respectively, and selecting individuals from those two populations. The resulting novel hybridization, called PSODE, encompasses most up-to-date variants from both sides, and more importantly gives rise to an enormous number of unseen swarm algorithms via different instantiations of the modules therein. In detail, we consider 16 different variation operators originating from existing PSO- and DE algorithms, which, combined with 4 different selection operators, allow the hybridization framework to generate 800 novel algorithms. The resulting set of hybrid algorithms, along with the combined 30 PSO- and DE algorithms that can be generated with the considered operators, is tested on the 24 problems from the well-known COCO/BBOB benchmark suite, across multiple function groups and dimensionalities.
