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Particle Swarm Optimization Based on Dynamic Island Model

机译：基于动态岛模型的粒子群算法

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摘要

Particle Swarm Optimization (PSO) algorithm is a metaheuristic that has been used for solving optimization problems. In this method, many modifications have been carried out in order to improve the search performance. Furthermore, island models is a structured population mechanism used to preserve the diversity and thus to improve the population performance. The aim of this paper is to integrate dynamic island models with PSO algorithm to improve its convergence and its diversity properties where the new method is referred to as island PSO. The dynamic regulation of migrations aims to distribute the particles in the search space. The experimental results, using a set of benchmark functions show that the island model context is crucial to the PSO performance and the comparative study shows the efficiency of the integration of dynamic island models.
机译：粒子群优化（PSO）算法是一种元启发式算法，已用于解决优化问题。在该方法中，为了提高搜索性能，已经进行了许多修改。此外，岛屿模型是一种结构化的人口机制，用于维护多样性并因此改善人口绩效。本文的目的是将动态孤岛模型与PSO算法集成在一起，以改善其收敛性和多样性，其中新方法称为孤岛PSO。迁移的动态调节旨在将粒子分布在搜索空间中。使用一组基准函数的实验结果表明，孤岛模型上下文对于PSO性能至关重要，而比较研究则表明了动态孤岛模型集成的效率。

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