Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

S. Aote, M. Raghuwanshi
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引用次数: 1

Abstract

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.
基于粒子群算法、遗传算法、差分进化及其相似性的数学优化
为了解决优化问题,在不同的领域提供了不同的方法。进化计算(EC)是解决这些问题的方法之一。粒子群优化(PSO)、遗传算法(GA)和差分进化(DE)是目前最常用的电子商务技术。这些技术的工作结构不同,但内部的工作结构是相同的。不同的任务有不同的名称和公式,但最终都是一样的。在这里,我们试图找出这些技术之间的相似之处,并给出每个步骤的工作结构。所有步骤都提供了适当的示例和用MATLAB编写的代码,以便更好地理解。本文首先介绍了粒子群算法、遗传算法和遗传算法的优化和求解问题,最后对它们进行了简要的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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