一个利基粒子群优化-津贴和前景

Z. Arfeen, M. Abdullah, M. Shehzad, Saleh Altbawi, Muhammad Ashafaq Khan Jiskani, Muhammad Asif Imran YiRan
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引用次数: 5

摘要

优化是一种寻找最佳候选解的方法,以减小或扩大目标问题的价值。广义上讲,算法可以分为四大类,即基于生物学的算法、基于物理学的算法、基于社会学的算法和基于人类智能的算法。基于群体智能(SI)的算法是优化技术的一个统领家族。本文旨在简要回顾元启发式算法,特别是粒子群优化(PSO)及其姊妹变体。本文涵盖了群体智能粒子群算法的所有重要方面,为实践者和学者提供了深入的洞察学习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Niche Particle Swarm Optimization-Perks and Perspectives
Optimization is a method for searching the best candidate solution to lessen or expand the value of the objective problem. Broadly speaking algorithms can be orgabized into four main classes, i.e. biology-based algorithms, physics-based algorithms, sociology-based algorithms, and human intelligence-based algorithms. Swarm-intelligence (SI) based algorithms appeared as a commanding family of optimization techniques. The paper aims to commence a brief review of meta-heuristic algorithms especially Particle swarm optimization (PSO) and its sister variants in short. The understudy paper covers all important aspects of swarm intelligence PSO with deep insight learning for practitioners and scholars.
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