A Survey to Nature Inspired Soft Computing

Deepak Kumar, Sushil Kumar, Rohit Bansal, Parveen Singla
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引用次数: 8

Abstract

This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.
自然启发的软计算研究综述
本文描述了群体智能(SI)和生物启发技术如何在最新算法的进步中塑造时尚话题。这些算法可以在SI的基础上工作,使用物理、化学和生物框架。作者可以将这些算法命名为基于si的,根据特定算法背后的基本概念,受到生物学,物理学和化学的启发。有几个计算结果非常有效,因此已经成为处理现实世界问题的主流设备。在本文中,进行这项调查的原因是为了展示相当数量的算法的适度完整列表,以促进这些算法的研究。本文详细讨论了蚁群算法、布谷鸟算法、萤火虫算法、粒子群算法和遗传算法。对于蚁群算法,考虑的是一个实时问题,称为旅行推销员问题,而对于其他算法,考虑的是最小球问题,这是众所周知的群体技术的比较。
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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