A Survey to Nature Inspired Soft Computing

A Survey to Nature Inspired Soft Computing

Deepak Kumar (Amity University, Noida, India), Sushil Kumar (Amity University, Noida, India), Rohit Bansal (Management Department, Rajiv Gandhi Institute of Petroleum Technology, Noida, India) and Parveen Singla (ECE Department, Chandigarh Engineering College, Landran, India)
Copyright: © 2017 |Pages: 22
DOI: 10.4018/IJISMD.2017040107

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.
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Ant Colony Optimization

Ant Colony Optimization (ACO) is as a meta-heuristic algorithm that works on the basis of working of a combination of positive feedback, distributed computation, and greediness to discover an ideal solution for optimization issues. As its name implies, this algorithm is based on movement pattern of the ants. This algorithm is a derivative of Swarm intelligence (SI) (Colorni et al., 1992). He studied the complex social behaviour of ants and concluded that it could be very useful in solving complex optimization problems.

The principal concept behind this algorithm is based on the unique ability of ants to find the shortest route. Ants go here and there in search of food and they establish their communication with the other ants by laying down pheromones along their trails. The term pheromone refers to a chemical which is laid down by the ants so that other ants can smell it and follow the same path. This dropping of the pheromone by many ants forms a trail and thus creates a path. In this way, they will be able to find the path from their home to the source of food and back to their home.

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