COMPARATIVE STUDY ON NATURE INSPIRED ALGORITHMS FOR OPTIMIZATION PROBLEM

被引：0

作者：

Luthra, Ishani ^{[1
]}

Chaturvedi, Shubham Krishna ^{[1
]}

Upadhyay, Divya ^{[1
]}

Gupta, Richa ^{[1
]}

机构：

[1] Amity Univ, Dept Comp Sci & Technol, Noida, India

来源：

2017 INTERNATIONAL CONFERENCE OF ELECTRONICS, COMMUNICATION AND AEROSPACE TECHNOLOGY (ICECA), VOL 2 | 2017年

关键词：

Artificial Bee Colony Algorithm (ABC); Ant Colony Optimization Algorithm (ACO); Bat Algorithm (BA); Genetic algorithm (GA); Facial Recognition; Travelling Salesman Problem (TSP); ANT COLONY OPTIMIZATION;

D O I：

暂无

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Nature inspired algorithms are gaining popularity for optimizing complex problems. These algorithms have been classified into 2 general categories, namely Evolutionary and Swarm Intelligence, which have further been divided into a couple of algorithms. This paper presents a comparative study between Bat Algorithm, Genetic algorithm, Artificial Bee Colony Algorithm and Ant Colony Optimization Algorithm. These algorithms are compared on the basis of various factors such as Efficiency, Accuracy, Performance, Reliability and Computation Time. At the end, a table has been created which enables the reader to easily differentiate between them and realise which algorithm outperforms the others.

引用

页码：143 / 147

页数：5

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