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Genetic Algorithm and Particle Swarm Optimization: Analysis and Remedial Suggestions

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 5)

Abstract

A comprehensive comparison of two powerful evolutionary computational algorithms: Genetic Algorithm and Particle Swarm Optimization have been presented in this paper. Both the algorithms have the global exploration capability; is being applied to the difficult optimization problems. The operators of each algorithm greatly contribute to the success have been reviewed, focusing on how they affect the searching in the problem space. The rationale of conducting this study is: to bring additional insights into how these algorithms work, and suggest remedies, if incorporated, improves the performance.

Keywords

Bio-inspired algorithm Crossover Mutation Genetic algorithm Particle swarm optimization Nature inspired algorithm 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringAmity UniversityNoidaIndia

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