Genetic Algorithm, Particle Swarm Optimization and Harmony Search: A quick comparison

Sonia Sharma, Hari Mohan Pandey

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)

11 Citations (Scopus)
76 Downloads (Pure)

Abstract

There exists several complex optimization problems, are difficult to solve using simple conventional or mathematical approach. Many scientific applications have a search space exponentially proportional to the problem dimensions, cannot be solved employing exhaustive search methods. Therefore, there is considerable interest in metaheuristic methods attempt to discover near optimal solution within the acceptable time. This paper presents a comprehensive study and comparison of three: Genetic Algorithm, Particle Swarm Optimization and Harmony Search, global optimization algorithms. The comparative analysis has been reported in an organized manner for quick review. The underlying motivation is to identify possibility to develop a new hybrid algorithm to solve real world problems.

Original languageEnglish
Title of host publicationProceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016
EditorsAbhay Bansal, Abhishek Singhal
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages40-44
Number of pages5
ISBN (Electronic)9781467382021
ISBN (Print)9781467382021
DOIs
Publication statusPublished - 8 Jul 2016
Event6th International Conference on Cloud System and Big Data Engineering, Confluence 2016 - Uttar Pradesh, Noida, India
Duration: 14 Jan 201615 Jan 2016

Publication series

NameProceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016

Conference

Conference6th International Conference on Cloud System and Big Data Engineering, Confluence 2016
CountryIndia
CityUttar Pradesh, Noida
Period14/01/1615/01/16

Keywords

  • algorithm
  • artificial intelligence
  • genetic algorithm
  • harmony search
  • meta-heuristic algorithms
  • optimization
  • particle swarm optimization

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