A comprehensive survey: Applications of multi-objective particle swarm optimization (MOPSO) algorithm

Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been...

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Bibliographic Details
Main Authors: Soniya Lalwani, Sorabh Singhal, Rajesh Kumar, Nilama Gupta
Format: Article
Language:English
Published: University of Isfahan 2013-03-01
Series:Transactions on Combinatorics
Subjects:
Online Access:http://www.combinatorics.ir/?_action=showPDF&article=2834&_ob=2534709aaead83732828c7103a34a4a6&fileName=full_text.pdf.
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Summary:Numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of Multi-Objective Optimization (MOO) had arisen several years ago. Due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by Swarm Intelligence (SI) techniques. Particle Swarm Optimization (PSO) has been established in 1995 and became a very mature and most popular domain in SI. Multi-Objective PSO (MOPSO) established in 1999, has become an emerging field for solving MOOs with a large number of extensive literature, software, variants, codes and applications. This paper reviews all the applications of MOPSO in miscellaneous areas followed by the study on MOPSO variants in our next publication. An introduction to the key concepts in MOO is followed by the main body of review containing survey of existing work, organized by application area along with their multiple objectives, variants and further categorized variants.
ISSN:2251-8657
2251-8665