NDLI: Survey of multi objective evolutionary algorithms

Content Provider	IEEE Xplore Digital Library
Author	Vachhani, V.L. Dabhi, V.K. Prajapati, H.B.
Copyright Year	2015
Description	Author affiliation: Dept. of Inf. Technol., Dharmsinh Desai Univ., Nadiad, India (Vachhani, V.L.; Dabhi, V.K.; Prajapati, H.B.)
Abstract	Multi-objective optimization aims at simultaneously optimizing two or more objectives of a problem. Multi-objective evolutionary algorithms (MOEAs) are widely accepted and useful for solving real world multi-objective problems. When we have two or more conflicting objectives of a problem then we can apply MOEA. MOEA generates a set of non-dominated solutions at the end of run, which is called Pareto set. The Pareto front contains set of Pareto solutions. Any MOEA aims to improve (i) convergence of population towards true Pareto front and (ii) diversity of solutions belonging to Pareto set. Generally, an external archive is used by MOEAs to maintain a set of non-dominated Pareto set solutions. Sometimes, Pareto set contains more number of solutions than the size of archive. This paper presents survey of various methods used by different MOEAs for reducing the size of Pareto set while maintaining solutions diversity. It presents comparison of these methods along with their advantages and disadvantages. The paper concludes by giving limitation of crowding distance based method in various scenarios.
Starting Page	1
Ending Page	9
File Size	335053
Page Count	9
File Format	PDF
e-ISBN	9781479970759
DOI	10.1109/ICCPCT.2015.7159422
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-03-19
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Couplings Pareto front Sociology Evolutionary computation Diversity Diversity methods Statistics Multi-objective evolutionary algorithm Optimization Sorting Convergence
Content Type	Text
Resource Type	Article

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