DocumentCode
117891
Title
Non-dominated sorting environmental adaptation method (NS-EAM)
Author
Nigam, Ritu ; Choudhary, Alok ; Mishra, K.K.
Author_Institution
Comput. Sci. & Eng., ABES Eng. Coll., Ghaziabad, India
fYear
2014
fDate
20-21 Feb. 2014
Firstpage
595
Lastpage
600
Abstract
A modified version of NSGA-II named as NSEAM is proposed to solve multi-objective optimization (MOO) problems, which is better in performance as compared to NSGA-II. In this proposed algorithm a new Bio inspired algorithm (EAM) is used instead of genetic algorithm. Benchmark functions have been used to justify the performance of this newly proposed algorithm. Initially this bio inspired algorithm was created for the purpose of solving single objective optimization (SOO) problems and it had already showed promising results as compared to the currently prevailing evolutionary algorithms. So it was thought to extend this EAM algorithm´s application area over MOO problems. With slight change in the present SOO algorithm, it can be applied in the MOO problems. This proposed algorithm is compared with NSGA-II and Result analysis shows that it is more efficient and converges to the true parito-optimal front very rapidly.
Keywords
Pareto optimisation; evolutionary computation; MOO problems; NS-EAM; NSGA-II; Pareto-optimal front; SOO problems; benchmark functions; bio inspired algorithm; evolutionary algorithms; multiobjective optimization problem; nondominated sorting environmental adaptation method; single objective optimization problems; Evolutionary computation; Genetic algorithms; Genetics; Optimization; Signal processing algorithms; Sociology; Statistics; Environmental Adaptation Algorithm (EAM); Evolutionary Algorithms (EA); Genetic Algorithms (GA);
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing and Integrated Networks (SPIN), 2014 International Conference on
Conference_Location
Noida
Print_ISBN
978-1-4799-2865-1
Type
conf
DOI
10.1109/SPIN.2014.6777024
Filename
6777024
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