Multi-objective single agent stochastic search in non-dominated sorting genetic algorithm

A hybrid multi-objective optimization algorithm based on genetic algorithm and stochastic local search is developed and evaluated. The single agent stochastic search local optimization algorithm has been modified in order to be suitable for multi-objective optimization where the local optimization...

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Bibliographic Details
Main Authors: Algirdas Lančinskas, Pilar Martinez Ortigosa, Julius Žilinskas
Format: Article
Language:English
Published: Vilnius University Press 2013-07-01
Series:Nonlinear Analysis
Subjects:
Online Access:http://www.journals.vu.lt/nonlinear-analysis/article/view/14011
Description
Summary:A hybrid multi-objective optimization algorithm based on genetic algorithm and stochastic local search is developed and evaluated. The single agent stochastic search local optimization algorithm has been modified in order to be suitable for multi-objective optimization where the local optimization is performed towards non-dominated points. The presented algorithm has been experimentally investigated by solving a set of well known test problems, and evaluated according to several metrics for measuring the performance of algorithms for multi-objective optimization. Results of the experimental investigation are presented and discussed.
ISSN:1392-5113
2335-8963