DocumentCode :
53533
Title :
Multi-Objective Optimization by Using Evolutionary Algorithms: The p -Optimality Criteria
Author :
Carreno Jara, Emiliano
Author_Institution :
Univ. Nac. de San Luis, San Luis, Argentina
Volume :
18
Issue :
2
fYear :
2014
fDate :
Apr-14
Firstpage :
167
Lastpage :
179
Abstract :
In this paper, a novel general class of optimality criteria is defined and proposed to solve multi-objective optimization problems by using evolutionary algorithms. These criteria, named p-optimality criteria, allow us to value (assess) the relative importance of those solutions with outstanding performance in very few objectives and poor performance in all others, regarding those solutions with an equilibrium (balance) among all the objectives. The optimality criteria avoid interrelating the relative values of the different objectives, respecting the integrity of each one in a rational way. As an example, a simple multi-objective approach based on the p-optimality criteria and genetic algorithms is designed, where solutions used to generate new solutions are selected according to the proposed optimality criteria. It is implemented and applied on several benchmark test problems, and its performance is compared to that of the nondominated sort genetic algorithm-II method, in order to analyze the contribution and potential of these new optimality criteria.
Keywords :
genetic algorithms; evolutionary algorithms; genetic algorithms; multiobjective optimization; p-optimality criteria; Algorithm design and analysis; Evolutionary computation; Pareto optimization; Sociology; Vectors; Evolutionary algorithms; genetic algorithms; multi-objective optimization; optimality criteria; optimality criterion;
fLanguage :
English
Journal_Title :
Evolutionary Computation, IEEE Transactions on
Publisher :
ieee
ISSN :
1089-778X
Type :
jour
DOI :
10.1109/TEVC.2013.2243455
Filename :
6461089
Link To Document :
بازگشت