DocumentCode :
3318155
Title :
Surrogate-based Multi-Objective Particle Swarm Optimization
Author :
Santana-Quintero, Luis V. ; Coello, Carlos A Coello ; Hernandez-Diaz, A.G. ; Velazquez, J.
Author_Institution :
Comput. Sci. Dept., CINVESTAV-IPN, Mexico City
fYear :
2008
fDate :
21-23 Sept. 2008
Firstpage :
1
Lastpage :
8
Abstract :
This paper presents a new algorithm that approximates real function evaluations using supervised learning with a surrogate method called support vector machine (SVM). We perform a comparative study among different leader selection schemes in a multi-objective particle swarm optimizer (MOPSO), in order to determine the most appropriate approach to be adopted for solving the sort of problems of our interest. The resulting hybrid presents a poor spread of solutions, which motivates the introduction of a second phase to our algorithm, in which an approach called rough sets is adopted in order to improve the spread of solutions along the Pareto front. Rough sets are used as a local search engine, which is able to generate solutions in the neighborhood of the nondominated solutions previously generated by the surrogate-based algorithm. The resulting approach is able to generate reasonably good approximations of the Pareto front of problems of up to 30 decision variables with only 2,000 fitness function evaluations. Our results are compared with respect to the NSGA-II, which is a multi-objective evolutionary algorithm representative of the state-of-the-art in the area.
Keywords :
Pareto optimisation; evolutionary computation; learning (artificial intelligence); particle swarm optimisation; rough set theory; support vector machines; NSGA-II; Pareto front; local search engine; multiobjective evolutionary algorithm; multiobjective particle swarm optimization; rough set; supervised learning; support vector machine; surrogate-based algorithm; Computational efficiency; Evolutionary computation; Machine learning algorithms; Pareto optimization; Particle swarm optimization; Rough sets; Search engines; Supervised learning; Support vector machines; USA Councils; Multi-objective optimization; PSO; hybrid algorithms; rough sets; support vector machines; surrogates;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Swarm Intelligence Symposium, 2008. SIS 2008. IEEE
Conference_Location :
St. Louis, MO
Print_ISBN :
978-1-4244-2704-8
Electronic_ISBN :
978-1-4244-2705-5
Type :
conf
DOI :
10.1109/SIS.2008.4668300
Filename :
4668300
Link To Document :
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