Empirical analysis of Competitive Coevolution Multiobjective Evolutionary Algorithm

The integration between strength Pareto Evolutionary algorithm 2 (SPEA2) and competitive coevolution (CE) concept is presented in this paper, a strategy for solving an optimization problem with three scalable objectives. The Hall of Fame (HOF) competitive fitness function is used to implement the CE...

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Main Authors: Tse Guan Tan, Teo, J., Hui Keng Lau
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Distance measurement
Evolutionary computation
Genetic algorithms
Laboratories
Optimization
Polynomials
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Teo, J.
Hui Keng Lau
description The integration between strength Pareto Evolutionary algorithm 2 (SPEA2) and competitive coevolution (CE) concept is presented in this paper, a strategy for solving an optimization problem with three scalable objectives. The Hall of Fame (HOF) competitive fitness function is used to implement the CE. This proposed algorithm referred to as SPEA2-CE-HOF. The performance between SPEA2-CE-HOF is compared against original SPEA2 in solving problems in the DTLZ suite having three to five objectives. The results showed that the SPEA2-CE-HOF performed better than SPEA2 in most of the DTLZ test problems for the generational distance. However the proposed algorithm performed average for the coverage metric.
doi_str_mv 10.1109/ICIAS.2007.4658439
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subjects Artificial intelligence
Distance measurement
Evolutionary computation
Genetic algorithms
Laboratories
Optimization
Polynomials
title Empirical analysis of Competitive Coevolution Multiobjective Evolutionary Algorithm
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