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JACIII Vol.14 No.1 pp. 110-118
doi: 10.20965/jaciii.2010.p0110
(2010)

Paper:

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Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

Hrvoje Markovic, Fangyan Dong, and Kaoru Hirota

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:
July 29, 2009
Accepted:
August 31, 2009
Published:
January 20, 2010
Creative Commons License
Keywords:
approximation, genetic algorithm, metaheuristic, optimization, particle swarm optimization
Abstract
A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.
Cite this article as:
H. Markovic, F. Dong, and K. Hirota, “Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.1, pp. 110-118, 2010.
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