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@INPROCEEDINGS{Kennedy98matchingalgorithms,
    author = {James Kennedy and William M. Spears},
    title = {Matching Algorithms to Problems: An Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimodal Problem Generator},
    booktitle = {In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC},
    year = {1998},
    pages = {78--83}
}

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Abstract:

A multimodal problem generator was used to test three versions of genetic algorithm and the binary particle swarm algorithm in a factorial time-series experiment. Specific strengths and weaknesses of the various algorithms were identified. 1. Introduction This paper will compare the performance of the binary particle swarm and several varieties of genetic algorithm on sets of problems produced by a multimodality problem generator. The study will be constructed in the form of a repeated-measures factorial experiment, reporting results from multivariate analysis of variance. Research questions involve effects of various aspects of problems on performance of the particle swarm and genetic algorithms with mutation or crossover, or both. One difficulty with empirical comparisons of search algorithms is that results may not generalize beyond the test problems used. For instance, a new algorithm may be carefully tuned so that it outperforms some existing algorithms on a few problems. Unfort...

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