A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models |
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Authors: | Mohammed El-Abd Mohamed S Kamel |
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Institution: | 1.ECE Department,University of Waterloo,Waterloo,Canada |
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Abstract: | Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking,
and that has been successfully used in many applications as an optimization tool. Estimation of distribution algorithms (EDAs)
are a class of evolutionary algorithms which perform a two-step process: building a probabilistic model from which good solutions
may be generated and then using this model to generate new individuals. Two distinct research trends that emerged in the past
few years are the hybridization of PSO and EDA algorithms and the parallelization of EDAs to exploit the idea of exchanging
the probabilistic model information. In this work, we propose the use of a cooperative PSO/EDA algorithm based on the exchange
of heterogeneous probabilistic models. The model is heterogeneous because the cooperating PSO/EDA algorithms use different
methods to sample the search space. Three different exchange approaches are tested and compared in this work. In all these
approaches, the amount of information exchanged is adapted based on the performance of the two cooperating swarms. The performance
of the cooperative model is compared to the existing state-of-the-art PSO cooperative approaches using a suite of well-known
benchmark optimization functions. |
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Keywords: | |
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