Multi-objective hybrid PSO using µ-fuzzy dominance

This paper describes a PSO-Nelder Mead Simplex hybrid multi-objective optimization algorithm based on a numerical metric called µ -fuzzy dominance. Within each iteration of this approach, in addition to the position and velocity update of each particle using PSO, the k-means algorithm is applied to divide the population into smaller sized clusters. The Nelder-Mead simplex algorithm is used separately within each cluster for added local search. The proposed algorithm is shown to perform better than MOPSO on several test problems as well as for the optimization of a genetic model for flowering time control in Arabidopsis. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks.

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