A novel particle swarm-based method for nonlinear function optimization

This paper proposes a hybrid Particle Swarm Optimization (PSO) method, which is based on the fusion of the PSO, Clonal Selection Algorithm (CSA), and Mind Evolutionary Computation (MEC). The clone function borrowed from the CSA and MEC-characterized similartaxis and dissimilation operations are embedded in the original PSO algorithm. Simulations of nonlinear function optimization are made to compare this hybrid PSO with the regular PSO method. It has been demonstrated that our hybrid optimization algorithm can achieve a better convergence performance, and provide diverse solutions to the multi-model optimization problems.

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