A Particle Swarm Optimization-Nelder Mead Hybrid Algorithm for Balanced Exploration and Exploitation in Multidimensional Search Space

between exploration and exploitation is the key to faster convergence. This paper proposes a method to add an exploitative component to particle swarm optimization, a recently proposed biologically inspired metaphor. This is accomplished by applying the well-known Nelder Mead simplex algorithm to the population of solutions at the end of each iteration. It has been shown that this proposed hybridization method speeds up the significantly the rate of convergence for several well known benchmark problems as well as for the problem of fitting a gene model with observable data.

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