English
A hybrid simplex search and particle swarm optimization for unconstrained optimization
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资源说明：This paper proposes the hybrid NM-PSO algorithm based on the Nelder–Mead (NM) simplex search method and particle
swarm optimization (PSO) for unconstrained optimization. NM-PSO is very easy to implement in practice since it
does not require gradient computation. The modification of both the Nelder–Mead simplex search method and particle
swarm optimization intends to produce faster and more accurate convergence. The main purpose of the paper is to demonstrate
how the standard particle swarm optimizers can be improved by incorporating a hybridization strategy. In a suite
of 20 test function problems taken from the literature, computational results via a comprehensive experimental study, preceded
by the investigation of parameter selection, show that the hybrid NM-PSO approach outperforms other three relevant
search techniques (i.e., the original NM simplex search method, the original PSO and the guaranteed convergence
particle swarm optimization (GCPSO)) in terms of solution quality and convergence rate. In a later part of the comparative
experiment, the NM-PSO algorithm is compared to various most up-to-date cooperative PSO (CPSO) procedures
appearing in the literature. The comparison report still largely favors the NM-PSO algorithm in the performance of accuracy,
robustness and function evaluation. As evidenced by the overall assessment based on two kinds of computational
experience, the new algorithm has demonstrated to be extremely effective and efficient at locating best-practice optimal
solutions for unconstrained optimization.
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