Improving particle swarm optimization performance with local search for high-dimensional function optimization

摘要

Particle swarm optimization (PSO) is one recently proposed population-based stochastic optimization technique, and gradient-based descent methods are efficient local optimization techniques that are often used as a necessary ingredient of hybrid algorithms for global optimization problems (GOPs). By examining the properties of the two methods, a two-stage hybrid algorithm for global optimization is proposed. In the present algorithm, the gradient descent technique is used to find a local minimum of the objective function efficiently, and a PSO method with latent parallel search capability is employed to help the algorithm to escape from the previously converged local minima to a better point which is then used as a starting point for the gradient methods to restart a new local search. The above search procedure is applied repeatedly until a global minimum is found (when a global minimum is known in advance) or the maximum number of function evaluations is reached. In addition, a repulsion technique and partially initializing population method are incorporated in the new algorithm to increase its global jumping ability. Simulation results on 15 test problems including five large-scale ones with dimensions up to 1000 demonstrate that the proposed method is more stable and efficient than several other existing methods.View full textDownload full textKeywordsglobal optimization, gradient descent method, particle swarm optimization (PSO), repulsion technique AMS Subject Classification 90C30, 90C52, 90C26, 65K05, 49M37Related var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10556780903034514