A Hybrid Optimized Algorithm Based on Improved Simplex Method and Particle Swarm Optimization

The experimental results show that this new algorithm not only improves the global optimization performance, but also quickens the convergence speed and obtains robust results with good quality, which indicates this new algorithms is an effective approach for solving global optimization problems.

Abstract

Aiming at the problem that the particle swarm optimization is difficult to deal with local convergence and premature problem, a hybrid computational algorithm based on an improved simplex method and particle swarm optimization has been presented in this paper. In the given hybrid algorithm the improved simplex method which has expansion function and contraction function is embedded in the particle swarm optimization as an operator. Using this improved simplex method with certain probability, simplex searching for the optimization is implemented to elitist particles that passed through the particle swarm optimization one time, which can induce the evolution of the swarm rapidly. The experimental results show that this new algorithm not only improves the global optimization performance, but also quickens the convergence speed and obtains robust results with good quality, which indicates this new algorithm is an effective approach for solving global optimization problems.