A fast hybrid algorithm for global optimization

An algorithm, consisting of gradient descent technique and particle swarm optimization (PSO) method for global optimization is proposed. The gradient descent technique is used to find a local minimum of objective function fast and efficiently, and particle swarm optimization method helps minimization sequence to escape from the previously converged local minima to a better point. The search procedure is applied repeatedly till a global minimum of the objective function is found. In addition, a repulsion technique and partially initializing population method are also incorporated in the new algorithm. Global convergence is proven, and test on benchmark problems shows that the proposed method is more effective and reliable than the existed optimization methods.