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Particle swarm optimization (PSO) has shown its good performance in many optimization problems. However, PSO could often easily fall into local minima because the particles could quickly converge to a position by the attraction of the best particles. Under this circumstance, all the particles could hardly be improved. This paper presents a hybrid PSO (AMPSO) to solve this problem by applying a novel...
In evolutionary computation, Gaussian and Cauchy mutations are two popular mutation techniques and completely discussed in this study. It is known that Cauchy mutation has the better ability of escaping local optima, and Gaussian mutation is excellent in local convergence. However, there still exist some vague characteristics in the abilities of local escape/convergence for these two mutations. Therefore,...
Particle swarm optimization (PSO) shares many similarities with evolutionary algorithms (EAs), while the standard PSO does not use any evolution operators such as crossover and mutation. This paper presents a hybrid PSO algorithm to inherit some excellent characteristics of advanced evolutionary computation techniques. The proposed method employs a novel multi-parent crossover operator and a self-adaptive...
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