Abstract
Particle swarm optimization (PSO) is one of the important evolutionary algorithms. However, the traditional PSO suffers from the premature convergence problem. In view of this, a new PSO, named mutation PSO (MPSO), is proposed in this paper. The proposed MPSO not only makes use of a mutation operator to update particles/individuals, which was originally designed for genetic algorithm (GA). But also a new weighted update rule is proposed for MPSO to produce the new swarm. Then we use the proposed MPSO to train multilayer perceptron (MLP) with two tasks: curve fitting and classification. In particular, the performance investigation is concentrated on scene classification. For a comparison purpose, MLPs trained using the error backpropagation (BP), traditional PSO and GA are also investigated. The advantages and disadvantages of these algorithms are also analyzed. Experimental results show that the proposed MPSO outperforms than other algorithms for the training of an MLP.
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