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The particle swarm optimization (PSO) is a stochastic optimization algorithm imitating animal behavior, which shows a bad performance when optimizing the multimodal and high dimensional functions. Each particle uses own experience and otherpsilas to make decision, it is easy to trap into premature convergence, but group decision making with all the individuals to make decisions uses various experiences...
Particle swarm optimization (PSO) has shown its good performance on numerical function problems. However, on some multimodal functions the PSO easily suffers from premature convergence because of the rapid decline in velocity. In this paper, a hybrid PSO algorithm, called HPSO, is proposed, which employs a modified velocity model to guarantee a non-zero velocity. In addition, a Cauchy mutation operator...
Particle Swarm Optimisation (PSO) has been very successful in finding, if not the optimum, at least very good positions in many diverse and complex problem spaces. However, as the number of dimensions of this problem space increases, the performance can fall away. This paper considers the role that the separable nature of the traditional PSO equations may have in this and introduces the ideal of a...
In recent years, Particle Swarm Optimization (PSO) has been used in data mining, feature extraction and other optimization based applications. Time to time, a number of researchers have suggested modifications to the basic PSO. Although this optimization technique finds good solutions much faster than the traditional and evolutionary algorithms, they suffer from a major drawback of premature convergence...
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