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A new cluster-structured Particle Swarm Optimization (PSO) with interaction and diversity of parameters is proposed in this paper. A swarm of PSO is divided into some sub-swarms (clusters), and not only interactions between sub-swarms and but also diversity of PSO parameters are added so as to improve the search ability of PSO. The cluster structure and the interaction of the proposed PSO are analyzed...
In this paper, an approach based on particle swarm optimization (PSO) and Lyapunov method to construct neural stabilizing controller is presented. The procedure to learn the value of neural network is formulated as min-max problem. And the problem is solved by the co-evolutionary predator-prey PSO which we newly propose. The PSO is able to generate an optimal set of parameters for neural controller...
Recently, a variety of optimization algorithms has developed as systems get complicated. One of those is called Particle Swarm Optimization (PSO). PSO is an algorithm which takes a cue from nature's bird flock or fish school and is known to have superior ability in search and fast convergence. However, it might be difficult to find global optimal solutions when it comes to complex higher-dimensional...
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