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A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are(More)
The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed.(More)
The performance of particle swarm optimization using an inertia weight is compared with performance using a constriction factor. Five benchmark functions are used for the comparison. It is concluded that the best approach is to use the constriction factor while limiting the maximum velocity Vmax to the dynamic range of the variable Xmax on each dimension.(More)
1. viii Line 13, change " iteration s " to " iterations " 2. xv Line 17, change " Figure 99.1 " to " Figure 1 " 3. xv Lines 20-21, delete the sentence " GP-related resources are provided in an appendix. " 4. xvi Figure 99.1, change " 99.1 " to " 1 " 5. xvii After the 2 nd bulleted item beginning " Part III, " insert the following, additional bullet, Y Four(More)
This paper presents a Particle Swarm Optimization (PSO) algorithm for constrained nonlinear optimization problems. In PSO, the potential solutions, called particles, are "flown" through the problem space by learning from the current optimal particle and its own memory. In this paper, preserving feasibility strategy is employed to deal with constraints. PSO(More)
Gene clustering, the process of grouping related genes in the same cluster, is at the foundation of different genomic studies that aim at analyzing the function of genes. Microarray technologies have made it possible to measure gene expression levels for thousand of genes simultaneously. For knowledge to be extracted from the datasets generated by these(More)