Novel KNN-motivation-PSO and its application to image segmentation

Particle Swarm Optimization (PSO), limited by the Exploration-Exploitation balance problem is challenging. Exploring more search space and fast convergence is an effective approach to solve this problem. In this paper, we propose a novel PSO algorithm called K-Nearest-Neighbour Motivation PSO, KNN-M-PSO, which provides a promising solution to this problem. KNN-M-PSO is a cascade of K-Nearest-Neighbour algorithm, a motivation factor and the basic PSO. This algorithm has been tested on standard benchmark functions and a real world image segmentation problem. It is used to find the optimum values of thresholds for an image, based on Tsallis Entropy method. The method gives better results in terms of increased objective values and PSNR values when compared with the basic PSO and other optimization algorithms such as Genetic Algorithm and Bacterial Foraging Algorithm.

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