Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Multilevel minimum cross entropy threshold selection based on honey bee mating optimization
CISST'09 Proceedings of the 3rd WSEAS international conference on Circuits, systems, signal and telecommunications
Handbook of Swarm Intelligence: Concepts, Principles and Applications
Handbook of Swarm Intelligence: Concepts, Principles and Applications
Median-based image thresholding
Image and Vision Computing
Practical construction of k-nearest neighbor graphs in metric spaces
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Adaptive inertia weight particle swarm optimization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
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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.