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This study attempts to propose a hybrid particle swarm optimization (PSO) based on grey relational analysis and the mutation strategy. In the proposed hybrid PSO, the determination of the algorithm parameters (the inertia weight and the acceleration coefficients) for a particle is depended upon the grey relational grade of that particle. The algorithm parameters are varying over the generations. Also...
In this paper we propose the design of an autonomous robotic vacuum cleaner by using three-dimensional vector coordinates to guide its path. Our design is fully adapted to planning an effective mode for completing the cleaning task in an unknown environment. In addition we propose a dynamic return path for recharging, including the relative coordinates of the record starting point and the current...
The bacterial-foraging-based swarm intelligent algorithm called bacterial foraging particle swarm optimization (BFPSO) is proposed to design the vector quantization (VQ)-based fuzzy image compression systems. It can improve the compressed image quality in processing the large amount of image-patterns. BFPSO combines the inspired behaviors of bacterial foraging mode and the PSO learning scheme to simultaneously...
This paper proposes a differential evolution with local information for TSK-type neuro-fuzzy system optimization. The differential evolution with local information consider neighborhood between each individual to keep the diversity of population. An adaptive parameter tuning based on 1/5th rule is used to trade off between local search and global search. For structure learning algorithm, the on-line...
This paper presents a new framework to calculate the continuous brain blood vessel skeleton curve of binary images and gray-scale images. The main idea of the method is: the minimum cost path between any two skeleton points is a skeleton line. Algorithm using two different intermediate functions, one is Euclidean distance field and another is deformed gradient vector flow, gained two different energy...
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