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This paper contributes a novel Particle Swarm Optimization (PSO) method. The particle is updated not only by the best position in history (pbest) and the best position among all the particles in the swarm (gbest), but also using the position that is nearest neighbor of pbest. Additionally, we introduce a modified PSO algorithm based on the fuzzy clustering of particles to communication with the nearest...
A new optimization technique named as sliced particle swarm optimization (SPSO) is proposed. It introduces the slicing of search space into rectangular slices. It gives complete solution in terms of reduction in the computational cost and tracking minutely each sliced search space. It introduces the momentum factor which restricts the particle in a sliced search space. Linearly decreasing inertia...
In this paper, the authors propose a small population based modified parallel particle swarm optimization (SPMPPSO) and its application to reduce computational time for motion estimation in video sequence. In motion estimation, initial search, search space, matching criteria, search parameter and step size are important aspect to predict the position of the current macro block for which motion vector...
In this paper, the authors propose a new evolutionary optimization i.e. synchronous bacterial foraging optimization (SBFO). The SBFO can be used for optimization of multimodal and high dimensional functions. It also enhances computational throughput and global search capability. The convergence of original BFO to the optimum value is very slow and its performance is also heavily affected with increased...
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