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Closeness based Hierarchical Particle Swarm Optimizer with Time Varying Acceleration Coefficients


Author : Sambhavi Tiwari and Archana Singh

Pages : 2454-2458
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Abstract

A Particle Swarm Optimization algorithm maintains a swarm of particles, where each particle has position vector and velocity vector which represents the potential solutions of the particles. These vectors are updated from the information of global best (Gbest) and personal best (Pbest) of the swarm. All particles move in the search space to obtain optimal solution. In this paper a new concept is introduced of calculating the velocity of the particles with the help of Euclidian Distance concept. This new concept helps in finding whether the particle is closer to Pbest or Gbest and updates the velocity equation accordingly. By this we aim to improve the performance in terms of the optimal solution within a reasonable number of generations.

Keywords: Pbest; Gbest; optimization; uni-modal; multi-modal

Article published in International Journal of Current Engineering and Technology, Vol.5, No.4 (Aug-2015)

 

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