DNPSO: A Dynamic Niching Particle Swarm Optimizer for multi-modal optimization

A Blind Source Separation Algorithm Based on Dynamic Niching Particle Swarm Optimization

“…3) The best previous position which giving the best fitness value of the ith particle is updated by the following expression, which also updates the best position among the whole particles: In traditional PSO algorithm, each individual particle shares the best position of the whole particle swarm, which leads to the high possibility that the whole particle swarm converge on the optimum found early with slow convergence rate [6]. These limitations will require an efficient and accurate method.…”

“…6) Train the velocity and position of the particles of each sub-swarm according to GCPSO, specifically as the equation follows [6]: ( k best p is the best position of this sub-swarm)…”

“…And if two masters are too close to each other, the particle with the better fitness value is set as the master of the other particle and form a sub-swarm. All of the particles that are not a member of any sub-swarm are set as free particles [6].…”

“…Since the traditional optimization algorithm, including gradient descent algorithm and natural gradient algorithm, are easily trapped into local optimum and have slow convergence rate, researchers proposed Particle Swarm Optimization (PSO) and the its improved algorithm as the optimization algorithm for ICA and have achieved remarkable experimental result [1]. However, the standard PSO algorithm also has the probability that being trapped into local optimum, and its convergence rate turns to be slow at the later time [6]. The Chaos-PSO algorithm proposed by Ding [7] improved the searching efficiency and accuracy of the standard PSO and shows high performance in optimizing low-dimensional functions, however, its accuracy still need to be improved in dealing with high-dimensional functions.…”

“…In DNPSO, the particle swarm is divided into several sub-swarms, which form independent searching spaces dynamically so as to ensure every extreme point being examined. Meanwhile, the free particles are always searching for new optimums, which can help the algorithm avoid being trapped into local optimum [6].…”