High Dimensional Problem Based on Elite-Grouped Adaptive Particle Swarm Optimization

Particle swarm optimization is a new globe optimization algorithm based on swarm intelligent search. It is a simple and efficient optimization algorithm. Therefore, this algorithm is widely used in solving the most complex problems. However, particle swarm optimization is easy to fall into local minima, defects and poor precision. As a result, an improved particle swarm optimization algorithm is proposed to deal with multi-modal function optimization in high dimension problems. Elite particles and bad particles are differentiated from the swarm in the initial iteration steps, bad particles are replaced with the same number of middle particles generated by mutating bad particles and elite particles. Therefore, the diversity of particle has been increased. In order to avoid the particles falling into the local optimum, the direction of the particles changes in accordance with a certain probability in the latter part of the iteration. The results of the simulation and comparison show that the improved PSO algorithm named EGAPSO is verified to be feasible and effective.

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