Adaptive learning factor chaotic master-slave particle swarm optimization algorithm

Based on the standard particle swarm optimization algorithm (SPSO), an improved particle swarm optimization algorithm, adaptive learning factor chaotic master-slave particle swarm optimization algorithm (ACCMSPSO), is put forward, into which the concept of adaptive learning factor and master-slave particle swarm is introduced. In the improved algorithm, the learning factor of each particle is different and changes dynamically according to its own fitness. Once the master particle swarm has evolved some generations, a slave particle swarm will be produced which initial particles are generated from the global optimal particle of the master one in a chaos way. Simulation results show that the improved algorithm can improve the global search capability, convergence speed and robustness, and the performance of the improved algorithm is the best in all the algorithms involved in the experiment.