Dynamic multi-species coevolution large-scale optimize based on the fuzzy clustering and trust region
Department of Mechanical Engineering, Xi’an University of Science and Technology, No.58 Yatan Road, Xi’an 710054, China
Based on the above-mentioned studies, this article put the modified Fuzzy Clustering method into the particle swarm optimization, which solved the curse of dimensionality existing in the conventional algorithms. The large-scale parameter optimization method applied the modified Fuzzy C-Means method to clustering the large-scale dimension under the coevolution frame and achieved the valid dimension grouping. Later on, the dynamic neighbourhood topology multi-species particle algorithms divided the whole species into packets and constructed the subspecies sharing neighbourhood information, which improve the searching efficiency. The bring-in trust region could have the self-adapt adjustment for the particle optimization range, accelerate the optimizing speed, and decrease the iterations in the dead space. We use 20 standard large-scale testing functions for simulation. Compared with the top-ranked tournament algorithm, the mentioned algorithm achieved a better optimizing result in most functions, which surely laded the foundation of the large-scale neural network parameter optimization and the application in the control system.