Coevolutionary particle swarm optimization using AIS and its application in multiparameter estimation of PMSM

In this paper, a coevolutionary particle-swarm-optimization (PSO) algorithm associating with the artificial immune principle is proposed. In the proposed algorithm, the whole population is divided into two kinds of subpopulations consisting of one elite subpopulation and several normal subpopulations. The best individual of each normal subpopulation will be memorized into the elite subpopulation during the evolution process. A hybrid method, which creates new individuals by using three different operators, is presented to ensure the diversity of all the subpopulations. Furthermore, a simple adaptive wavelet learning operator is utilized for accelerating the convergence speed of the pbest particles. The improved immune-clonal-selection operator is employed for optimizing the elite subpopulation, while the migration scheme is employed for the information exchange between elite subpopulation and normal subpopulations. The performance of the proposed algorithm is verified by testing on a suite of standard benchmark functions, which shows faster convergence and global search ability. Its performance is further evaluated by its application to multiparameter estimation of permanent-magnet synchronous machines, which shows that its performance significantly outperforms existing PSOs. The proposed algorithm can estimate the machine dq-axis inductances, stator winding resistance, and rotor flux linkage simultaneously. © 2013 IEEE

GPU-accelerated parallel coevolutionary algorithm for parameters identification and temperature monitoring in permanent magnet synchronous machines

A hierarchical fast parallel co-evolutionary immune particle swarm optimization (PSO) algorithm, accelerated by graphics processing unit (GPU) technique (G-PCIPSO), is proposed for multiparameter identification and temperature monitoring of permanent magnet synchronous machines (PMSM). It is composed of two levels and is developed based on compute unified device architecture (CUDA). In G-PCIPSO, the antibodies (Abs) of higher level memory are selected from the lower level swarms and improved by immune clonal-selection operator. The search information exchanges between swarms using the memory-based sharing mechanism. Moreover, an immune vaccine-enhanced operator is proposed to lead the Pbests particles to unexplored areas. Optimized parallel implementations of G-PCIPSO algorithm is developed on GPU using CUDA, which significantly speeds up the search process. Finally, the proposed algorithm is applied to multiple parameters identification and temperature monitoring of PMSM. It can track parameter variation and achieve temperature monitoring online effectively. Compared with a CPU-based serial execution, the computational efficiency is greatly enhanced by GPU-accelerated parallel computing technique