Implementation of hybrid pattern search–genetic algorithm into optimizing axial-flux permanent magnet coreless generator (AFPMG)

Complex real-world problems can be solved by heuristic optimization efficiently. Improved hybrid optimization method using pattern search (PS) algorithm and genetic algorithm (GA) in MATLAB is presented in this paper, and the optimization is based on the popular multi-objective sizing equation. This hybrid model utilizes concepts from GA and invents new-generation chromosomes not only through mutation and crossover operation but also by mechanisms of PS. In the design procedure, hybrid optimization model with some predefined constraints for the objective function has been taken into consideration which includes the physical limitations and performance characteristics. The dimensions of the machine are optimized with multiple adjustments to the number of magnet pole, the number of winding turns and air-gap distance to gain the highest power density within desired dimensional constraints. Moreover, the electromagnetic field and electromagnetic characteristics of the chosen generator are subject to finite element analysis. A finalized low-power AFPM generator is fabricated, examined and testified to produce desired output. It has been observed that the experiment result agreed with the simulation result.