Enhanced Flower Pollination Approach Applied to Electromagnetic Optimization

摘要

It is difficult to use the deterministic mathematical tools such as a gradient method to solve global optimization problems. Flower pollination algorithm (FPA) is a new nature-inspired algorithm of the swarm intelligence field to global optimization applications, based on the characteristics of flowering plants. To enhance the performance of the standard FPA, an enhanced FPA (EFPA) approach based on beta probability distribution was proposed in this paper. In order to verify the performance of the proposed EFPA, five benchmark functions are chosen from the literature as the test suit. Furthermore, tests using Loney's solenoid benchmark, a classical problem in the electromagnetics area, are realized to evaluate the effectiveness of the FPA and the proposed EFPA. Simulation results and comparisons with the FPA demonstrated that the performance of the EFPA approach is promising in electromagnetics optimization.