Abstract
Multi-objective design is very common in electrical engineering, and the development of new multi-objective optimizers becomes recently a topical issue in computational electromagnetics because of the unavailability of a universal multi-objective optimizer. In this regard, an improved physical programming (PP) method is presented to solve multi-objective electromagnetic inverse problems. The improvements include: (1) the multi-pseudo preference concept is introduced to overcome the deficiency of existing PP method in finding only one Pareto solution in a simulation and enhance the usage rate of sampling points; (2) a novel dynamically guiding mechanism is proposed to improve the computational efficiency and to provide the evolutionary direction; (3) some new rules for algorithm parameter updating are proposed to realize the two ultimate goals of an idea multi-objective optimizer of finding as many as possible Pareto optimal solutions and having them uniformly distributed on the Pareto Front. The results of typical mathematic test functions and a practical optimization design demonstrate the validity and advantages of the proposed method.