An improved quantum particle swarm optimizer for electromagnetic design problem

The expansion of global optimum methods for electromagnetic design optimization has been successful in the last few years. However, there is no any universal algorithm to be equally successful for all engineering inverse problems. In this regard, inspired from the classical particle swarm optimization (PSO) method and quantum mechanics, this paper presents an improved quantum particle swarm optimizer (MQPSO) by using a tournament selection strategy. Also, a new index, called the torment best (tbest), is incorporated into the QPSO to further enrich its performance. In addition, a new parameter updating strategy is proposed to tradeoff between the exploration and exploitation searches. The feasibility and merit of the proposed approach are verified by the numerical results on mathematic test functions and an electromagnetic inverse problem, namely the TEAM workshop problem 22.