Binary MOGWO Based On Competition and Teaching for Computationally Complex Engineering Applications

Xia Yuan, Jian Dong, Meng Wang

Abstract: A new efficient optimization method, called binary multi-objective grey wolf optimizer based on competition and teaching (BMOGWO-CT) mechanisms, is proposed for computationally complex engineering applications. The proposed algorithm first divides the population into four parts belonging to three levels through the competition mechanism, thereby reducing the population number during the following procedure of position updating. Then, the teaching mechanism supervises different parts to update their positions according to their priorities within the whole population therefore further reducing the computational cost for solving the problems. To check the effectiveness of the method, the BMOGWO-CT is tested on ten benchmark test functions and compared to other population-based optimization methods, indicating BMOGWO-CT is more effective and efficient. Furthermore, the novel optimization method is extended to a computationally time-consuming engineering problem- multi-objective optimization of antenna topology. This example verifies the effectiveness of our proposed method in engineering applications.