Optimal design of squirrel cage induction motor using multi-objective Salp Swarm Algorithm

The design of three-phase induction motors is a challenge in electrical engineering. Therefore, new design techniques are continuously provided. Since the design of the induction motors is carried out for different purposes, it is difficult to find a method that can addresses all the targets. Nowadays, the normal methods used to solve multi-objective problems are the optimization strategies. In this paper, the meta-heuristic optimization method has been used to design the squirrel cage induction motors with the aim of increasing efficiency and reducing costs. In this way, modeling of induction motor is done accurately and to solve this optimization problem, a new method of Multi-objective optimization of the Salp Swarm Algorithm (MSSA) has been provided. This algorithm is inspired by the social behavior of Salps. The proposed method is applied to the data of an induction motor of 2.5 KW with IE2 efficiency. The results of single-objective and multi-objective optimization exhibit that the design of an induction motor with two objective functions increases the efficiency and reduces the cost. Additionally, the MSSA algorithm is compared with the methods of genetic algorithms for indirect sorting (NSGA-II) and multi-object particle optimization algorithms (MOPSO). The MSSA algorithm has a convergence rate and a set of optimal response sets which demonstrates a good performance in optimal design of the induction motor to increase efficiency and reduce its costs.