A More Efficient Induction Machine based on Hill Climbing Local Search Optimization

Abstract

A multi-objective search optimization technique is utilized to improve the efficiency of the induction machine design. This technique is referred to as Random restart local search optimization or Hill Climbing based local search optimization (HC aLSO). To create an induction machine with a high efficiency of operation, the preceding technique utilizes repeated explo-rations of the problem space to generate the induction machine data. To build the induction motor, this suggested technique utilizes objective functions from the discrete and continuous hill climbing processes. The new HC-LSO technique is compared to two current algorithms for multi-objective design optimization of induction motors, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Hybrid Genetic Algorithm and Particle Swarm Optimization (HGAPSO). The suggested HC a LSO technique and other existing techniques are compared using MATLAB simulations. As a result, the suggested technique's performance has an effect on induction machine parameters such as rotor current, power factor, and efficiency. 2022 IEEE.