IEEE

A Computationally Efficient Multi-Objective Design Optimization of SRM using K-Means Clustering and Artificial Neural Networks

Abstract: This paper presents a novel methodology for realizing a computationally efficient multi-objective design optimization (CE-MODO) of switched reluctance motors (SRMs). Existing MODO approaches ...
IEEE

Deep Reinforcement Learning-Assisted Automated Operator Portfolio for Constrained Multi-Objective Optimization

Abstract: Constrained multi-objective optimization problems (CMOPs) are of great significance in the context of practical applications, ranging from scientific to engineering domains. Most existing ...