An Eigenvector-Enhanced Parallel Adaptive Differential Evolution for Electric Motor Design

Abstract

Differential Evolution (DE) is a well-known metaheuristic designed to solve continuous optimization problems. Its simple structure and straight forward search operators make it suitable for solving a wide range of real world problems. Despite its success, DE performance may be limited when tackling high dimensional complex problems. Therefore, its algorithmic structure can be reconsidered by adaptively controlling its parameters, and incorporating more resilient search operators. In this study, a Q-learning-based strategy is proposed to adapt DE parameters during the search process. Moreover, an eigenvector-based crossover is introduced in order to accelerate the convergence rate when ill-conditioned landscapes are treated. However, to avoid premature convergence, a simple yet efficient switching technique is proposed to choose between the normal and the eigenvector-based crossover. Due to the high computational time that might occur when applying the eigenvector-based crossover, a parallel counterpart of the algorithm has been implemented using graphics processing units (GPUs). The proposed algorithm has been applied to find the optimal mechanical structure of a recent electric motor. Its performance has been also validated by testing the proposal on CEC 2011 test suite, which contains a set of real world problems. The experimental results reveal the competetive performance of our algorithm compared to recent adaptive DE versions. Besides, the parallel version of the proposal achieved a serious speedup compared with the sequential version while keeping the same results.