Parallel computing strategies for Sanitized Teaching Learning Based Optimization

Abstract

Teaching-Learning-Based Optimization is a metaheuristic technique that imitates the knowledge transfer in a classroom. The competitive performance with other state-of-the-art metaheuristic algorithms, innate simplicity, and the requirement of only two user-defined parameters are the primary reason behind the popularity of this algorithm. However, due to the inherent nature of the Teaching Learning Based Optimization, it is not amenable to parallel computing for the multiple evaluations of the fitness function, thereby limiting its application to computationally intensive problems. This article considers the Teaching Learning Based Optimization algorithm without the duplicate removal step as Sanitized Teaching Learning Based Optimization. In this work, three competitive parallelized strategies of Sanitized Teaching Learning Based Optimization are suggested that can harness the benefits of parallel computing. The efficacies of the proposed strategies are demonstrated on 14 benchmark problems and the CEC’14 real parameter benchmark test suite. Additionally, the impact on the computational time with respect to the computational complexity of the objective function and the number of resources has been studied. It is observed that the proposed strategies can lead up to a 90% reduction in computational time over Sanitized Teaching Learning Based Optimization algorithm.