MTLBO-MS: Modified teaching learning based optimization on multicore system

Teaching-Learning-Based Optimization (TLBO) algorithm is newly developed nature-inspired algorithm for solving large-scale-global optimization problems. The basic TLBO algorithm is modified using the approach of Differential Evolution with Random-Scale Factor (DERSF). This paper presents a Modified Teaching-Learning-Based Optimization algorithm on Multicore System (MTLBO-MS), which is a parallel version of TLBO. Master-worker paradigm is used for MTLBO-MS algorithm in the teacher and learner stage. This proposed algorithm is tested on different unimodal and multimodal unconstrained benchmark functions with diverse characteristics. The proposed algorithm is implemented on multi-core architecture using open multiprocessing (OpenMP). The effectiveness of the MTLBO-MS algorithm is analyzed in terms of statistical value such as best, mean, speedup and efficiency. The experimental outcomes and discussions justify that the proposed MTLBO-MS algorithm has better speedup, efficiency, computational complexity and optimal best or mean value as compared to other evolutionary algorithms.