GPGPU based teaching learning based optimization and Artificial bee colony algorithm for unconstrained optimization problems

Teaching learning based optimization (TLBO) and Artificial bee colony (ABC) algorithm is population based modern method of optimization, used to solve diverse complex engineering and real time applications. To obtain best solution for the complex problem it requires more time and results in performance degradation. To improve the performance of the population based algorithm, they are either parallelized or implemented on General Purpose Graphic Processing Unit (GPGPU). In this paper, the GPGPU based implementation of TLBO and ABC algorithm is discussed to solve unconstrained benchmark problems. The performance of both the approaches is compared based on standard deviation, standard error mean and time. It is observed that both the approaches gives good results but time taken by TLBO algorithm is more as compared to ABC algorithm.