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A Review of Methodologies of TLBO Algorithm to Test the Performance of Benchmark Functions
Abstract
Like other nature-inspired algorithms, Teaching-Learning-Based Optimization (TLBO) is a population-based method that uses a population of solutions to proceed to the global solution. The motivation to develop a nature-based algorithm is its capacity to solve different optimization problems effectively and efficiently. It was assumed that the behavior of nature is always optimum in its performance. The Performance of algorithm on unconstrained and constrained benchmark functions was tested by running these algorithms sequentially. The main motivation of using parallel computing is to improve the performance of these algorithms. GPGPU is applicable where data parallelism and independency is possible, a good implementation on a General Purpose Graphics Processing Unit (GPGPU) can achieve more than 100 times better speedup over sequential execution. Compute Unified Device Architecture (CUDA) implementation proves better in the evolutionary algorithm regarding of speed up as well as convergence time.
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