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Metaheuristic, Open Multiprocessing (OpenMP), Teaching-Learning-Based Optimization (TLBO), Unconstrained Function Optimization, Multicore
The problem with metaheuristics, including Teaching-Learning-Based Optimization (TLBO) is that, it increases in the number of dimensions (D) leads to increase in the search space which increases the amount of time required to find an optimal solution (delay in convergence). Nowadays, multi-core systems are getting cheaper and more common. To solve the above large dimensionality problem, implementation of TLBO on a multi-core system using OpenMP API’s with C/C++ is proposed in this paper. The functionality of a multi-core system is exploited using OpenMP which maximizes the CPU (Central Processing Unit) utilization, which was not considered till now. The experimental results are compared with a sequential implementation of Simple TLBO (STLBO) with Parallel implementation of STLBO i.e. OpenMP TLBO, on the basis of total run time for standard benchmark problems by studying the effect of parameters, viz. population size, number of cores, dimension size, and problems of differing complexities. Linear speedup is observed by proposed OpenMP TLBO implementation over STLBO.
A. J. Umbarkar, N. M. Rothe, A.S. Sathe, "OpenMP Teaching-Learning Based Optimization Algorithm over Multi-Core System", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.7, pp.57-65, 2015. DOI:10.5815/ijisa.2015.07.08
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