GPGPU based Dual Population Genetic Algorithm for solving Constrained Optimization Problem

Dual Population Genetic Algorithm is a variant of Genetic Algorithm that provides additional diversity to the main population. It covers the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. But also its additional population introduces large search space that increases time required to find an optimal solution. This large scale search space problem can be easily solved using consumer-level graphics cards. The solution obtained using accelerated DPGA for solving a constrained optimization problem from CEC 2006 is compared with the obtained solution using sequential algorithm. The results show speed up maintaining solution quality. Keywords— High Performance Computing (HPC), CUDA C, Dual Population Genetic Algorithm (DPGA), Constrained Optimization Problems (COPs), Function Optimization