Parallel particle swarm optimization with modified stochastic acceleration factors for solving large scale economic dispatch problem

This paper presents an advanced parallelized particle swarm optimization algorithm with modified stochastic acceleration factors (PSO-MSAF) to solve large scale economic dispatch (ED) problems with prohibited operating zones, ramp-rate limits and transmission losses. In this parallel algorithm, the range within which the random cognitive and social learning parameters are to be chosen is bounded. Different values for these boundaries are tried simultaneously using multiple workers in a parallel computing environment with a manger scheduling the tasks to the workers. The results produced by these workers are subsequently compared and the best result is given as the final optimal solution. This algorithm prevents premature convergence and achieves better speed up, especially for large scale ED problems, mitigating the burden of multimodality and heavy computation. To improve the performance of the proposed algorithm, penalty parameter-less constraint handling scheme is employed to handle power balance, prohibited operating zones and ramp-rate limits. The dispatch results obtained by this algorithm satisfy Karush–Kuhn–Tucker (KKT) conditions, conforming optimality. The KKT-based error metric termination criterion is implemented for successful termination of the proposed algorithm. The proposed architecture effectively controls the local search ability thereby leading to better convergence towards the true optimal solution.