Non-convex Dynamic Economic Dispatch Using an Innovative Hybrid Algorithm

An innovative hybrid algorithm (ihPSODE) is proposed in this paper to solve non-convex dynamic economic dispatch (DED) problem. It integrated with a novel particle swarm optimization (nPSO) and differential evolution (nDE). A new inertia weight, gradually varying acceleration coefficient and position update equation introduced in nPSO, to avoid stagnation. And in nDE a new mutation strategy and crossover rate are presented, to avoid premature convergence. Additionally, in ihPSODE after calculation identify best half member and discard rest of members from the population. In current population applied nPSO, to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applied nDE. Hence, proposed ihPSODE has higher probability of avoiding local optima and it is likely to find global optima more quickly. Firstly, proposed ihPSODE as well as its anticipated integrating component nPSO and nDE are testified on IEEE CEC2006 constrained benchmark functions. Then performance of these proposed algorithms are evaluated with non-convex DED problem with valve-point effects using five different test systems (5-unit with losses, 10-unit without losses, 10-unit with losses, 15-unit with losses and 30-unit without losses). Several numerical and graphical experiments have been done to verify performances of proposed algorithms. Additionally, statistical and comparative analysis confirms superiority of proposed algorithms over many state-of-the-art algorithms.