An Optimal Algorithm Based on Kinetic-Molecular Theory with Artificial Memory to Solving Economic Dispatch Problem

Economic dispatch (ED) problem exhibits highly nonlinear characteristics, such as prohibited operating zone, ramp rate limits and non-smooth property. Due to its nonlinear characteristics, it is hard to achieve the expected solution by classical methods. To overcome the challenging difficulty, an improved optimization algorithm based on kinetic-molecular theory (KMTOA) was proposed to solve the ED problem in this article. Memory principle is employed into the improved algorithm. By accepting strengthened or weakened stimulus strength, the memory is divided into four states; instant-term, short-term, long-term and forgotten states to update the memory value iteratively. In this way, more and more elites appear in the long-term memory library. Simultaneously, the improved KMTOA, according to the elite population-based guide on the other population, enhances the search ability and avoids the premature convergence which usually suffered in traditional KMTOA. The designs are able to enhance the performance of KMTOA, which has been demonstrated on 12 benchmark functions. To validate the proposed algorithm, we also use three different systems to demonstrate its efficiency and feasibility in solving the ED problem. The experimental results show that the improved KMTOA can achieve higher quality solutions in ED problems.