Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. Harmony search (HS) is a derivative-free real parameter optimization algorithm. It draws inspiration from the musical improvisation process of searching for a perfect state of harmony. This paper proposes a novel approach to accelerate the HS algorithm. The proposed opposition-based HS of the present work employs opposition-based learning for harmony memory initialization and also for the generation jumping. In the present work, opposite numbers have been utilized to improve the convergence rate of the HS. The potential of the proposed algorithm, presented in this paper, is assessed by means of an extensive comparative study of the solution obtained for four standard combined economic and emission dispatch problems of power systems. The results obtained confirm the potential and effectiveness of the proposed algorithm compared to some other algorithms surfaced in the recent state-of-the art literatures. Both the near-optimality of the solution and the convergence speed of the proposed algorithm are found to be promising.

► This paper proposes a novel opposition-based harmony search (OHS) algorithm.
► The OHS algorithm employs opposition-based learning for harmony memory initialization and also for the generation jumping.
► Opposite numbers have been utilized to improve the convergence rate of the harmony search.
► The OHS is employed for four standard combined economic and emission dispatch problems of power systems.