A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems

Gravitational search algorithm (GSA) is based on the law of gravity and interaction between masses. In GSA, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. This paper proposes a novel algorithm to accelerate the performance of the GSA. The proposed opposition-based GSA (OGSA) of the present work employs opposition-based learning for population initialization and also for generation jumping. In the present work, opposite numbers have been utilized to improve the convergence rate of the GSA. For the experimental verification of the proposed algorithm, a comprehensive set of 23 complex benchmark test functions including a wide range of dimensions is employed. Additionally, four standard power systems problems of combined economic and emission dispatch (CEED) are solved by the OGSA to establish the optimizing efficacy of the proposed algorithm. 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 promising.

► OGSA is proposed and tested on a suite of 23 benchmark test functions. ► The OGSA-based optimal results on 23 benchmark test functions are presented. ► The results are compared to other techniques like real coded GA, PSO, and GSA. ► The performance of the OGSA in solving different CEED problems is tested. ► The results are compared to those published in the recent state-of-the art literatures.