Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem

The development of computers and control software has contributed to the innovation of electrical networks. This development is necessarily linked to several concerns: energy, economic, environmental, etc. The introduction of the techniques of artificial intelligence software in the control and decision is essential in research and in the development of tomorrow’s networks. This paper deals with multi-criteria optimization metaheuristics. These criteria are moving toward the economic/environmental dispatch that addresses the impact of the cost of production and the emission of toxic gases such as competing objectives. This requires some form of conflict resolution to reach a solution. That is why we need effective optimization algorithms. The firefly algorithm and bat algorithm are two recent metaheuristics inspired by nature. Both methods have been studied and adapted to solve our multi-objective optimization problem within the constraints. At the end of this work, the hybridization of the firefly algorithm and bat algorithm was proposed. The purpose of this hybridization is to combine the advantages of both methods and thus improve their performance. The effectiveness of this new method was demonstrated by applying it on different network tests of 6, 10, and 20 generators; testing with several power demands in accordance with constraints; and considering the variability of active transmission losses.