Chemo-Inspired GA for Non-convex Economic Load Dispatch

Powered by the chemotactic step of bacterial foraging optimization (BFO), a new hybrid genetic algorithm is proposed in this paper for solving non-linear constrained optimization problems. In the recent past, researchers attempted to hybridize the GA and BFO for improving the quality of the solution. However, this hybridization unnecessarily increases the computational burden as some of the mechanisms/steps are seem to be technically repeated. It is due to the fact that the internal mechanism of selection in GA and the reproduction in BFO; and the elitism in GA and elimination-dispersal step in BFO is almost similar. Undoubtedly, chemotactic step plays the vital role in the better performance of BFO. Therefore in this present study, only the chemotactic step of BFO is considered for hybridization with GA. Further, it is designed to tackle constrained optimization problems and is named as chemo-inspired genetic algorithm for constrained optimization (CGAC). Here in this paper, it is applied to solve economic load dispatch (ELD) problem, and finally, the result comparison has been done with other state-of-the-art algorithms to validate the superiority of CGAC.