A novel Chemo-Inspired genetic algorithm for economic load dispatch with valve point loading effect

Abstract

The inherent drawback of the popular evolutionary algorithm as such genetic algorithm (GA) and also bio-inspired algorithm bacterial foraging optimization (BFO) lies in the fact that they very often suffer from the problem of being trapped into the local optimum. In recent past, various popular hybridized techniques of GA and BFO came out through different thought processes of the researches and have been implemented in the algorithm. Inspired by those ideas, in this paper, a novel approach has been opted for the hybridization of GA with BFO by incorporating chemotactic step as a local search operator at the end of the entire GA cycle; thus, the algorithm is named as chemo-inspired genetic algorithm (CGA) and it has also been extended for constrained optimization, and further it is named as CGAC, where “C” stands for being capable of handling constraints. At the outset, experiments are made to validate the superiority of CGAC over another hybrid method, namely LX-PM-C and H-LX-PM-C taking a set of 8 typical benchmark problems of various difficulty labels from the literature. Later, it has been applied to real-life application problem, where economic load dispatch (ELD) problem having 40 generators has been considered with valve point loading effect.