Chemo-inspired genetic algorithm for function optimization

Finding global optimal solution for a non-linear optimization problem with high complexity became a challenge for the researchers. Evolutionary optimization process is being treated as an alternate paradigm to solve such problems. In this context, Bacterial Foraging Optimization (BFO) is a novel heuristic algorithm inspired from foraging behavior of Escherichia coli bacterium. At the same time, Genetic Algorithm (GA) has also achieved popularity from the academic and industrial communities to deal with such problems. To improve the solution quality further, the hybridization of GA with BFO (GA-BF) is proven to be much robust in recent past. It is found that while hybridizing GA with BFO, the behavior/mechanism of some of the operators seems to be repeated and it may lead to hamper the solution quality as well as increases the computational time. Hence, instead of taking the whole BFO to hybridize with GA (called GA-BF); only the chemotaxis step is picked from BFO mechanism and hybridized with GA. It is named as chemo-inspired Genetic Algorithm (CGA). The superiority of CGA over GA-BF algorithm is being realized through a set of four typical benchmark problems available in the literature. Later, the faster convergence of CGA is shown graphically. Hence, CGA outperforms GA-BF in terms of solution quality and the computational time. Moreover, two real life problems namely (a) solving system of linear equations and (b) frequency modulation sounds parameter identification problems have been solved, in order to justify the above conclusion.