An automated hybrid genetic-conjugate gradient algorithm for multimodal optimization problems

The genetic algorithm (GA) have good global search characteristics and local optimizing algorithm (LOA) have good local search characteristics. In the present work, best characteristics of GA and LOA are combined to develop a hybrid genetic algorithm (HGA). A bank of GA's are used to get a good starting solution for a conjugate gradient algorithm. The number of GA banks is selected using an automated procedure based on Fibonacci numbers. This automated hybrid genetic algorithm (AHGA) is used for solving general multimodal optimization problems while assuring global optimality to a significant degree. The designed algorithm is also tested against a variety of standard test functions. Besides assuring global optimality to a significant extent AHGA is also found to be an efficient algorithm requiring only one tuning error parameter saving considerable time on the part of the user. The method also addresses the problem of selecting a good starting design for gradient based algorithm. Further in the few cases where the algorithm does not converge to a global minima, a local minima is assured because of the use of the gradient based local search in the final stage of the algorithm. Further, the algorithm assures one final solution to the optimization problem and addresses the problem of providing a deterministic output which inhibits the use of GA in engineering optimization software and engineering applications.