A new hybrid genetic algorithm for optimizing the single and multivariate objective functions
Jaya Shankar Tumuluru, Richard McCulloch · 2015 ASABE International Meeting · 2015
Abstract. A new hybrid genetic algorithm was developed which combines a stochastic evolutionary algorithm with a deterministic adaptive step steepest descent hill climbing algorithm in order to optimize complex multivariate problems. By combining both algorithms computational resources are conserved and the solution converges rapidly as compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive step steepest descent algorithm the solution moves toward the lowest surrounding point. Step sizes start big and get progressively smaller, increasing computational efficiency. The genetic algorithm ensures the solution samples the entire global search space, thus a global minimum is found. The steepest descent method fine tunes the solution by moving it to the nearest local minimum. The code was developed, including a graphical user interface, in MATLAB. Additional features such as bounding the input, weighting the objective functions individually, and constraining the output are also built into the interface. The algorithm developed was used to optimize the response surface models which use process variables (feedstock moisture content, die speed, and preheating temperature) to predict pellet properties (pellet moisture content, unit, bulk and tapped density, durability, and specific energy consumption). The solution found by the hybrid algorithm was validated experimentally. Execution times were decreased by approximately 40%, based on 1,0000 trials with each method, using the new hybrid algorithm as compared to using a genetic algorithm alone with the same parameters, both developed at INL. Performance of the hybrid algorithm versus the commercial Matlab genetic algorithm is investigated. Results show that the hybrid genetic algorithm converged to the global maximum for bulk density in one iteration, whereas the commercial genetic algorithm took twenty nine iterations to converge.