Chaotic based differential evolution algorithm for optimization of baker's yeast drying process

Abstract

Chaotic based Differential Evolution (CDE) algorithm is presented to determine the optimal control variables for the optimization of Baker's Yeast drying process. The chaotic system is proposed to determine the initial population, to select the trial individuals from the population in the mutation operation instead of the random number generator. The random values produced by the random number generator are likely to be similar or same values with each other. In this study, four different chaotic systems, such as Lorenz attractor, Rössler attractor, Chua circuit and Mackey-Glass equation, are solved by Runge-Kutta method to produce the random values of the initial individuals. To demonstrate the performance of the CDE algorithms, ten optimization problems are taken from the literature. Furthermore, the performances of the proposed CDE algorithms are compared with the classic Differential Evolution (DE) algorithm, Particle Swarm Optimization (PSO) algorithm, Artificial Bee Colony (ABC) algorithm, Simulated Annealing (SA) algorithm, Touring Ant Colony Optimization (TACO) algorithm in terms of the mean best solution, the number of function evaluations (NFE) and CPU-time metrics. At the same time, the proposed CDE algorithms are implemented for numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. For the optimization of baker's yeast drying process, there are four significant parameters, such as product quality, drying total time, energy cost of air and the final moisture content. The proposed CDE algorithms and classic DE algorithm are applied for the same optimization problem that is taken from a baker's yeast producer in Turkey. The experimental results prove that the proposed CDE algorithms are able to provide very competitive results.