An improved fruit fly optimization algorithm inspired from cell communication mechanism for pre-oxidation process of carbon fiber production

Abstract

Fruit fly optimization algorithm (FOA) invented recently is a new swarm intelligence method based on fruit fly's foraging behaviors, and has been shown to be competitive with existing evolutionary algorithms, such as particle swarm optimization (PSO). However, there are still some disadvantages in FOA, such as, low convergence precision, easily trapped in a local optimum value at the later evolution stage. Inspired by the cell communication mechanism, we propose an improved FOA (CFOA) by incorporating the information of the global worst, mean and best solution into the search strategy to improve the exploitation. The results from a set of numerical benchmark functions show that CFOA outperforms the FOA in most of the experiments. In other words, the performance of the CFOA has a reasonable performance for the testing benchmark functions. Moreover, we apply the CFOA to optimize the controller for pre-oxidation furnaces in carbon fiber production. Simulation results demonstrate the effectiveness of the CFOA.