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) algorithm. However, there are still some disadvantages in the FOA, such as low convergence precision, easily trapped in a local optimum value at the later evolution stage. This paper presents an improved FOA based on the cell communication mechanism (CFOA), by considering the information of the global worst, mean, and best solutions into the search strategy to improve the exploitation. The results from a set of numerical benchmark functions show that the CFOA outperforms the FOA and the PSO in most of the experiments. Further, the CFOA is applied to optimize the controller for preoxidation furnaces in carbon fibers production. Simulation results demonstrate the effectiveness of the CFOA.
Highlights
There are a lot of bioinspired optimization algorithms that are applied in practical engineering successfully, such as genetic algorithm (GA) inspired by the genetic science and natural selection [1, 2], particle swarm optimization (PSO) algorithm inspired by the simulation of the behavior of birds in nature [3,4,5], artificial bee colony (ABC) algorithm inspired by the intelligent behavior of honeybee swarm [6], artificial immune algorithm (AIA) inspired by the biological immune system [7,8,9,10], and ant colony optimization (ACO) algorithm inspired by the foraging behavior of the real ants [11]
In order to overcome the lack of search strategy control mechanism in the basic fly optimization algorithm (FOA) leading to poor quality of solution, in this paper, inspired by the cell communication mechanism, we propose an improved FOA based on the cell communication mechanism (CFOA) by incorporating the information of the global worst, mean, and best solutions
The main contributions of this paper include the following aspects: (1) We propose the CFOA, which pushes forward the development of intelligent computing; (2) The CFOA is used to optimize the controller for preoxidation furnaces in carbon fibers production
Summary
There are a lot of bioinspired optimization algorithms that are applied in practical engineering successfully, such as genetic algorithm (GA) inspired by the genetic science and natural selection [1, 2], particle swarm optimization (PSO) algorithm inspired by the simulation of the behavior of birds in nature [3,4,5], artificial bee colony (ABC) algorithm inspired by the intelligent behavior of honeybee swarm [6], artificial immune algorithm (AIA) inspired by the biological immune system [7,8,9,10], and ant colony optimization (ACO) algorithm inspired by the foraging behavior of the real ants [11]. We apply the CFOA to optimize the controller for preoxidation furnaces in carbon fibers production. Hou et al [28] investigated the influence of ozone on chemical reactions during the preoxidation process of the PAN as a carbon fiber precursor. Most of the previous work was focused on analyzing the properties of carbon fibers by means of physical or chemical instruments, and little was concerning the control effect of the temperature on preoxidation reactions. The main contributions of this paper include the following aspects: (1) We propose the CFOA, which pushes forward the development of intelligent computing; (2) The CFOA is used to optimize the controller for preoxidation furnaces in carbon fibers production. Preoxidation furnace for producing the PAN-based carbon fibers and compares the results with the former methods.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
