An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism

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.

[1]  Hua Han,et al.  An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking , 2011, Comput. Math. Appl..

[2]  Yongsheng Ding,et al.  Immunological mechanism inspired iterative learning control , 2014, Neurocomputing.

[3]  Wei-Yuan Lin,et al.  Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service , 2013 .

[4]  D. Bertrand,et al.  Feature selection by a genetic algorithm. Application to seed discrimination by artificial vision , 1998 .

[5]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[6]  Huiru Zhao,et al.  Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm , 2012 .

[7]  Lei Gao,et al.  Comprehensive Learning Particle Swarm Optimizer for Constrained Mixed-Variable Optimization Problems , 2010 .

[8]  Zhi-Hua Hu,et al.  A container multimodal transportation scheduling approach based on immune affinity model for emergency relief , 2011, Expert Syst. Appl..

[9]  Jieying Liang,et al.  Effect of the oxygen-induced modification of polyacrylonitrile fibers during thermal-oxidative stabilization on the radial microcrystalline structure of the resulting carbon fibers , 2013 .

[10]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[11]  Jim Xiang,et al.  Mechanisms of cellular communication through intercellular protein transfer , 2010, Journal of cellular and molecular medicine.

[12]  Chengguo Wang,et al.  Structural Evolution of Polyacrylonitrile Precursor Fibers during Preoxidation and Carbonization , 2007 .

[13]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[14]  Bo Liu,et al.  Improved particle swarm optimization combined with chaos , 2005 .

[15]  Ching-Ter Chang,et al.  A study on business performance with the combination of Z-score and FOAGRNN hybrid model , 2012 .

[16]  Yongsheng Ding,et al.  A synergetic immune clonal selection algorithm based multi-objective optimization method for carbon fiber drawing process , 2013, Fibers and Polymers.

[17]  Wang Sheng,et al.  Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle , 2013 .

[18]  Yu Wang,et al.  Self-adaptive learning based particle swarm optimization , 2011, Inf. Sci..

[19]  Chengguo Wang,et al.  Combined Effect of Processing Parameters on Thermal Stabilization of PAN Fibers , 2006 .

[20]  Hua Han,et al.  An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink , 2014, Int. J. Syst. Sci..

[21]  Yongping Hou,et al.  Influence of ozone on chemical reactions during the stabilization of polyacrylonitrile as a carbon fiber precursor , 2008 .

[22]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[23]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[24]  N. Garc'ia-Pedrajas,et al.  CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features , 2005, J. Artif. Intell. Res..

[25]  Ahmad Fauzi Ismail,et al.  A review of heat treatment on polyacrylonitrile fiber , 2007 .

[26]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[27]  S. Chand,et al.  Review Carbon fibers for composites , 2000 .

[28]  S. Ozbek,et al.  Strain-induced density changes in PAN-based carbon fibres , 2000 .

[29]  Bo Zhu,et al.  Effects of Preoxidation and Carbonization Technologies on Tensile Strength of PAN-Based Carbon Fiber , 2008 .

[30]  Liu Jie,et al.  Evolution of structure and properties of PAN precursors during their conversion to carbon fibers , 2003 .

[31]  Siba K. Udgata,et al.  Integrated Learning Particle Swarm Optimizer for global optimization , 2011, Appl. Soft Comput..

[32]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[33]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[34]  A. Ismail,et al.  Post spinning and pyrolysis processes of polyacrylonitrile (PAN)-based carbon fiber and activated carbon fiber: A review , 2012 .