An improved evolution fruit fly optimization algorithm and its application

Fruit fly optimization algorithm (FOA) is a kind of swarm intelligence optimization algorithm, which has been widely applied in science and engineering fields. The aim of this study is to design an improved FOA, namely evolution FOA (EFOA), which can overcome some shortcomings of basic FOA, including difficulty in local optimization, slow convergence speed, and lack of robustness. EFOA applies a few new strategies which adaptively control the search steps and swarm numbers of the fruit flies. The evolution mechanism used in EFOA can preserve dominant swarms and remove inferior swarms. Comprehensive comparison experiments are performed to compare EFOA with other swarm intelligence algorithms through 14 benchmark functions and a constrained engineering problem. Experimental results suggest that EFOA performs well both in global search ability and in robustness, and it can improve convergence speed.

[1]  K. V. Arya,et al.  Opposition based lévy flight artificial bee colony , 2012, Memetic Computing.

[2]  Lei Wu,et al.  An improved fruit fly optimization algorithm based on selecting evolutionary direction intelligently , 2016, Int. J. Comput. Intell. Syst..

[3]  E. Sandgren,et al.  Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .

[4]  Ataollah Ebrahimzadeh,et al.  Improved Fruit-Fly Optimization Algorithm and Its Applications in Antenna Arrays Synthesis , 2018, IEEE Transactions on Antennas and Propagation.

[5]  Yoel Tenne,et al.  An Optimization Algorithm Employing Multiple Metamodels and Optimizers , 2013, Int. J. Autom. Comput..

[6]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[7]  Carlos A. Coello Coello,et al.  Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer , 2008, Informatica.

[8]  Xiaohui Hu,et al.  Engineering optimization with particle swarm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[9]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[10]  Youtian Tao,et al.  The Improvement of Fruit Fly Optimization Algorithm , 2012 .

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

[12]  Leandro dos Santos Coelho,et al.  Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems , 2010, Expert Syst. Appl..

[13]  Rui Liu,et al.  An effective and efficient fruit fly optimization algorithm with level probability policy and its applications , 2016, Knowl. Based Syst..

[14]  Yafei Huang,et al.  An effective hybrid cuckoo search algorithm for constrained global optimization , 2014, Neural Computing and Applications.

[15]  Kalyanmoy Deb,et al.  GeneAS: A Robust Optimal Design Technique for Mechanical Component Design , 1997 .

[16]  Liang Gao,et al.  An improved fruit fly optimization algorithm for continuous function optimization problems , 2014, Knowl. Based Syst..

[17]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[18]  Shan Liu,et al.  An improved fruit fly optimization algorithm and its application to joint replenishment problems , 2015, Expert Syst. Appl..

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

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

[21]  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..

[22]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

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

[24]  Qian He,et al.  On a novel multi-swarm fruit fly optimization algorithm and its application , 2014, Appl. Math. Comput..

[25]  S. M. Abd-Elazim,et al.  PI controller design for MPPT of photovoltaic system supplying SRM via BAT search algorithm , 2017, Neural Computing and Applications.

[26]  Pan Duan,et al.  An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory , 2016, Kybernetes.

[27]  Dervis Karaboga,et al.  Artificial bee colony algorithm for large-scale problems and engineering design optimization , 2012, J. Intell. Manuf..

[28]  Siamak Talatahari,et al.  An improved ant colony optimization for constrained engineering design problems , 2010 .

[29]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[30]  Patrice Joyeux,et al.  Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism , 2013, Eng. Appl. Artif. Intell..

[31]  C. A. Coello Coello,et al.  Multiple trial vectors in differential evolution for engineering design , 2007 .

[32]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[33]  Dan Shan,et al.  LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems , 2013 .

[34]  Chun Zhang,et al.  Mixed-discrete nonlinear optimization with simulated annealing , 1993 .

[35]  Milan Tuba,et al.  An ant colony optimization algorithm for partitioning graphs with supply and demand , 2015, Appl. Soft Comput..

[36]  E. S. Ali,et al.  Speed control of DC series motor supplied by photovoltaic system via firefly algorithm , 2014, Neural Computing and Applications.

[37]  A Kaveh,et al.  ENGINEERING OPTIMIZATION WITH HYBRID PARTICLE SWARM AND ANT COLONY OPTIMIZATION , 2009 .

[38]  Yongsheng Ding,et al.  An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism , 2015 .

[39]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[40]  Jiuyuan Huo,et al.  Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model , 2019, Neural Computing and Applications.

[41]  Tingsong Du,et al.  DSLC-FOA : Improved fruit fly optimization algorithm for application to structural engineering design optimization problems , 2018 .

[42]  S. N. Kramer,et al.  An Augmented Lagrange Multiplier Based Method for Mixed Integer Discrete Continuous Optimization and Its Applications to Mechanical Design , 1994 .

[43]  S. M. Abd-Elazim,et al.  Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm , 2016, Neural Computing and Applications.

[44]  Huaijin Zhang,et al.  Prediction of pK(a) values of neutral and alkaline drugs with particle swarm optimization algorithm and artificial neural network , 2019, Neural Computing and Applications.

[45]  Carlos A. Coello Coello,et al.  An empirical study about the usefulness of evolution strategies to solve constrained optimization problems , 2008, Int. J. Gen. Syst..

[46]  Xiangyu Wang,et al.  A novel differential search algorithm and applications for structure design , 2015, Appl. Math. Comput..

[47]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[48]  Wen-Tsao Pan,et al.  Using modified fruit fly optimisation algorithm to perform the function test and case studies , 2013, Connect. Sci..

[49]  Shengyao Wang,et al.  A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem , 2013, Knowl. Based Syst..

[50]  Yi Zhu,et al.  An Improved Fruit Fly Optimization Algorithm and Its Application , 2015 .

[51]  Jian Zhang,et al.  Deep Extreme Learning Machine and Its Application in EEG Classification , 2015 .