Chaotic grasshopper optimization algorithm for global optimization

Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.

[1]  A. Rezaee Jordehi.  A chaotic artificial immune system optimisation algorithm for solving global continuous optimisation problems , 2014, Neural Computing and Applications.

[2]  A. Rezaee Jordehi.  A chaotic-based big bang–big crunch algorithm for solving global optimisation problems , 2014 .

[3]  Satvir Singh,et al.  Mutated firefly algorithm , 2014, 2014 International Conference on Parallel, Distributed and Grid Computing.

[4]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[5]  Meikang Qiu,et al.  The Effects of Using Chaotic Map on Improving the Performance of Multiobjective Evolutionary Algorithms , 2014 .

[6]  Anis Naanaa,et al.  Fast chaotic optimization algorithm based on spatiotemporal maps for global optimization , 2015, Appl. Math. Comput..

[7]  David B. Skalak,et al.  Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms , 1994, ICML.

[8]  A. Rezaee Jordehi,et al.  An efficient chaotic water cycle algorithm for optimization tasks , 2015, Neural Computing and Applications.

[9]  Gaige Wang,et al.  A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization , 2013, J. Appl. Math..

[10]  S. Arora,et al.  Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm , 2017, Arabian Journal for Science and Engineering.

[11]  Gehad Ismael,et al.  Feature selection via a novel chaotic crow search algorithm , 2017 .

[12]  Satvir Singh,et al.  The Firefly Optimization Algorithm: Convergence Analysis and Parameter Selection , 2013 .

[13]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[14]  Xiaoming Chang,et al.  A chaotic digital secure communication based on a modified gravitational search algorithm filter , 2012, Inf. Sci..

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[17]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[18]  Di He,et al.  Chaotic characteristics of a one-dimensional iterative map with infinite collapses , 2001 .

[19]  Jason Brownlee,et al.  Clever Algorithms: Nature-Inspired Programming Recipes , 2012 .

[20]  Satvir Singh,et al.  An improved butterfly optimization algorithm with chaos , 2017, J. Intell. Fuzzy Syst..

[21]  Mohammad Saleh Tavazoei,et al.  Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms , 2007, Appl. Math. Comput..

[22]  Xin-She Yang,et al.  Chaos-enhanced accelerated particle swarm optimization , 2013, Commun. Nonlinear Sci. Numer. Simul..

[23]  Joni-Kristian Kämäräinen,et al.  Differential Evolution Training Algorithm for Feed-Forward Neural Networks , 2003, Neural Processing Letters.

[24]  Hossein Nezamabadi-pour,et al.  BGSA: binary gravitational search algorithm , 2010, Natural Computing.

[25]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[26]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[27]  Rainer Storn,et al.  Differential evolution design of an IIR-filter , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[28]  Sankalap Arora,et al.  Chaotic grey wolf optimization algorithm for constrained optimization problems , 2018, J. Comput. Des. Eng..

[29]  A. Gandomi,et al.  Imperialist competitive algorithm combined with chaos for global optimization , 2012 .

[30]  A. Rezaee Jordehi,et al.  Chaotic bat swarm optimisation (CBSO) , 2015, Appl. Soft Comput..

[31]  Ali R. Yildiz,et al.  An effective hybrid immune-hill climbing optimization approach for solving design and manufacturing optimization problems in industry , 2009 .

[32]  Ehl Emile Aarts,et al.  Simulated annealing and Boltzmann machines , 2003 .

[33]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[34]  Xin-She Yang.  Introduction to Mathematical Optimization: From Linear Programming to Metaheuristics , 2008 .

[35]  Andrew Lewis,et al.  Biogeography-based optimisation with chaos , 2014, Neural Computing and Applications.

[36]  Xiaoming Chang,et al.  An intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transforms , 2013, Inf. Sci..

[37]  G. Cheng,et al.  On the efficiency of chaos optimization algorithms for global optimization , 2007 .

[38]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[39]  Xin-She Yang,et al.  Firefly algorithm with chaos , 2013, Commun. Nonlinear Sci. Numer. Simul..

[40]  Wei-Mou Zheng,et al.  KNEADING PLANE OF THE CIRCLE MAP , 1994 .

[41]  Xin-She Yang,et al.  Metaheuristics in water, geotechnical and transport engineering , 2012 .

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

[43]  Xin-She Yang,et al.  Firefly Algorithm, Lévy Flights and Global Optimization , 2010, SGAI Conf..

[44]  Li-Yeh Chuang,et al.  Chaotic catfish particle swarm optimization for solving global numerical optimization problems , 2011, Appl. Math. Comput..

[45]  Satvir Singh,et al.  Butterfly algorithm with Lèvy Flights for global optimization , 2015, 2015 International Conference on Signal Processing, Computing and Control (ISPCC).

[46]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[47]  L. Coelho,et al.  Combining of chaotic differential evolution and quadratic programming for economic dispatch optimization with valve-point effect , 2006, IEEE Transactions on Power Systems.

[48]  Muhammad Khurram Khan,et al.  An effective memetic differential evolution algorithm based on chaotic local search , 2011, Inf. Sci..