Hybrid Grey Wolf Optimizer Using Elite Opposition-Based Learning Strategy and Simplex Method

To overcome the poor population diversity and slow convergence rate of grey wolf optimizer (GWO), this paper introduces the elite opposition-based learning strategy and simplex method into GWO, and proposes a hybrid grey optimizer using elite opposition (EOGWO). The diversity of grey wolf population is increased and exploration ability is improved. The experiment results of 13 standard benchmark functions indicate that the proposed algorithm has strong global and local search ability, quick convergence rate and high accuracy. EOGWO is also effective and feasible in both low-dimensional and high-dimensional case. Compared to particle swarm optimization with chaotic search (CLSPSO), gravitational search algorithm (GSA), flower pollination algorithm (FPA), cuckoo search (CS) and bat algorithm (BA), the proposed algorithm shows a better optimization performance and robustness.

[1]  Al-Attar Ali Mohamed,et al.  Grey Wolf Optimization for Multi Input Multi Output System , 2015 .

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

[3]  O. Hasançebi,et al.  A bat-inspired algorithm for structural optimization , 2013 .

[4]  Hui Wang,et al.  Firefly algorithm with neighborhood attraction , 2017, Inf. Sci..

[5]  Yaonan Wang,et al.  Operating Point Optimization of Auxiliary Power Unit Using Adaptive Multi-Objective Differential Evolution Algorithm , 2017, IEEE Transactions on Industrial Electronics.

[6]  P. N. Suganthan,et al.  Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.

[7]  Seyed Mohammad Mirjalili,et al.  Evolutionary population dynamics and grey wolf optimizer , 2015, Neural Computing and Applications.

[8]  Osama Moh'd Alia,et al.  Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm , 2017, Inf. Sci..

[9]  Wei Pan,et al.  Grey wolf optimizer for unmanned combat aerial vehicle path planning , 2016, Adv. Eng. Softw..

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

[11]  Mohammad Nazri Mohd. Jaafar,et al.  Genetic algorithm for optimization of energy systems: Solution uniqueness, accuracy, Pareto convergence and dimension reduction , 2017 .

[12]  Radu-Emil Precup,et al.  Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity , 2017, IEEE Transactions on Industrial Electronics.

[13]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[14]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[15]  A. Chowdhury,et al.  Cuckoo search algorithm for economic dispatch , 2013 .

[16]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

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

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

[19]  Sen Zhang,et al.  Template matching using grey wolf optimizer with lateral inhibition , 2017 .

[20]  Laizhong Cui,et al.  Artificial bee colony algorithm with gene recombination for numerical function optimization , 2017, Appl. Soft Comput..

[21]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

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

[23]  Aaron C. Zecchin,et al.  An Adaptive Convergence-Trajectory Controlled Ant Colony Optimization Algorithm With Application to Water Distribution System Design Problems , 2017, IEEE Transactions on Evolutionary Computation.

[24]  A. Kaveh,et al.  A novel heuristic optimization method: charged system search , 2010 .

[25]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[26]  Ali Mortazavi,et al.  Sizing and layout design of truss structures under dynamic and static constraints with an integrated particle swarm optimization algorithm , 2017, Appl. Soft Comput..

[27]  Seyed Mohammad Mirjalili.  How effective is the Grey Wolf optimizer in training multi-layer perceptrons , 2014, Applied Intelligence.

[28]  Crina Grosan,et al.  Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks , 2018, IEEE Transactions on Neural Networks and Learning Systems.

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

[30]  Alireza Askarzadeh,et al.  Developing a discrete harmony search algorithm for size optimization of wind–photovoltaic hybrid energy system , 2013 .

[31]  Saurabh Chaudhury,et al.  Multilevel thresholding using grey wolf optimizer for image segmentation , 2017, Expert Syst. Appl..

[32]  Guan-zheng Tan,et al.  Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems , 2014, Journal of Central South University.

[33]  Wansheng Tang,et al.  Monkey Algorithm for Global Numerical Optimization , 2008 .

[34]  Zhijian Wu,et al.  Elite Opposition-Based Differential Evolution for Solving Large-Scale Optimization Problems and Its Implementation on GPU , 2012, 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies.

[35]  Fang Liu,et al.  Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning , 2010 .

[36]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.