An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount

To improve the optimization quality, stability, and speed of convergence of wolf pack algorithm, an adaptive shrinking grid search chaotic wolf optimization algorithm using standard deviation updating amount (ASGS-CWOA) was proposed. First of all, a strategy of adaptive shrinking grid search (ASGS) was designed for wolf pack algorithm to enhance its searching capability through which all wolves in the pack are allowed to compete as the leader wolf in order to improve the probability of finding the global optimization. Furthermore, opposite-middle raid method (OMR) is used in the wolf pack algorithm to accelerate its convergence rate. Finally, “Standard Deviation Updating Amount” (SDUA) is adopted for the process of population regeneration, aimed at enhancing biodiversity of the population. The experimental results indicate that compared with traditional genetic algorithm (GA), particle swarm optimization (PSO), leading wolf pack algorithm (LWPS), and chaos wolf optimization algorithm (CWOA), ASGS-CWOA has a faster convergence speed, better global search accuracy, and high robustness under the same conditions.

[1]  Salah Kamel,et al.  Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer , 2018, Energies.

[2]  Salah Kamel,et al.  Optimal reactive power dispatch considering SSSC using Grey Wolf algorithm , 2016, 2016 Eighteenth International Middle East Power Systems Conference (MEPCON).

[3]  Cheng Yongbo,et al.  Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm , 2017 .

[4]  Aidong Zhang,et al.  An ant colony optimization algorithm for learning brain effective connectivity network from fMRI data , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[5]  Baocai Yin,et al.  A comparative study on swarm intelligence for structure learning of Bayesian networks , 2017, Soft Comput..

[6]  Wu Deng,et al.  An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem , 2019, IEEE Access.

[7]  A. K. Bhatia,et al.  Tackling 0/1 knapsack problem with gene induction , 2003, Soft Comput..

[8]  M. J. Mahjoob,et al.  A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search , 2010, Comput. Math. Appl..

[9]  Meng Sun,et al.  Study on an Adaptive Co-Evolutionary ACO Algorithm for Complex Optimization Problems , 2018, Symmetry.

[10]  W. Jacquet,et al.  How standard deviation contributes to the validity of a LDF signal: a cohort study of 8 years of dental trauma , 2019, Lasers in Medical Science.

[11]  Li Xiao,et al.  An Optimizing Method Based on Autonomous Animats: Fish-swarm Algorithm , 2002 .

[12]  Aidong Zhang,et al.  BFO-FMD: bacterial foraging optimization for functional module detection in protein–protein interaction networks , 2018, Soft Comput..

[14]  Xuyan Tu,et al.  Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search , 2007, The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007).

[15]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[16]  Lalit M. Patnaik,et al.  Genetic algorithms: a survey , 1994, Computer.

[17]  Baocai Yin,et al.  Bacterial foraging optimization using novel chemotaxis and conjugation strategies , 2016, 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]  Aidong Zhang,et al.  Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm , 2016, PloS one.

[20]  T. Eschenbach,et al.  Risk, standard deviation, and expected value: when should an individual start social security? , 2019, The Engineering Economist.

[21]  Erik Cuevas,et al.  An Algorithm for Global Optimization Inspired by Collective Animal Behavior , 2012 .

[22]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[23]  Wu Deng,et al.  A novel collaborative optimization algorithm in solving complex optimization problems , 2016, Soft Computing.

[24]  Hu Peng,et al.  An adaptive beamforming method for ultrasound imaging based on the mean‐to‐standard‐deviation factor , 2018, Ultrasonics.

[25]  Roy Sterritt,et al.  Swarms and Swarm Intelligence , 2007, Computer.

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

[27]  Mingwen Chi.  An improved wolf pack algorithm , 2019, AIIPCC '19.

[28]  Baocai Yin,et al.  Structural learning of Bayesian networks by bacterial foraging optimization , 2016, Int. J. Approx. Reason..

[29]  Aidong Zhang,et al.  Bacterial biological mechanisms for functional module detection in PPI networks , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[30]  Yong Zhu,et al.  A chaos wolf optimization algorithm with self-adaptive variable step-size , 2017 .

[31]  Zhou Yong-quan,et al.  Wolf colony search algorithm based on leader strategy , 2013 .

[32]  Husheng Wu,et al.  An oppositional wolf pack algorithm for Parameter identification of the chaotic systems , 2016 .

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

[34]  Salah Kamel,et al.  Optimal planning of renewable distributed generation in distribution systems using grey wolf optimizer GWO , 2017, 2017 Nineteenth International Middle East Power Systems Conference (MEPCON).

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

[36]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[37]  Jianqiao Yu,et al.  Three-dimensional unmanned aerial vehicle path planning using modified wolf pack search algorithm , 2017, Neurocomputing.

[38]  Manojit Pramanik,et al.  Validation of delay‐multiply‐and‐standard‐deviation weighting factor for improved photoacoustic imaging of sentinel lymph node , 2019, Journal of biophotonics.