A Hybrid Algorithm Based on Squirrel Search Algorithm and Invasive Weed Optimization for Optimization

This paper proposes a hybrid algorithm called ISSA based on the combination of squirrel search algorithm (SSA) proposed in 2019 and invasive weed optimization (IWO) proposed in 2006. About 36 benchmark functions are employed to test the performances of ISSA. Then, ISSA is combined with support vector machine (SVM) and deterministic maximum-likelihood (DML) algorithm, respectively, and the two corresponding models ISSA-SVM and ISSA-DML are established for performing the grade classifications of air quality and the direction of arrival (DOA) estimation of MEMS vector hydrophone, respectively. The results of 36 benchmark functions prove that the proposed ISSA is able to provide very competitive results in terms of the average values, the standard derivation, and the convergence curves. The average accuracy rate of classification of ISSA-SVM model is the best and reaches 87.91971%, and the DOA estimations of ISSA-DML have the least root mean square error (RMSE) and the closest to the actual angles. Therefore, it is concluded that the proposed ISSA is an effective algorithm for function optimizations and is suitable to be combined with other algorithms and machine learning for classification and estimation.

[1]  Faisal Mehmood,et al.  High Impedance Fault Detection and Isolation in Power Distribution Networks using Support Vector Machines , 2019, Journal of King Saud University - Engineering Sciences.

[2]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[3]  Magdalene Marinaki,et al.  A multi-adaptive particle swarm optimization for the vehicle routing problem with time windows , 2019, Inf. Sci..

[4]  Ujjwal Maulik,et al.  Integrated Rough Fuzzy Clustering for Categorical data Analysis , 2019, Fuzzy Sets Syst..

[5]  Vijander Singh,et al.  A novel nature-inspired algorithm for optimization: Squirrel search algorithm , 2019, Swarm Evol. Comput..

[6]  Giancarlo Mauri,et al.  MedGA: A novel evolutionary method for image enhancement in medical imaging systems , 2019, Expert Syst. Appl..

[7]  C. Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[8]  Yu Huang,et al.  Review on landslide susceptibility mapping using support vector machines , 2018, CATENA.

[9]  Francisco de A. T. de Carvalho,et al.  Clustering of multi-view relational data based on particle swarm optimization , 2019, Expert Syst. Appl..

[10]  Dervis Karaboga,et al.  Artificial bee colony algorithm , 2010, Scholarpedia.

[11]  Yixin Yang,et al.  A variational Bayesian strategy for solving the DOA estimation problem in sparse array , 2019, Digit. Signal Process..

[12]  Seyedali Mirjalili,et al.  SCA: A Sine Cosine Algorithm for solving optimization problems , 2016, Knowl. Based Syst..

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

[14]  Min-Yuan Cheng,et al.  Hybrid Artificial Intelligence–Based PBA for Benchmark Functions and Facility Layout Design Optimization , 2012 .

[15]  Wenhua Du,et al.  Application of Parameter Optimized Variational Mode Decomposition Method in Fault Diagnosis of Gearbox , 2019, IEEE Access.

[16]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[17]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[18]  Qi Liu,et al.  A novel hybrid bat algorithm for solving continuous optimization problems , 2018, Appl. Soft Comput..

[19]  Joseph W Bahlman,et al.  Glide performance and aerodynamics of non-equilibrium glides in northern flying squirrels (Glaucomys sabrinus) , 2013, Journal of The Royal Society Interface.

[20]  Karl Vernes,et al.  GLIDING PERFORMANCE OF THE NORTHERN FLYING SQUIRREL (GLAUCOMYS SABRINUS) IN MATURE MIXED FOREST OF EASTERN CANADA , 2001 .

[21]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.

[22]  Pradeep Jangir,et al.  Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems , 2016, Applied Intelligence.

[23]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[24]  Seyed Mohammad Mirjalili,et al.  Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm , 2015, Knowl. Based Syst..

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

[26]  Kai Song,et al.  The artificial tree (AT) algorithm , 2017, Eng. Appl. Artif. Intell..

[27]  Li Tang,et al.  Predicting the direction of stock markets using optimized neural networks with Google Trends , 2018, Neurocomputing.

[28]  Jianzhou Wang,et al.  A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network. , 2014, The Science of the total environment.

[29]  Yu Song,et al.  Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market , 2016 .

[30]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[31]  Joseph Tabrikian,et al.  A modular neural network for direction-of-arrival estimation of two sources , 2011, Neurocomputing.

[32]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[33]  Chun-Hao Chen,et al.  A Grid-Based Swarm Intelligence Algorithm for Privacy-Preserving Data Mining , 2019, Applied Sciences.

[34]  Yanping Bai,et al.  A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems , 2019, IEEE Access.

[35]  Rui Yao,et al.  A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm , 2017, Soft Computing.

[36]  Hossam Faris,et al.  An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks , 2019, Inf. Fusion.

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

[38]  Ulla M. Norberg,et al.  Evolution of Vertebrate Flight: An Aerodynamic Model for the Transition from Gliding to Active Flight , 1985, The American Naturalist.

[39]  Yu Song,et al.  Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model , 2016, PloS one.

[40]  Xiaomin Xie.  Improvement on projection twin support vector machine , 2017, Neural Computing and Applications.