Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm

Abstract Due to the variability of the radiated signal of the underwater targets, the classification of the underwater acoustical dataset is a challenging problem in the real world application. In this paper, to classify underwater acoustical targets, first, a new meta-heuristic Chimp Optimization Algorithm (ChOA) inspired by chimp hunting behaviour is developed for training an Artificial Neural Network (ANN). Second, a new underwater acoustical dataset is developed using passive propeller acoustic data collected in a laboratory. To evaluate the proposed classifier, this algorithm is compared to the Ion Motion Algorithm (IMA), Gray Wolf Optimization (GWO), and a hybrid algorithm. Measured metrics are convergence speed, the possibility of trapping in local minimum and classification accuracy. The results show that the newly proposed algorithm in most cases provides better or comparable performance compared to the other benchmark algorithms.

[1]  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..

[2]  Tayfun Dede,et al.  Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm , 2014 .

[3]  Craig B. Stanford,et al.  The Hunting Ecology of Wild Chimpanzees: Implications for the Evolutionary Ecology of Pliocene Hominids , 1996 .

[4]  Luis Pastor Sánchez Fernández,et al.  Aircraft class identification based on take-off noise signal segmentation in time , 2013, Expert Syst. Appl..

[5]  Adnan Shaout,et al.  Airport take-off noise assessment aimed at identify responsible aircraft classes. , 2016, The Science of the total environment.

[6]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks , 2007, MDAI.

[7]  M. Khishe,et al.  Neural Network Trained by Biogeography-Based Optimizer with Chaos for Sonar Data Set Classification , 2017, Wireless Personal Communications.

[8]  Mohammad Reza Mosavi,et al.  Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network , 2018, Analog Integrated Circuits and Signal Processing.

[9]  S M Mousavi,et al.  MARINE PROPELLERS DESIGN USING PARTICLE SWARM OPTIMIZATION WITH INDEPENDENT GROUPS TO IMPROVE EFFICIENCY AND REDUCE CAVITATION , 2016 .

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

[11]  Mohammad Reza Mosavi,et al.  Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network , 2017 .

[12]  Mohammad Reza Mosavi,et al.  Accurate Classification of EEG Signals Using Neural Networks Trained by Hybrid Population-physic-based Algorithm , 2018, International Journal of Automation and Computing.

[13]  M. Khishe,et al.  Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm , 2019, Wirel. Pers. Commun..

[14]  Iain D. Couzin,et al.  Fission–fusion populations , 2009, Current Biology.

[15]  Silke A.T. Weber,et al.  Social-Spider Optimization-Based Artificial Neural Networks Training and Its Applications for Parkinson's Disease Identification , 2014, 2014 IEEE 27th International Symposium on Computer-Based Medical Systems.

[16]  Mohammad Reza Mosavi,et al.  Classification of sonar data set using neural network trained by Gray Wolf Optimization , 2016 .

[17]  Hossam Faris,et al.  Optimizing connection weights in neural networks using the whale optimization algorithm , 2016, Soft Computing.

[18]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[19]  Andrew Lewis,et al.  Let a biogeography-based optimizer train your Multi-Layer Perceptron , 2014, Inf. Sci..

[20]  Sirapat Chiewchanwattana,et al.  An improved grey wolf optimizer for training q-Gaussian Radial Basis Functional-link nets , 2014, 2014 International Computer Science and Engineering Conference (ICSEC).

[21]  M. Khishe,et al.  Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm , 2019, Ocean Engineering.

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

[23]  Navid Razmjooy,et al.  A multi layer perceptron neural network trained by Invasive Weed Optimization for potato color image segmentation. , 2012 .

[24]  Christophe Boesch,et al.  Cooperative hunting roles among taï chimpanzees , 2002, Human nature.

[25]  Craig B. Stanford,et al.  HUNTING DECISIONS IN WILD CHIMPANZEES , 1994 .

[26]  Y. Ho,et al.  Simple Explanation of the No-Free-Lunch Theorem and Its Implications , 2002 .

[27]  Luis Alejandro Sánchez-Pérez,et al.  Marine mammal sound classification based on a parallel recognition model and octave analysis , 2017 .

[28]  Mohammad Reza Mosavi,et al.  Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset , 2017 .

[29]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[30]  Christian Blum,et al.  Training feed-forward neural networks with ant colony optimization: an application to pattern classification , 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

[31]  Mohammad Reza Mosavi,et al.  Chaotic fractal walk trainer for sonar data set classification using multi-layer perceptron neural network and its hardware implementation , 2018, Applied Acoustics.

[32]  Mohammad Reza Mosavi,et al.  Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset , 2019, Archives of Acoustics.

[33]  Mohammad Reza Mosavi,et al.  Improved whale trainer for sonar datasets classification using neural network , 2019, Applied Acoustics.

[34]  M. Khishe,et al.  Training a Feed-Forward Neural Network Using Particle Swarm Optimizer with Autonomous Groups for Sonar Target Classification , 2017, J. Circuits Syst. Comput..

[35]  Victor O. K. Li,et al.  Evolutionary artificial neural network based on Chemical Reaction Optimization , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[36]  Khishe Mohammad,et al.  Classification of Sonar Targets Using OMKC, Genetic Algorithms and Statistical Moments , 2016 .

[37]  Christian Blum,et al.  An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training , 2007, Neural Computing and Applications.

[38]  Robert C. Green,et al.  Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons , 2012, Expert Syst. Appl..

[39]  Zita A. Vale,et al.  Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection , 2013, 2013 IEEE PES Conference on Innovative Smart Grid Technologies (ISGT Latin America).