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A New Chaotic whale optimization algorithm for Features Selection

JOURNAL OF CLASSIFICATION 2018年第2期35卷 300-344页

作者： Sayed, Gehad Ismail Darwish, Ashraf Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat 5 Ahmed Zewail Ad Doqi 12613 Giza Governorat Egypt Helwan Univ Fac Sci Helwan Egypt

The whale optimization algorithm (WOA) is a novel evolutionary algorithm inspired by the behavior of whales. Similar to other evolutionary algorithms, entrapment in local optima and slow convergence speed are two probable problems it encounters in solving challenging real applications. This paper presents a novel chaotic whale optimization algorithm (CWOA) to overcome these problems where chaotic search is embedded in the searching iterations of WOA. Ten chaotic maps are considered to improve the performance of WOA. Experiments on ten benchmark datasets show the novel CWOA is effective for selecting relevant features with a high classification performance and a small number of features. Additionally the performance of CWOA is compared with WOA and ten other optimization algorithms. The experimental results show that circle chaotic map is the best chaotic map to significantly boost the performance of WOA. Moreover, chaotic with modifications of exploration operators outperform the highest performance.

关键词： whale optimization algorithm Chaotic maps Features selection

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A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm

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MODERN PHYSICS LETTERS B 2019年第7期33卷

作者： Ren, Gong Yang, Renhuan Yang, Renyu Zhang, Pei Yang, Xiuzeng Xu, Chuangbiao Hu, Baoguo Zhang, Huatao Lu, Yaosheng Cai, Yanning Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Guangdong Peoples R China Sun Yat Sen Univ Sch Engn Guangzhou 511400 Guangdong Peoples R China Guangxi Normal Univ Nationalites Dept Phys & Elect Engn Chongzuo 532200 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Jiangsu Peoples R China Chinese Acad Sci Key Lab Astron Opt & Technol Nanjing Inst Astron Opt & Technol Nanjing 210042 Jiangsu Peoples R China

Compared to the integer-order systems, the system characteristics of the fractional system are closer to the system characteristics of the real engineering system, the study found beyond that, strictly speaking, various physical phenomena in nature are nonlinear. The problem of parameter estimation problem of fractional-order nonlinear systems can be transformed into the problem of parameter optimization problem by constructing an appropriate fitness function. This paper proposes a hybrid improvement algorithm based on whale optimization algorithm (WOA) to solve this problem and verify it both in Lorenz system and Lu system. The simulation result shows that the hybrid improved algorithm is superior to genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and WOA in convergence speed and accuracy.

关键词： Fractional-order Lu nonlinear systems Lorenz nonlinear system whale optimization algorithm

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A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 116卷 454-471页

作者： Ghahremani-Nahr, Javid Kian, Ramez Sabet, Ehsan ACECR Tabriz Iran Nottingham Trent Univ Nottingham Business Sch Nottingham NG1 4FQ England Univ Tabriz Dept Ind Engn Tabriz Iran Loughborough Univ Technol Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England

The closed-loop supply chain (CLSC) management as one of the most significant management issues has been increasingly spotlighted by the government, companies and customers, over the past years. The primary reasons for this growing attention mainly down to the governments-driven and environmental related regulations which has caused the overall supply cost to reduce while enhancing the customer satisfaction. Thereby, in the present study, efforts have been made to propose a facility location/allocation model for a multi-echelon multi-product multi-period CLSC network under shortage, uncertainty, and discount on the purchase of raw materials. To design the network, a mixed-integer nonlinear programming (MINLP) model capable of reducing total costs of network is proposed. Moreover, the model is developed using a robust fuzzy programming (RFP) to investigate the effects of uncertainty parameters including customer demand, fraction of returned products, transportation costs, the price of raw materials, and shortage costs. As the developed model was NP-hard, a novel whale optimization algorithm (WOA) aimed at minimizing the network total costs with application of a modified priority-based encoding procedure is proposed. To validate the model and effectiveness of the proposed algorithm, some quantitative experiments were designed and solved by an optimization solver package and the proposed algorithm. Comparison of the outcomes provided by the proposed algorithm and exact solution is indicative of high quality performance of the applied algorithm to find a near-optimal solution within the reasonable computational time. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Closed-loop supply chain network design Modified priority-based encoding Uncertainty whale optimization algorithm

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SLIDING MODE PREDICTION FAULT-TOLERANT CONTROL METHOD BASED ON whale optimization algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第6期15卷 2119-2133页

作者： Liu, Zhangxi Yang, Pu Li, Dejie Xu, Mengyang Nanjing Univ Aeronaut & Astronaut Coll Automat 29 Jiangjun Ave Nanjing 210016 Jiangsu Peoples R China

A novel fault-tolerant control method for sliding mode prediction based on whale optimization algorithm is designed for fault-tolerant control of quad-rotor aircraft system with discrete time-delay uncertainties. The whole process sliding surface is used as the prediction model to ensure the global robustness, and a power function reference trajectory with fault compensation is designed to suppress the influence of chattering and suppress the uncertainty and fault. In the process of rolling optimization, considering the high-precision and fast response of the optimization process, the whale optimization algorithm is adopted, which has strong optimization performance, less parameter setting, fast convergence and high precision. The simulation shows that the algorithm has good effects in terms of robustness, weakening of chattering, and convergence speed.

关键词： Fault tolerant control Time varying delay whale optimization algorithm Power function approach rate Quad-rotor helicopter

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Texture classification using convolutional neural network optimized with whale optimization algorithm

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SN APPLIED SCIENCES 2019年第6期1卷 655-655页

作者： Dixit, Ujjawal Mishra, Apoorva Shukla, Anupam Tiwari, Ritu ABV IIITM Soft Comp & Expert Syst Lab Gwalior India Bennett Univ Dept Comp Sci Engn Greater Noida India Indian Inst Informat Technol Pune Maharashtra India

Texture classification is an active area of research in the field of pattern recognition. Convolutional neural networks (CNNs) have a remarkable capability of recognizing patterns and are one of the most efficient deep learning techniques. But, finding the optimal values of the different hyperparameters of the CNN is a major challenge. Nature-inspired algorithms (NIAs) are the meta-heuristic algorithms well-known for their optimizing capability. whale optimization algorithm (WOA) is a recent nature-inspired algorithm (NIA) that is inspired by the hunting behaviour of the humpback whales. In this paper, we propose a novel deep learning technique for texture recognition using a CNN optimized through WOA. We apply WOA at the two different levels in the CNN: In the convolutional layer (for optimizing the values of the filters), and in the fully-connected layer (for optimizing the values of the weights and biases). For examining the performance of our technique, we apply it to the following three benchmark texture datasets: Kylberg v1.0, Brodatz, and Outex_TC_00012. Our model performs better than the most of the existing methods for the Kylberg and the Outex_TC_00012 datasets and gives competitive results for the Brodatz dataset. It is evident from the results that our model has the potential for application in the field of texture recognition.

关键词： Convolutional neural network whale optimization algorithm Pattern recognition Texture classification Deep learning

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Efficient Lidar Signal Denoising algorithm Using Variational Mode Decomposition Combined with a whale optimization algorithm

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REMOTE SENSING 2019年第2期11卷 126-126页

作者： Li, Hongxu Chang, Jianhua Xu, Fan Liu, Zhenxing Yang, Zhenbo Zhang, Luyao Zhang, Shuyi Mao, Renxiang Dou, Xiaolei Liu, Binggang Nanjing Univ Informat Sci & Technol CIC FEMD Nanjing 210044 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Key Lab Meteorol Disaster Nanjing 210044 Jiangsu Peoples R China Taizhou Polytech Coll Dept Informat Technol Taizhou 225300 Jiangsu Peoples R China

Although lidar is a powerful active remote sensing technology, lidar echo signals are easily contaminated by noise, particularly in strong background light, which severely affects the retrieval accuracy and the effective detection range of the lidar system. In this study, a coupled variational mode decomposition (VMD) and whale optimization algorithm (WOA) for noise reduction in lidar signals is proposed and demonstrated completely. The combination of optimal VMD parameters of decomposition mode number K and quadratic penalty alpha was obtained by using the WOA and was critical in acquiring satisfactory analysis results for VMD denoising technology. Then, the Bhattacharyya distance was applied to identify the relevant modes, which were reconstructed to achieve noise filtering. Simulation results show that the performance of the proposed VMD-WOA method is superior to that of wavelet transform, empirical mode decomposition, and its variations. Experimentally, this method was successfully used to filter a lidar echo signal. The signal-to-noise ratio of the denoised signal was increased to 23.92 dB, and the detection range was extended from 6 to 10 km.

关键词： lidar signal variational mode decomposition whale optimization algorithm Bhattacharyya distance

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RETRACTED: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem (Retracted article. See vol. 128, pg. 567, 2022)

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 85卷 129-145页

作者： Abdel-Basset, Mohamed Manogaran, Gunasekaran El-Shahat, Doaa Mirjalili, Seyedali Zagazig Univ Fac Comp & Informat Zagazig Sharqiyah Egypt Univ Calif Davis Davis CA 95616 USA Griffith Univ Sch Informat & Commun Technol Nathan Campus Brisbane Qld 4111 Australia

The flow shop scheduling problem is one of the most important types of scheduling with a large number of real-world applications. In this paper, we propose a new algorithm that integrates the whale optimization algorithm (WOA) with a local search strategy for tackling the permutation flow shop scheduling problem. The Largest Rank Value (LRV) requires the algorithm to deal with the discrete search space of the problem. The diversity of candidate schedules is improved using a swap mutation operation as well. In addition to the insert-reversed block operation is adopted to escape from the local optima. The proposed hybrid whale algorithm (HWA) is incorporated with Nawaz-Enscore-Ham (NEH) to improve the performance of the algorithm. It is observed that HWA gives competitive results compared to the existing algorithms. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Flow shop scheduling Makespan whale optimization algorithm Hybrid algorithm Local search

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Photovoltaic-grid Complementary Electric Charging System Based on whale optimization algorithm

Photovoltaic-grid Complementary Electric Charging System Bas...

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2019 2nd International Conference on Electrical and Electronic Engineering（EEE 2019）

作者： Xin-jia LU Yi LUO Yong TANG Guilin University of Electronic Technology Mechanical and Electrical Engineering College

This paper proposes a photovoltaic-grid complementary electric vehicle charging system based on whale optimization algorithm, including control part, traditional electric energy and photovoltaic energy supply part and electric vehicle charging equipment part. The electric vehicle is charged by the complementary power supply mode of photovoltaic energy and traditional grid power. The control system uses the whale optimization algorithm to dynamically plan the use of the charging equipment in different periods of time to optimize the utilization of photovoltaic and grid power. The photovoltaic-grid complementary electric charging system based on the whale optimization algorithm can effectively improve the user’s satisfaction with the charging of electric vehicles, plan the utilization of new energy and grid energy, realize the economical efficiency of energy utilization, and reduce the energy use cost.

关键词： whale optimization algorithm Photovoltaic-grid complementation Electric vehicle charging Dynamic programming Economy

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An Improved Binary whale optimization algorithm for Feature Selection of Network Intrusion Detection 4

An Improved Binary Whale Optimization Algorithm for Feature ...

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4th IEEE International Symposium on Wireless Systems within the International Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS)

作者： Xu, Hui Fu, Yingchun Fang, Ce Cao, Qianqian Su, Jun Wei, Siwei Hubei Univ Technol Wuhan 430068 Hubei Peoples R China CCCC Second Highway Consultants Co Ltd Wuhan 430052 Hubei Peoples R China

ISBN: (纸本)9781538675878

As for network intrusion detection, the time and space consumption of the detection algorithm increases with the feature dimension. The irrelevant or redundant features may cause the detection accuracy to decrease. In order to improve the timeliness and reliability of intrusion detection, feature selection is one of the most effective methods proposed currently. Considering that whale optimization algorithm (WOA) has good classification performance and dimensionality reduction in reducing feature redundancy, it can be applied to feature selection of network intrusion detection. The WOA converges slowly in the search process and easily falls into a local optimum in the updating mechanism, which restricts the classification performance and dimensionality reduction of the algorithm. Therefore, this paper adopts the nonlinear convergence factor strategy in the search process and introduces the Particle Swarm optimization (PSO) strategy in the updating mechanism. An Improved Binary whale optimization algorithm (IBWOA) is then proposed in order to achieve a better dimension reduction on the premise of ensuring accuracy for feature selection of intrusion detection. In the experimental stage, multiple data sets in the UCI database for machine learning were used for verification by comparing with other feature selection methods. Finally, the KDD CUP 99 data set was used for verification of feature selection in intrusion detection. The experimental results show that, compared with Genetic algorithm (GA), PSO and WOA, the IBWOA improves the classification accuracy and dimensionality reduction in feature selection of intrusion detection.

关键词： network intrusion detection feature selection whale optimization algorithm convergence factor Particle Swarm optimization

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Parameter Estimation of Software Reliability Growth Models by A Modified whale optimization algorithm 17

Parameter Estimation of Software Reliability Growth Models b...

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17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Lu, Kezhong Ma, Zongmin Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Jiangsu Peoples R China Chizhou Univ Coll Comp Sci Chizhou 247100 Anhui Peoples R China

ISBN: (纸本)9781538674451

Software reliability growth models (SRGMs) are non-linear in nature, so they are difficult to estimate the proper parameters. An estimation method based on modified whale optimization algorithm (MWOA) in which parameters are estimated is discussed in this paper. The proposed MWOA shows significant advantages in handling variety of modeling problems such as the exponential model (EXPM), power model (POWM) and delayed S-shaped model (DSSM). However, the fitting error of a single model is relatively large. In view of the different growth characteristics at each stage of the model, a three-stage software reliability growth model is proposed in this paper. MWOA is used to estimate the parameters of three-stage software reliability models. Experimental results show that the fitting accuracy of three-stage model is significantly less than that of a single model. MWOA with the three-stage model can provide a better estimate of the software faults.

关键词： Software Reliability Growth Models Parameter Estimation whale optimization algorithm Three-stage Model

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