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Augmented whale feature selection for IoT attacks: Structure, analysis and applications

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 112卷 18-40页

作者： Mafarja, Majdi Heidari, Ali Asghar Habib, Maria Faris, Hossam Thaher, Thaer Aljarah, Ibrahim Birzeit Univ Dept Comp Sci Birzeit Palestine Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Al Quds Univ Dept Comp Sci Jerusalem Palestine Palestine Polytech Univ Dept Comp Sci & Engn Hebron Palestine

Smart connected appliances expand the boundaries of the conventional Internet into the new Internet of Things (IoT). IoT started to hold a significant role in our life, and in several fields as in transportation, industry, smart homes, and cities. However, one of the critical issues is how to protect IoT environments and prevent intrusions. Attacks detection systems aim to identify malicious patterns and threats that cannot be detected by traditional security countermeasures. In literature, feature selection or dimensionality reduction has been profoundly studied and applied to the design of intrusion detection systems. In this paper, we present a novel wrapper feature selection approach based on augmented whale optimization algorithm (WOA), which adopted in the context of IoT attacks detection and handles the high dimensionality of the problem. In our approach, we introduce the use of both V-shaped and S-shaped transfer functions into WOA and compare the superior variant with other well-known evolutionary optimizers. The experiments are conducted using N-BaIoT dataset;wherein, five datasets were sampled from the original dataset. The dataset represents real IoT traffic, which is drawn from the UCI repository. The experimental results show that WOA based on V-shaped transfer function combined with elitist tournament binarization method is superior over S-shaped transfer function and outperforms other well-regarded evolutionary optimizers based on the obtained average accuracy, fitness, number of features, running time and convergence curves. Hence, we can conclude that the proposed approach can be deployed in IoT intrusion detection systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Internet of things whale optimization algorithm Feature selection Attacks detection Classification

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A new whale optimisation algorithm based on self-adapting parameter adjustment and mix mutation strategy

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2020年第10-11期33卷 949-961页

作者： Tong, Wangyu Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

The whale optimization algorithm (WOA), which mimics the social behavior of humpback whales, is a recently developed meta-heuristic algorithm. However, WOA exists the defect of easily falling into local optimum. This paper proposes a new WOA based on a self-adapting parameter adjustment and a mix mutation strategy (abbreviated as SMWOA). The self-adapting parameter adjustment strategy based on a normal distribution is adopted to make the algorithm jump out of local optima and enhance the global exploration capability. Meanwhile, in order to obtain a better tradeoff between the exploration and exploitation capabilities of the WOA, a novel mix mutation strategy is embedded in the proposed algorithm. The performance of SMWOA is tested on 23 benchmark functions and test suites composed of four engineering design problems. Experimental results and statistical analyses indicate that the proposed algorithm is very competitive when compared to the original WOA as well as some state-of-the-art algorithms.

关键词： Engineering design problems exploration and Exploitation function optimization problems whale optimization algorithm

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An improved long short-term memory networks with Takagi-Sugeno fuzzy for traffic speed prediction considering abnormal traffic situation

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COMPUTATIONAL INTELLIGENCE 2020年第3期36卷 964-993页

作者： George, Shiju Santra, Ajit Kumar Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India Amal Jyothi Coll Engn Kottayam Kerala India

Traffic speed prediction is an emerging paradigm for achieving a better transportation system in smart cities and improving the heavy traffic management in the intelligent transportation system (ITS). The accurate traffic speed prediction is affected by many contextual factors such as abnormal traffic conditions, traffic incidents, lane closures due to construction or events, and traffic congestion. To overcome these problems, we propose a new method named fuzzy optimized long short-term memory (FOLSTM) neural network for long-term traffic speed prediction. FOLSTM technique is a hybrid method composed of computational intelligence (CI), machine learning (ML), and metaheuristic techniques, capable of predicting the speed for macroscopic traffic key parameters. First, the proposed hybrid unsupervised learning method, agglomerated hierarchical K-means (AHK) clustering, divides the input samples into a group of clusters. Second, based on parameters the Gaussian bell-shaped fuzzy membership function calculates the degree of membership (high, low, and medium) for each cluster using Takagi-Sugeno fuzzy rules. Finally, the whale optimization algorithm (WOA) is used in LSTM to optimize the parameters obtained by fuzzy rules and calculate the optimal weight value. FOLSTM evaluates the accurate traffic speed from the abnormal traffic data to overcome the nonlinear characteristics. Experimental results demonstrated that our proposed method outperforms the state-of-the-art approaches in terms of metrics such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).

关键词： agglomerative hierarchical K-means clustering fuzzy optimized long short-term memory Gaussian bell-shaped membership function traffic speed prediction whale optimization algorithm

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Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network

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APPLIED SOFT COMPUTING 2020年 92卷 106259-106259页

作者： Yan, Chun Li, Meixuan Liu, Wei Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

Time deposit has the characteristics of strong stability and low cost. It is a stable source of funds for banks. In this paper, S_Kohonen network is used to predict the success rate of fixed deposit in bank telephone marketing. Firstly, the output layer is added after the competition layer of unsupervised Kohonen network, which makes Kohonen network become S_Kohonen network with supervised learning. Because the improved S_Kohonen network is similar to other feedforward neural networks, each adjacent layer is connected by weights, and the initial weights are random, which easily leads to the unstable output of the network, and still has the disadvantage of relatively low prediction accuracy. Therefore, an improved whale optimization algorithm (IWOA) is proposed to optimize the weights between the input layer and the competition layer of S_Kohonen network. In this paper, the inertia weight of whale optimization algorithm is introduced into random factor on the basis of non-linear decline, and then the random search pattern of Levy flight is introduced into whale algorithm. Finally, the empirical results show that the improved S_Kohonen network can more intuitively represent the classification results of the network, and the classification accuracy of S_Kohonen network optimized by IWOA is significantly higher than that of S_Kohonen network optimized by GA, WOA and LWOA algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： whale optimization algorithm Kohonen neural network Supervised learning

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Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout

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ENERGIES 2020年第3期13卷 556页

作者： Tao, Siyu Feijoo, Andres Zhou, Jiemin Zheng, Gang Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Univ Vigo Dept Enxeneria Elect Campus Lagoas Vigo 36310 Spain Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China

The advances in the manufacturing industry make it possible to install wind turbines (WTs) with large capacities in offshore wind farms (OWFs) in deep water areas far away from the coast where there are the best wind resources. This paper proposes a novel method for OWF optimal planning in deep water areas with a circular boundary. A three-dimensional model of the planning area's seabed is established in a cylindrical coordinate. Two kinds of WTs with capacities of 4 and 8 MW respectively are supposed to be mixed-installed in that area. Baseline cases are analyzed and compared to verify the superiority of a circular layout pattern and the necessity of a non-uniform installation. Based on the establishment of the optimization model and a realistic wind condition, a novel heuristic algorithm, i.e., the whale optimization algorithm (WOA), is applied to solve the problem to obtain the type selection and coordinates of WTs simultaneously. Finally, the feasibility and advantages of the proposed scheme are identified and discussed according to the simulation results.

关键词： circular pattern multiple types of wind turbines offshore wind farm three-dimensional seabed whale optimization algorithm

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Robust possibilistic programming for multi-item EOQ model with defective supply batches: whale optimization and Water Cycle algorithms

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NEURAL COMPUTING & APPLICATIONS 2019年第10期31卷 6587-6614页

作者： Khalilpourazari, Soheyl Pasandideh, Seyed Hamid Reza Ghodratnama, Ali Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran

This paper proposes a new mathematical model for multi-product economic order quantity model with imperfect supply batches. The supply batch is inspected upon arrival using "all or None" policy and if found defective, the whole batch will be rejected. In this paper, the goal is to determine optimal order quantity and backordering size for each product. To develop a realistic mathematical model of the problem, three robust possibilistic programming (RPP) approaches are developed to deal with uncertainty in main parameters of the model. Due to the complexity of the proposed RPP models, two novel meta-heuristic algorithms named water cycle and whale optimization algorithms are proposed to solve the RPP models. Various test problems are solved to evaluate the performance of the two novel meta-heuristic algorithms using different measures. Also, single-factor ANOVA and Tukey's HSD test are utilized to compare the effectiveness of the two meta-heuristic algorithms. Applicability and efficiency of the RPP models are compared to the Basic Possibilistic Chance Constrained Programming (BPCCP) model within different realizations. The simulation results revealed that the RPP models perform significantly better than the BPCCP model. At the end, sensitivity analyses are carried out to determine the effect of any change in the main parameters of the mathematical model on the objective function value to determine the most critical parameters.

关键词： Economic order quantity Imperfect batches Robust possibilistic programming Water Cycle algorithm whale optimization algorithm Chance constrained programming

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MO-WOA-GRA-TOPSIS integrated framework for thermo-hydrodynamic optimization of supercritical n-decane in aerospace corrugated cooling systems

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2025年 215卷

作者： Chen, Zhijie Han, Huaizhi He, Da Yang, Jiali Zou, Xuanyang Sichuan Univ Sch Chem Engn Chengdu 610065 Peoples R China Sichuan Univ Engn Res Ctr Combust & Cooling Aerosp Power Minist Educ Chengdu 610065 Peoples R China

This study proposes a novel MO-WOA-GRA-TOPSIS integrated framework for multi-objective thermo-hydrodynamic optimization of supercritical n-decane in aerospace corrugated cooling systems. Five critical design parameters (corrugation pitch, height, fillet radius, pressure, and mass flow rate) were systematically analyzed to balance heat transfer efficiency, flow resistance, and thermal stability. Quadratic response surface models were developed for Nusselt number (Nu), friction factor (f), and average temperature fluctuation coefficient (z1C). Grey relational analysis identified dimensionless corrugation height (R/D) as the dominant factor for Nu (26.73 %) and f (24.62 %), while mass flow rate (qm) primarily influenced dC (21.88 %). The proposed framework outperformed conventional NSGA-II in convergence speed and Pareto front uniformity. Pareto-optimal solutions achieved 46.2 % Nu enhancement and 19.7 % f reduction while maintaining dC within 0.5, demonstrating superior thermal management for next-generation aero-engine cooling systems.

关键词： Multi-objective optimization Supercritical hydrocarbon fuel Corrugated cooling channel whale optimization algorithm Thermal management Nomenclature

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Efficient path planning of AUVs for container ship oil spill detection in coastal areas

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OCEAN ENGINEERING 2020年 217卷 107932-107932页

作者： Kumar, S. Vinoth Jayaparvathy, R. Priyanka, B. N. Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Commun Engn Chennai Tamil Nadu India

Oil spills due to accidental leakage from Container Ships in a Marine environment poses a major challenge to aquatic living organisms and affects human health by increasing the environmental pollution. Autonomous Underwater Vehicles (AUV's) are deployed for detection and cleaning of oil spills. As the coastal areas are vast and lengthy, effective path planning of deployed AUVs is necessary. Many path planning algorithms for AUVs have been proposed in literature using approaches which necessitate search through large distance. The AUVs have to travel through a large distance to detect the spills in the search area resulting in more energy consumption. In this article we propose an efficient approach for determination of optimal AUV paths for detection of oil spills resulting in reduced search distance for a given area. The proposed approach implements a hybrid evolutionary optimization algorithm named whale Cuckoo Search optimization algorithm (WCSOA) that results in increase in the search space, global search ability and the convergence speed. With the proposed approach the overall search delay and energy consumption of AUVs are reduced. It is observed from the simulation results obtained that the proposed approach provides reduction in AUV energy consumption by 57% compared to the existing Boustrophedon and WOA approach.

关键词： AUV optimization Oil spill whale optimization algorithm Cuckoo search algorithm

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Optimal weighted feature vector and deep belief network for medical data classification

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2020年第2期18卷

作者： Kadam, Vinod Jagannath Jadhav, Shivajirao Manikrao Dr Babasaheb Ambedkar Technol Univ Dept Informat Technol Lonere Maharashtra India

Medical data classification is the process of transforming descriptions of medical diagnoses and procedures into universal medical code numbers. The diagnoses and procedures are usually taken from a variety of sources within the healthcare record, such as the transcription of the physician's notes, laboratory results, radiologic results and other sources. However, there exist many frequency distribution problems in these domains. Hence, this paper intends to develop an advanced and precise medical data classification approach for diabetes and breast cancer dataset. With the knowledge of the features and challenges persisting with the state-of-the-art classification methods, deep learning-based medical data classification methodology is proposed here. It is well known that deep learning networks learn directly from the data. In this paper, the medical data is dimensionally reduced using Principle Component Analysis (PCA). The dimensionally reduced data are transformed by multiplying by a weighting factor, which is optimized using whale optimization algorithm (WOA), to obtain the maximum distance between the features. As a result, the data are transformed into a label-distinguishable plane under which the Deep Belief Network (DBN) is adopted to perform the deep learning process, and the data classification is performed. Further, the proposed WOA-based DBN (WOADBN) method is compared with the Neural Network (NN), DBN, Generic algorithm-based NN (GANN), GADBN, Particle Swarm optimization (PSONN), PSO-based DBN (PSODBN), WOA-based NN (WOANN) techniques and the results are obtained, which shows the superiority of proposed algorithm over conventional methods.

关键词： Medical data classification principle component analysis distance measure whale optimization algorithm deep belief network

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On modeling of substrate loading in GaN HEMT using grey wolf algorithm

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2020年第2期19卷 576-590页

作者： Jarndal, Anwar Univ Sharjah Elect Engn Dept Sharjah 27272 U Arab Emirates

In this paper, four different equivalent circuit models to describe substrate loading effect in GaN HEMT on Si substrate are investigated. The effect is characterized by Z-parameter measurements of open de-embedding structure for 16 x 200-mu m GaN HEMT on Si substrate. The grey wolf optimization (GWO)-based procedure is developed to extract optimal values for the model elements. The performance of the proposed technique is evaluated by using two other meta-heuristic optimizations, the well-known particle swarm and the recently developed whale algorithm. The three extraction procedures are evaluated in terms of their effectiveness and rate of convergences. The models are validated by means of S-parameters simulation for the considered device at different passive and active bias conditions. A very good agreement with measurements is achieved when using the GWO, validating its applicability for small- and large-signal modeling applications.

关键词： Grey wolf optimizer whale optimization algorithm Particle swarm optimization GaN HEMT Open de-embedding structure Substrate loading

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