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

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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Embedded chaotic whale survival algorithm for filter-wrapper feature selection

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SOFT COMPUTING 2020年第17期24卷 12821-12843页

作者： Guha, Ritam Ghosh, Manosij Mutsuddi, Shyok Sarkar, Ram Mirjalili, Seyedali Jadavpur Univ Comp Sci & Engn Dept 188 Raja SC Mallick Rd Kolkata 700032 W Bengal India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia

Classification accuracy provided by a machine learning model depends a lot on the feature set used in the learning process. Feature selection (FS) is an important and challenging preprocessing technique which helps to identify only the relevant features from a dataset, thereby reducing the feature dimension as well as improving the classification accuracy at the same time. The binary version of whale optimization algorithm (WOA) is a popular FS technique which is inspired from the foraging behavior of humpback whales. In this paper, an embedded version of WOA called embedded chaotic whale survival algorithm (ECWSA) has been proposed which uses its wrapper process to achieve high classification accuracy and a filter approach to further refine the selected subset with low computation cost. Chaos has been introduced in the ECWSA to guide selection of the type of movement followed by the whales while searching for prey. A fitness-dependent death mechanism has also been introduced in the system of whales which is inspired from the real-life scenario in which whales die if they are unable to catch their prey. The proposed method has been evaluated on 18 well-known UCI datasets and compared with its predecessors as well as some other popular FS methods. The source code of ECWSA can be found in https://***/Ritam-Guha/ECWSA.

关键词： whale optimization algorithm Feature selection Embedded systems Chaotic mapping UCI dataset optimization Heuristic algorithm Benchmark

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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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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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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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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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Computer-aided diagnosis applied to MRI images of brain tumor using cognition based modified level set and optimized ANN classifier

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第5-6期79卷 3571-3599页

作者： Virupakshappa Amarapur, Basavaraj Appa Inst Engn & Technol Dept CSE Kalaburagi Karnataka India Poojya Doddappa Appa Coll Engn Dept E&E Kalaburagi Karnataka India

MRI image segmentation and classification is one of the important tasks in medical image analysis and visualization, despite occurrence of noise makes it tough to segment the region of interest. In this paper, the MRI images are pre-processed and segmentation is carried out using modified Level set method for the tumor segmentation. Also, it is important to extract the useful features to predict the image class accurately. The proposed method operates Multi-Level wavelet decomposition features and for the wavelet coefficients modified chief descriptions like Grey Level Co-Occurrence Matrix (GLCM), Gabor and moment invariant features are extracted. The classification is carried out using the Adaptive Artificial Neural Network (AANN) methodology. In the adaptive ANN, the layer neurons are optimized using whale optimization algorithm (WOA). The adaptive neural network optimizes the network structure to increase the classification accuracy and thus gives better classification results of tumors based on the segmented images. The proposed method will be executed in the working platform of MATLAB and the results are compared with the previous state of the art techniques. Finally, the proposed method results in classification accuracy of 98%.

关键词： Histogram equalization Median filter Modified level set segmentation Moment invariant features Gabor features Grey level co-occurrence matrix Artificial neural network whale optimization algorithm

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Efficient Hybrid Nature-Inspired Binary Optimizers for Feature Selection

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COGNITIVE COMPUTATION 2020年第1期12卷 150-175页

作者： Mafarja, Majdi Qasem, Asma Heidari, Ali Asghar Aljarah, Ibrahim Faris, Hossam Mirjalili, Seyedali Birzeit Univ Dept Comp Sci Birzeit Israel Birzeit Univ Fac Engn & Technol POB 14 Birzeit Palestine Univ Tehran Sch Surveying & Geospatial Engn Coll Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Univ Jordan King Abdullah II Sch Informat Technol Business Informat Technol Dept Amman Jordan Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4111 Australia

The process of dimensionality reduction is a crucial solution to deal with the dimensionality problem that may be faced when dealing with the majority of machine learning techniques. This paper proposes an enhanced hybrid metaheuristic approach using grey wolf optimizer (GWO) and whale optimization algorithm (WOA) to develop a wrapper-based feature selection method. The main objective of the proposed technique is to alleviate the drawbacks of both algorithms, including immature convergence and stagnation to local optima (LO). The hybridization is done with improvements in the mechanisms of both algorithms. To confirm the stability of the proposed approach, 18 well-known datasets are employed from the UCI repository. Furthermore, the classification accuracy, number of selected features, fitness values, and run time matrices are collected and compared with a set of well-known feature selection approaches in the literature. The results show the superiority of the proposed approach compared with both GWO and WOA. The results also show that the proposed hybrid technique outperforms other state-of-the-art approaches, significantly.

关键词： whale optimization algorithm Grey wolf optimizer optimization Feature selection Metaheuristics

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Optimize star sensor calibration based on integrated modeling with hybrid WOA-LM algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第3期38卷 2683-2691页

作者： Niu, Yanbiao Zhou, Yongquan Luo, Qifang Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning Peoples R China

To ensure the high measurement accuracy, the star sensor must be accurately calibrated before use. Which calibration method is selected to complete the calibration of parameters and how to improve the precision of parameters calibration is the key to achieve high precision attitude measurement of star sensor. Aiming at the problem that the accuracy of the traditional calibration algorithm is greatly affected by the initial value, a parameter calibration algorithm based on hybrid WOA-LM algorithm is proposed. This algorithm can dynamically fuse levenberg-Marquardt (LM) algorithm with whale optimization algorithm (WOA). By making full use of the global search ability of WOA algorithm and the local optimization ability of LM algorithm, the problems of traditional algorithm relying on the initial value, easy to fall into local convergence, low WOA algorithm's calibration efficiency and calibration results are solved. Compared with WOA method, Li method, and the two-step method, this method has better performance. Simulation and experimental results show that this method can optimize all parameters of the star sensor with high accuracy and efficiency.

关键词： Star sensor whale optimization algorithm levenberg-Marquardt algorithm calibration high accuracy

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