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Systems and Soft Computing 2025年 7卷

作者： Jinge Wang College of Humanities Guangxi Arts University Nanning 530000 China

With the exponential growth of generative art, existing evaluation methodologies are falling short in terms of precision. To address this, the present study enhances the whale optimization algorithm through the incorporation of swarm-based mechanisms and adaptive weight parameters. Subsequently, it amalgamates this improved algorithm with the sparrow search algorithm to fine-tune the parameters of the support vector machine (SVM). A new generative art evaluation model based on this hybrid algorithm is proposed. Experimental results show that the improved whale algorithm outperforms comparative algorithms, reaching optimal values after 190 iterations in uni-modal and 210 iterations in multi-modal functions. The model also performs well in fitness tests and evaluates AI-generated Paper Cuttings with a mean square error of 0.0101 and a fitting coefficient of 0.987. The proposed model achieves an average evaluation accuracy of 91.3%, outperforming other models in detail refinement, diversity, complexity, and ornamental value. The study offers a highly precise and reliable approach to generative art evaluation.

关键词： whale optimization algorithm Sparrow search algorithm Support vector machine Generative art Evaluation Artificial intelligence

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Using a Type-2 Neural Fuzzy Controller for Navigation Control of Evolutionary Robots

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SENSORS AND MATERIALS 2019年第9期31卷 2735-2751页

作者： Lin, Cheng-Jian Jhang, Jyun-Yu Young, Kuu-Young Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan Natl Chiao Tung Univ Inst Elect & Control Engn Hsinchu 300 Taiwan

In this paper, we present an effective navigation control method for mobile robots in an unknown environment. The proposed behavior manager (BM) switches between two behavioral control patterns, wall-following behavior (WFB) and toward-goal behavior (TGB), on the basis of the relationship between the mobile robot and the unknown environment. A type-2 neural fuzzy controller (T2NFC) with an improved whale optimization algorithm (IWOA) is proposed to provide WFB control and obstacle avoidance for mobile robots. In the WFB learning process, the input signal of a controller is the distance between the wall and the sonar sensors, and its output signal is the speed of two wheels of a mobile robot. A fitness function, which operates on the total distance traveled by the mobile robot, distance from the side wall, angle to the side wall, and moving speed, evaluates the WFB performance of the mobile robot. Experimental results reveal that the proposed IWOA is superior to other methods of WFB and navigation control.

关键词： mobile robot type-2 fuzzy neural controller whale optimization algorithm navigation control wall-following control

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Characterizing nonlinear oscillation behavior of an MRF variable rotational stiffness device

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SMART STRUCTURES AND SYSTEMS 2019年第3期24卷 303-317页

作者： Yu, Yang Li, Yancheng Li, Jianchun Gu, Xiaoyu Univ Technol Sydney Sch Civil & Environm Engn Ctr Built Infrastruct Res 15 Broadway Ultimo NSW 2007 Australia

Magneto-rheological fluid (MRF) rotatory dampers are normally used for controlling the constant rotation of machines and engines. In this research, such a device is proposed to act as variable stiffness device to alleviate the rotational oscillation existing in the many engineering applications, such as motor. Under such thought, the main purpose of this work is to characterize the nonlinear torque-angular displacement/angular velocity responses of an MRF based variable stiffness device in oscillatory motion. A rotational hysteresis model, consisting of a rotatory spring, a rotatory viscous damping element and an error function-based hysteresis element, is proposed, which is capable of describing the unique dynamical characteristics of this smart device. To estimate the optimal model parameters, a modified whale optimization algorithm (MWOA) is employed on the captured experimental data of torque, angular displacement and angular velocity under various excitation conditions. In MWOA, a nonlinear algorithm parameter updating mechanism is adopted to replace the traditional linear one, enhancing the global search ability initially and the local search ability at the later stage of the algorithm evolution. Additionally, the immune operation is introduced in the whale individual selection, improving the identification accuracy of solution. Finally, the dynamic testing results are used to validate the performance of the proposed model and the effectiveness of the proposed optimization algorithm.

关键词： magneto-rheological fluid variable stiffness device (VSD) rotational hysteresis model parameter identification whale optimization algorithm

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Study on sustainable development of power transmission system under ice disaster based on a new security early warning model

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JOURNAL OF CLEANER PRODUCTION 2019年第0期228卷 175-184页

作者： Wang, Weijun Peng, Weisong Tong, Lin Tan, Xichong Xin, Tan North China Elect Power Univ Dept Business Adm 689 Huadian Rd Baoding 071000 Peoples R China China Xiongan Grp Urban Dev Investment Co Ltd 2 Rongmei Rd Baoding 077000 Peoples R China Hebei Power Transmiss & Transformat Co 4 Biandian Rd Shijiazhuang 050000 Hebei Peoples R China China Energy Engn Grp Jiangsu Power Design Inst C Nanjing 211102 Jiangsu Peoples R China

As the ecological climate deteriorates, common disasters including typhoons, earthquakes and ice disasters occur frequently in southern China. In particular, ice disasters can make severe ice coating on power line, resulting in power outage accidents. Not only do power supply disruptions cause major economic losses, but also they threaten people's lives in extremely cold conditions. Therefore, studying on the security early warning of ice coating damage to power line is significant and urgent. This paper adopts grey correlation analysis (GRA) to obtain 16 influence factors, of which the correlation degrees are over 0.74. In order to reduce the internal relevance among the impact factors, 5 common factors are extracted as the predictive model input values through factor analysis. With the input weight and hidden layer threshold optimized, a new extreme learning machine based on the adaptive whale optimization algorithm improved by chaotic sine cosine operator (CSCWOA-ELM) is established to forecast the ice coating damage to power line in southern China. To verify the accuracy and advancement of the proposed model, real data from the power repair projects under ice disaster are selected for experiments. The simulation results prove that the extraction of common factors can highly improve the prediction accuracy by approximately 27.80%. Compared with the 3 benchmark models, the CSCWOA-ELM model, of which the root mean square error (RMSE) is 0.02341 and the mean absolute percentage error (MAPE) is 1.82175%, shows a better performance in predicting the ice coating damage to power line. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Transmission system Security early warning Factor analysis Extreme learning machine whale optimization algorithm

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Hybrid deep learning and isogeometric analysis for bearing capacity assessment of sand over clay

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Journal of Rock Mechanics and Geotechnical Engineering 2024年

作者： Toan Nguyen-Minh Tram Bui-Ngoc Jim Shiau Tan Nguyen Trung Nguyen-Thoi Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City 700000 Vietnam Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 700000 Vietnam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City 700000 Vietnam School of Engineering University of Southern Queensland Toowoomba QLD 4350 Australia Smart Computing in Civil Engineering Research Group Ton Duc Thang University Ho Chi Minh City 700000 Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City 700000 Vietnam

In this paper, Isogeometric analysis (IGA) is effectively integrated with machine learning (ML) to investigate the bearing capacity of strip footings in layered soil profiles, with a focus on a sand-over-clay configuration. The study begins with the generation of a comprehensive dataset of 10,000 samples from IGA upper bound (UB) limit analyses, facilitating an in-depth examination of various material and geometric conditions. A hybrid deep neural network, specifically the whale optimization algorithm-Deep Neural Network (WOA-DNN), is then employed to utilize these 10,000 outputs for precise bearing capacity predictions. Notably, the WOA-DNN model outperforms conventional ML techniques, offering a robust and accurate prediction tool. This innovative approach explores a broad range of design parameters, including sand layer depth, load-to-soil unit weight ratio, internal friction angle, cohesion, and footing roughness. A detailed analysis of the dataset reveals the significant influence of these parameters on bearing capacity, providing valuable insights for practical foundation design. This research demonstrates the usefulness of data-driven techniques in optimizing the design of shallow foundations within layered soil profiles, marking a significant stride in geotechnical engineering advancements.

关键词： UB limit analysis Isogeometric analysis (IGA) hybrid deep neural network whale optimization algorithm

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Carbon price prediction based on multidimensional association rules and optimized multi-factor LSTM model

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Energy 2025年 329卷

作者： Xinqi Tu Lianlian Fu Qiaoling Wang School of Computer and Information Engineering Jiangxi Agricultural University Nanchang 330045 China Jiangxi Rural Revitalization Research Institute Nanchang 330045 China

Precise forecasting of carbon prices serves as a crucial reference for operational production and investment strategies, which assumes great implications for prompting the healthy evolution of the carbon market. This study puts forward a multi-factor hybrid prediction framework, MARM-WOA-LSTM, which amalgamates three frontier technologies to realize a dependable forecast of carbon prices in Hubei. First, multidimensional association rule mining (MARM) is exploited to quantitatively assess the association and influence degree among eight aspects of factors and carbon prices, after which the filtered high-correlation factors are fed into the forecasting model. Thereafter, the whale optimization algorithm (WOA) is implemented to autonomously search and optimize the hyperparameters of the long short-term memory (LSTM) network, thereby enhancing the forecasting effect further. The results illustrate strong correlations of diverse uncertainty indexes and agricultural prices with carbon prices, where geopolitical risks and corn prices weigh more heavily. Moreover, MARM not only effectively cuts down the count of variables but also markedly augments the predictive accuracy. Relative to the single-factor LSTM model, models without MARM and WOA, as well as other comparative models, the developed system exhibits remarkable performance, thus fully attesting to its broad prospect and practical value in the field of carbon price forecasting.

关键词： Carbon price prediction LSTM Multi-factor Multidimensional association rules whale optimization algorithm

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A new heuristic for multilevel thresholding of images

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 117卷 176-203页

作者： Bohat, Vijay Kumar Arya, K. V. ABV Indian Inst Informat Technol & Management Multimedia & Informat Secur Res Grp Gwalior 474015 India AKTU Inst Engn & Technol Dept Comp Sci & Engn Lucknow 226021 Uttar Pradesh India

Multilevel thresholding of images helps in separating the interesting objects from their background. The quality of separation depends much on the selected threshold values. This paper proposes a novel thresholding (TH) heuristic for multilevel thresholding problem. Further, the proposed TH heuristic is embedded into whale optimization algorithm (WOA), grey wolf optimizer (GWO), particle swarm optimization (PSO) algorithm to develop new algorithms named WOA-TH, GWO-TH, and PSO-TH respectively. The TH heuristic fine-tunes the best solution of employer nature-inspired algorithms for increasing their capability to escape local optimum. To find the optimum thresholds for an image, between-class variance criterion is employed as the fitness function. Experiments have been performed on twenty benchmark test images using six different number of thresholds. The performance of the proposed algorithms is compared with their respective base algorithms. The results demonstrate that the proposed WOA-TH, GWO-TH, and PSO-TH algorithms are superior to the compared algorithms. Moreover, the computational time of WOA, GWO, and PSO is improved through the incorporation of proposed heuristic. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Image segmentation Multilevel thresholding Between-class variance whale optimization algorithm Grey wolf optimizer Particle swarm optimization TH heuristic

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ANN-Based STATCOM Tuning for Performance Enhancement of Combined Wind Farms

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第1-2期47卷 10-26页

作者： Rashad, Ahmed Kamel, Salah Jurado, Francisco Abdel-Nasser, Mohamed Mahmoud, Karar Aswan Univ Fac Engn Dept Elect Engn Aswan Egypt Univ Jaen Dept Elect Engn Jaen Spain Chongqing Univ Coll Elect Engn State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China

Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactive power compensation. Nevertheless, the wind farms based on DFIG are expensive compared with the SCIG wind farm, it features by its ability to control the active power independent of reactive power. However, combined wind farm (CWF) has been developed to collect the benefits of SCIG and DFIG wind turbines in the same wind farm. In this article, artificial neural network (ANN) is used to evaluate gain parameters of static synchronous compensator (STATCOM) in order to improve the stability performance of CWF. The impact of tuned STATCOM on the performance of CWF during gust wind speed and during three-phase fault is comprehensively investigated. The performance of CWF with STATCOM tuned by ANN is compared with its performance when the STATCOM tuned by the multiobjective genetic algorithm (MOGA) and whale optimization algorithm (WOA). The results show that the performance of CWF can be enhanced using STATCOM tuned by ANN more than MOGA and WOA.

关键词： squirrel cage induction generators doubly fed induction generators combined wind farm STATCOM artificial neural network multiobjective genetic algorithm whale optimization algorithm

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Model for Predicting NOx Emission from Boilers Based on MWOA-LSSVM Integration

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JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 2019年第8期52卷 702-709页

作者： Zhen, Chengang Liu, Huaiyuan North China Elect Power Univ Sch Control & Comp Engn Baoding 071000 Peoples R China

Accurate, reliable prediction of NOx emission in flue gas is of great significance for operation of power station boilers with low nitrogen emissions. To improve the accuracy of a prediction model, a method for predicting NOx emission from boilers based on integration of the whale optimization algorithm and least squares support vector machine (MWOA-LSSVM) is proposed in this paper. First, the sample space is divided, and a segmentation logistic chaotic map is then used to initialize the population. The nonlinear adaptive parameters are improved, and quadratic interpolation update position is used to improve the whale optimization algorithm (WOA) by broadening the global exploration ability of the algorithm. The MWOA is used to globally optimize the kernel function width and penalty factor of the LSSVM sub-model in each subspace, yielding the sub-model as an output. Finally, the sub-model output is integrated using the least squares regression, yielding the output from the integrated model. The simulation results show that the MWOA-LSSVM integrated model has stable, high-precision simulation performance compared with other selected prediction models and can provide more accurate predictions of NOx emissions from boilers.

关键词： NOx Emission whale optimization algorithm LSSVM Parameters optimization Integrated Model

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Scheduling hybrid flowshop with sequence-dependent setup times and due windows to minimize total weighted earliness and tardiness

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 135卷 780-792页

作者： Khare, Ankit Agrawal, Sunil Indian Inst Informat Technol Design & Mfg PDPM Jabalpur 482005 Madhya Pradesh India

This paper studies scheduling of hybrid flowshop with sequence-dependent setup times to minimize total weighted earliness and tardiness. Due dates are usually intervals in real life therefore due windows are considered in this paper. Since the considered problem is NP-hard, three population based hybrid metaheuristics are presented, namely: Hybrid squirrel search algorithm (HSSA), opposition based whale optimization algorithm (OBWOA), and discrete grey wolf optimization (DGWO). We utilized some effective and advanced technologies, including variable neighbourhood search, hybrid local search and opposition based learning to enhance the performance of the presented algorithms. The parameters of the presented algorithms are calibrated using design of experiments approach. To evaluate the performance of the presented algorithms, we compare them against five other well-known meta-heuristics on 240 randomly generated problems. The statistically sound results demonstrate that the presented algorithms are very competitive for the considered problem.

关键词： Hybrid flowshops scheduling Total weighted earliness and tardiness Due windows Squirrel search algorithm whale optimization algorithm Grey wolf optimization

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