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Time-Optimized Robot Trajectory Based on improved whale optimization algorithm in Intelligent Manufacturing

JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第8期24卷 084502-084502页

作者： Liu, Jie Nanjing Vocat Univ Ind Technol Jiangsu Prov Precis Mfg Engn andTechnol Res Ctr Sch Mech Engn 1 Yangshan North Rd Nanjing 210023 Jiangsu Peoples R China

To discuss the low convergence accuracy of whale optimization algorithm (WOA) and the problem of converging to local optima, we proposed using nonlinear convergence factors and introducing nonlinear inertia weights in the WOA. The modified WOA was used to optimize the trajectory of a six-degrees-of-freedom (6DOF) industrial robot. To improve the convergence accuracy and the local and global search ability of the WOA, we first replaced the convergence factor with a nonlinear convergence factor and added a nonlinear inertia weight. The algorithm was used along with a quintic polynomial equation to develop a time-optimal trajectory, for the robot, for use in practical application scenarios. Simulation experiment results showed that the duration of a complete loading-unloading process was reduced by 30% after robot motion trajectory optimization compared with that before optimization, indicating the effectiveness of the improved WOA and its suitability for robot trajectory optimization.

关键词： improved whale optimization algorithm trajectory optimization convergence factor inertia weight quintic polynomial equation advanced computing infrastructure computer-aided manufacturing engineering informatics manufacturing planning qualification verification and validation of computational models

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Short-term natural gas consumption prediction based on Volterra adaptive filter and improved whale optimization algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 87卷 103323-000页

作者： Qiao, Weibiao Yang, Zhe Kang, Zhangyang Pan, Zhen North China Univ Water Resources & Elect Power Sch Environm & Municipal Engn 136 Jinshui East Rd Zhengzhou 450046 Peoples R China Sinopec Petr Engn Zhongyuan Co Ltd Zhengzhou 450046 Peoples R China Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England Liaoning Shihua Univ Coll Petr & Nat Gas Engn Fushun 113001 Peoples R China

Short-term natural gas consumption prediction is an important indicator of natural gas pipeline network planning and design, which is of great significance. The purpose of this study is to propose a novel hybrid forecast model in view of the Volterra adaptive filter and an improved whale optimization algorithm to predict the short-term natural gas consumption. Firstly, Gauss smoothing and C-C method is adopted to pretreat and reconstruct short-term natural gas consumption time series;secondly, to improve the performance of whale optimization algorithm, adaptive search-surround mechanism and spiral position and jumping behavior are introduced into it;Thirdly, Volterra adaptive filter is used to predict the short-term natural gas consumption, and the important parameters (e.g. embedding dimension) is optimized by improved whale optimization algorithm. Finally, an actual example is given to test the performance of the developed prediction model. The results indicate that (1) short-term natural gas consumption time series has chaotic characteristics;(2) performance of the improved whale optimization algorithm is better than some comparative algorithms (i.e. cuckoo optimization algorithm, etc. ) based on the different evaluation indicators;(3) exploration factor is the main operational factor;(4) the performance of the proposed prediction model is better than some advanced prediction models (e.g. back propagation neural network). It can be concluded that such an innovative hybrid prediction model may provide a reference for natural gas companies to achieve intelligent scheduling.

关键词： Short-term natural gas consumption Chaotic character recognition Phase space reconstruction Volterra adaptive filter improved whale optimization algorithm Forecast

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APPLICATION OF improved whale optimization algorithm IN MULTI-RESOURCE ALLOCATION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2019年第3期15卷 1049-1066页

作者： Wang, Zhao Deng, Haojiang Zhu, Xiaoyong Hu, Linlin Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr 21 North 4th Ring Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn 19 A Yuquan Rd Beijing 100049 Peoples R China

In this paper, we propose an improved whale optimization algorithm (IWOA) to improve the performance of WOA in three aspects. First, nonlinearly changed convergence factor is introduced to preferably adjust exploration and exploitation process. Then, inspired by the inertia weight factor of Particle Swarm optimization (PSO), we add new inertia weight factor to further enhance the exploitation and exploration ability of WOA. Finally, random variation of current optimal individual is conducted during exploitation iterations to reduce the possibility of falling into local optimum. The IWOA is benchmarked on 29 well-known test functions and the results are verified by comparing IWOA with basic WOA, Grey Wolf optimization (GWO) and Particle Swarm optimization (PSO). We also apply the proposed IWOA to multi-resource allocation problem in resource-constrained embedded system. The results demonstrate that the proposed IWOA performs much better than WOA, GWO or PSO on most test functions and provides best performance in multi-resource allocation problem.

关键词： improved whale optimization algorithm Nonlinear convergence factor Inertia weight factor Random variation Multi-resource allocation

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Quantity sensitive task allocation based on improved whale optimization algorithm in crowdsensing system

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第20期35卷

作者： Jiang, Weijin Zhang, Wanqing Chen, Pingping Chen, Junpeng Sun, Yongxia Yang, Ying Hunan Univ Technol & Business Key Lab Hunan Prov New Retail Virtual Real Techno Changsha 410205 Peoples R China

With the large-scale popularity of mobile terminals, crowdsensing technology gradually replaces the existing static sensors with its advantages of high efficiency and low cost, becoming an emerging data collection method. How to assign perception tasks to the best performing users under the premise of ensuring quality and reducing costs to maximize the number of user tasks completed is the focus of the research on quantity sensitive task allocation. Based on this, a solution based on the improved whale optimization algorithm that combines the three operations of nonlinear decreasing convergence factor, optimal local jitter, and dynamic position update is put forward, which is used to solve the proposed task allocation problem. First, modeling the quantity sensitive task allocation problem, and then defining the spatial matching degree and skill matching degree according to the degree of adaptation between users and tasks. Taking into account the user's learning ability during the user's task execution, the skill update mechanism is introduced to update the user's existing skills in a timely manner, so as to improve task allocation effectiveness. Second, comprehensively considering the budget, the user's online time and the perceived task completion quality, and reasonably defining the task allocation problem that maximizes the number of tasks completed. In addition, from the perspective of selecting the best performing user for the task, designing a user selection strategy based on user's priority to reduce the cost of task allocation while ensuring the quality of the perceived task is basically completed. Then, in the process of solving the optimal task allocation plan, the improved algorithm is used to continuously optimize the initial task sequences of each iteration, and the final result can be obtained after a limited number of iterations. Finally, the improved algorithm is compared with other optimization algorithms in the same environment, and the results sho

关键词： quantity sensitive task allocation improved whale optimization algorithm skill update mechanism priority

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Research on Wind Power Consumption Dispatching Based on improved whale optimization algorithm

Research on Wind Power Consumption Dispatching Based on Impr...

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2019中国智能自动化大会(CIAC 2019)

作者： Hexu Sun Wei Zhang Zhaoming Lei Hebei University of Technology

Aiming at the current situation of high wind-abandonment rate and wind power consumption obstruction in China's wind power *** paper proposes a new scheduling mode,which is to use high-energy load as schedulable resource and participate in system scheduling with conventional power supply,and establishes a consumption model that aims to maximize wind power consumption and reduce total system scheduling *** model is solved using the improved whale optimization algorithm(IWOA),and the control parameter decrement strategy and random differential mutation perturbation strategy are proposed to enhance the algorithm search speed and convergence *** effectiveness and superiority of the scheduling model and IWOA are verified by simulation experiments.

关键词： Consumption of wind power Load and power coordination High-energy load improved whale optimization algorithm

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Maximum power point tracking control of photovoltaic systems using a hybrid improved whale particle swarm optimization algorithm

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 1789-1803页

作者： Zhang, Lianghao Wang, Shuang Ni, Zihao Li, Fashe Kunming Univ Sci & Technol Yunnan Key Lab Clean Energy & Energy Storage Techn Kunming Peoples R China Kunming Univ Sci & Technol State Key Lab Complex Nonferrous Met Resources Cle Kunming Peoples R China Kunming Univ Sci & Technol Fac Met & Energy Engn 68 Wenchang Rd121 St Kunming Yunnan 650093 Peoples R China

Maximum power point tracking (MPPT) control techniques are commonly employed to maximize the benefits and enhance the operational efficiency of photovoltaic systems in challenging outdoor environments. However, when the photovoltaic array is subjected to partial shading conditions, the conventional MPPT methods exhibit inadequate global maximum power point (GMPP) tracking performance. To this end, in this study, an effective MPPT control method for photovoltaic systems is proposed based on a hybrid improved whale particle swarm optimization algorithm (IWPOA). The control method integrates the advantages of the improved whale optimization algorithm (IWOA) and the Particle Swarm algorithm (PSO) with additional control and stochastic elements. Validation analysis under a variety of conditions reveals that IWPOA significantly improves MPPT, achieving a tracking speed of approximately 0.13 s, which represents a 18.75-53.57% enhancement over other comparative algorithms. The mean tracking accuracy of IWPOA is 99.21%, and it offers superior tracking stability, making it suitable for diverse applications in MPPT.

关键词： improved whale optimization algorithm maximum power point tracking partial shading conditions particle swarm algorithm photovoltaic array

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Multi-objective optimization of Antenna Based on improved WOA-BP Neural Network 5th

Multi-objective Optimization of Antenna Based on Improved WO...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhuang, Huawei Sun, Fangzhen Li, Gaoqi Liu, Jianzhao Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250101 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

During the design of multi-objective antennas, optimizing efficiency and computing expenses are key considerations. In this essay, a rapid antenna optimization strategy that combines the BP neural network with the non-dominated sorting genetic algorithm II is proposed. Firstly, to enhance the global optimization capability, we improve the whale optimization algorithm by improving population initialization, incorporating a nonlinear convergence factor, and introducing adaptive inertia weights. Then, using the IWOA, we optimize the BPNN's initial weights and thresholds to improve model accuracy. Finally, we present a Pareto-optimal three-band antenna optimization, demonstrating that the proposed method can effectively optimize antenna performance while significantly reducing computational cost.

关键词： Antenna optimization BP neural network improved whale optimization algorithm Pareto-optimal

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improved neighborhood search whale optimization algorithm and its engineering application

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SOFT COMPUTING 2023年第23期27卷 17687-17709页

作者： Wei, Fengtao Li, Junyu Zhang, Yangyang Xian Univ Technol Sch Mech & Instrumental Engn Xian 710048 Peoples R China

In order to solve the problems of insufficient optimization accuracy, slow convergence speed and easy to fall into local optimum in the whale optimization algorithm, this paper proposes a whale optimization algorithm with improved neighborhood search strategy. First, the algorithm generates a more evenly distributed and higher-quality initial population through an initialization strategy based on the opposite-based learning of pinhole imaging to expand the early search space of the algorithm. Secondly, it adopts improved neighborhood search strategy based on similarity and uses Mahalanobis distance and the law of universal gravitation to calculate and rank the similarity of solutions. At the same time, the algorithm counts the times of convergence oscillation. According to the algorithm iteration process, it selects the corresponding similarity ranking solution as the object to update the position and performs the second position update for the solution with most times of oscillation, so as to implement the space exploration of the target population to speed up the convergence of the algorithm and enhance the ability to jump out of the local optimum. Finally, an adaptive step size adjustment strategy is introduced, and the population convergence is adjusted using adaptive step size parameters according to the algorithm optimization process to improve the algorithm's global search performance and avoid premature convergence of the algorithm. The improved algorithm proposed in this paper is analyzed and compared with the sine-cosine optimization algorithm, artificial bee colony algorithm and three improved whale algorithms on a set of 20 test functions in low-dimensional and high-dimensional, respectively, and perform ANOVA and T-test on the simulation results. The results show that the improved algorithm proposed in this paper effectively improves the convergence accuracy and convergence speed. In addition, the improved optimization algorithm proposed in this paper i

关键词： improved whale optimization algorithm Pinhole imaging opposition-based learning strategy improved neighborhood search strategy Adaptive step size adjustment strategy Numerical simulation analysis Engineering application

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improved prediction model of energy performance for variable-speed pumps-as-turbines (PATs) in micro-hydropower schemes

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JOURNAL OF ENERGY STORAGE 2025年 120卷

作者： Zhou, Peijian Gu, Yangfan Yu, Wenjin Wu, Yanzhao Yao, Zhifeng Mou, Jiegang China Jiliang Univ Coll Metrol Measurement & Instrument Hangzhou 310018 Peoples R China China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China

The variable speed regulation in pumps-as-turbines (PATs) plays a pivotal role in micro-pumped hydro energy storage (MPHES) systems by expanding operational parameters, optimizing energy conversion efficiency, and augmenting system adaptability through enhanced dynamic response characteristics. While existing studies have proposed empirical formulas, these models are often limited to optimal operating points under specific conditions, leaving their accuracy and applicability under broader variable speed conditions insufficiently validated. This paper presents an improved prediction model for the energy performance of variable-speed PATs, integrating turbine theoretical models with composition analysis method to modify the affinity laws. The model utilizes a segmented analysis approach that classifies the affinity laws into three categories based on speed ratios, ensuring more accurate predictions across diverse operating conditions. An improved whale optimization algorithm is employed to systematically refine the coefficients of the modified affinity laws. The most optimal modification is selected through a comparison of R2 values for each model. The proposed model demonstrates a significant enhancement in prediction accuracy, particularly for low and high speed ratios, with an average improvement of 10 % over existing models. This work addresses critical gaps in current PAT energy performance prediction methods and provides a more robust framework for optimizing energy efficiency in micro-hydropower systems.

关键词： Variable speed regulation Pumps-as-turbines Energy performance prediction Modified affinity laws improved whale optimization algorithm

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A deep learning model coupled with metaheuristic optimization for urban rainfall prediction

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JOURNAL OF HYDROLOGY 2025年 651卷

作者： Zhao, Weiguo Zhang, Zhenxing Khodadadi, Nima Wang, Liying Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056038 Hebei Peoples R China Univ Illinois Prairie Res Inst Illinois State Water Survey Champaign IL 61820 USA Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA Univ Wollongong Sch Elect Comp & Telecommun Engn Wollongong NSW 2522 Australia

Accurate urban rainfall prediction is crucial for water resource management, flood defense and early warning, and disaster reduction. Leveraging deep learning allows us to establish effective data-driven forecasting models that can facilitate timely flood warnings, optimize water resource allocation, and minimize the impacts of natural disasters such as waterlogging. To address the challenges of urban rainfall prediction, this paper develops an innovative ensemble deep learning model that integrates convolutional neural network (CNN), long short-term memory network (LSTM), and a self-attention mechanism, enhanced by an improved whale optimization algorithm (IWOA). This approach utilizes historical rainfall sequences as inputs to predict future rainfall, thereby simplifying the prediction model's complexity. The CNN effectively extracts invariant and hidden features from the rainfall data, while the LSTM captures long-term dependencies within the rainfall sequences. Meanwhile, the self-attention mechanism is employed to further capture internal correlations within the rainfall sequence. To rapidly acquire the optimum hyperparameters for the prediction model, the IWOA algorithm incorporates three strategies, including the double random opposition-based learning (DROBL) strategy, the crosswise intersection strategy, and the local search strategy. The proposed DL-IWOA model has been validated for daily and monthly rainfall in Guangzhou City, China, and benchmarked against five other forecasting models. The results demonstrate the model's superior performance, achieving a NSE value of 0.83 for daily rainfall prediction, with a d-factor of 0.414 and a p-factor of 0.950. For monthly rainfall prediction, the model attains a NSE value of 0.85, with a d-factor of 0.827 and a p-factor of 0.883. By conducting rainfall predictions across multiple sites and comparing the DL-IWOA model's results with those of five other models, it is found that the DL-IWOA model not only delivers

关键词： Machine learning Precipitation prediction CNN LSTM improved whale optimization algorithm

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