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Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

JOURNAL OF VIBROENGINEERING 2024年第4期26卷 936-947页

作者： Han, Fang Liu, Yingjie Peng, Wen Weifang Univ Sch Machinery & Automat Weifang 261061 Shandong Peoples R China Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Peoples R China

In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in whale optimization algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

关键词： vehicle dynamics path tracking optimal control whale optimization algorithm

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An elitist whale optimization algorithm with the nonlinear parameter: algorithm and application

ENGINEERING REPORTS

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ENGINEERING REPORTS 2024年第10期6卷

作者： Zhang, Yajing Zhang, Guoxu Tangshan Coll Key Lab Intelligent Data Informat Proc & Control H Tangshan Peoples R China Tangshan Coll Key Lab Intelligent Mot Control Syst Tangshan City Tangshan Peoples R China Tangshan Coll Key Lab Intelligent Data Informat Proc & Control H 9 Daxue West Rd Tangshan 063000 Hebei Peoples R China

To address the problem that the whale optimization algorithm tends to fall into the local optimum and fails to maintain a balance between exploration and exploitation, an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed in this paper. An elitist strategy based on the random Cauchy mutation is used in the shrinking encircling mechanism to increase the chance of escaping the local optimum. Cleverly, the strategy is to generate mutation solutions based on the random Cauchy mutation, after which the better population is selected to proceed to the next iteration. Then, a nonlinear parameter is used in the logarithmic spiral mechanism to balance exploration and exploitation. Various numerical optimization experiments are performed based on the IEEE CEC2020 benchmark suite and compared with eleven other algorithms. The results show that EWOANP outperforms most competitors in numerical optimization. Finally, the backpropagation neural network is optimized by EWOANP to build a prediction model for the sulfur content in the molten iron. The experimental results based on production data indicate that the proposed prediction model has a relatively small fluctuation in errors. Compared to the other seven competitors, the proposed model has a better prediction performance with RMSE=0.001457$$ RMSE=0.001457 $$ and R2$$ {R}<^>2 $$=0.916619. 1. An elitist whale optimization algorithm with the nonlinear parameter (EWOANP) is proposed. 2. The performance of proposed EWOANP is validated on several benchmark functions. 3. The EWOANP is applied to establish a prediction model of sulfur content in molten iron. image

关键词： backpropagation neural network elitist strategy sulfur content whale optimization algorithm

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FSRW: fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Xu, Hui Liu, Wei-dong Li, Lu Yao, Deng-ju Ma, Lin Heilongjiang Univ Chinese Med Harbin 150040 Peoples R China Heilongjiang Prov Big Data Ctr Govt Affairs Harbin 150028 Peoples R China Harbin Univ Sci & Technol Harbin 150006 Peoples R China

The Internet of Things (IoT) is an extensive system of interrelated devices equipped with sensors to monitor and track real world objects, spanning several verticals, covering many different industries. The IoT's promise is capturing interest as its value in healthcare continues to grow, as it can overlay on top of challenges dealing with the rising burden of chronic disease management and an aging population. To address difficulties associated with IoT-enabled healthcare, we propose a secure routing protocol that combines a fuzzy logic system and the whale optimization algorithm (WOA) hierarchically. The suggested method consists of two primary approaches: the fuzzy trust strategy and the WOA-inspired clustering methodology. The first methodology plays a critical role in determining the trustworthiness of connected IoT equipment. Furthermore, a WOA-based clustering framework is implemented. A fitness function assesses the likelihood of IoT devices acting as cluster heads. This formula considers factors such as centrality, range of communication, hop count, remaining energy, and trustworthiness. Compared with other algorithms, the proposed method outperformed them in terms of network lifespan, energy usage, and packet delivery ratio by 47%, 58%, and 17.7%, respectively.

关键词： Internet of Things Healthcare Routing Fuzzy logic whale optimization algorithm

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Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

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SCIENTIFIC REPORTS 2024年第1期14卷 1-29页

作者： Wang, Zhen Zhao, Dong Heidari, Ali Asghar Chen, Yi Chen, Huiling Liang, Guoxi Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

关键词： COVID-19 X-ray Multi-threshold image segmentation Swarm intelligence whale optimization algorithm

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Multi-objective unmanned vehicle path planning based on whale optimization algorithm

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2024年第0期

作者： Zong, Xinlu Xia, Xue Chen, Zexi Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Xiaomi Technol Wuhan Co Ltd Wuhan 430068 Peoples R China

Path planning is an important part of the research field of mobile robots. How to plan more suitable paths more quickly has become a challenging optimization problem that requires consideration of several constraints and performance metrics. Given the multitude of optimization objectives and the intricate nature of addressing the static path planning problem, it is usually converted into a single-objective optimization problem. However, this approach would lead to the algorithm being unable to thoroughly explore the solution space. To address this problem, a nondominated sorting whale optimization algorithm based on crowding degree and memory (NSWOACDM) is presented and applied to unmanned vehicle path planning in this paper. Both the length and curvature of path are optimized simultaneously. Experimental results on benchmark functions show that the proposed algorithm is more effective compared with other traditional multi-objective algorithms. The effects of different control points on the optimization results are analyzed through path planning experiments. The experimental results demonstrate that the NSWOACDM algorithm exhibits superior performance in comparison to similar algorithms. Furthermore, it is capable of identifying several viable paths within a reduced timeframe in the context of the multi-objective path planning issue.

关键词： Path planning whale optimization algorithm multi-objective optimization collision-free path

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A new recommendation system using map-reduce-based tournament empowered whale optimization algorithm

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COMPLEX & INTELLIGENT SYSTEMS 2021年第1期7卷 297-309页

作者： Tripathi, Ashish Kumar Mittal, Himanshu Saxena, Pranav Gupta, Siddharth Malviya Natl Inst Technol Jaipur Rajasthan India Jaypee Inst Informat Technol Noida India

In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.

关键词： Recommendation system Big data Map-reduce Clustering whale optimization algorithm

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optimization of reservoir operation at Klang Gate Dam utilizing a whale optimization algorithm and a Levy flight and distribution enhancement technique

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2021年第1期15卷 1682-1702页

作者： Lai, V Huang, Y. F. Koo, C. H. Ahmed, Ali Najah El-Shafie, Ahmed Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Dept Civil Engn Kajang Selangor Malaysia Univ Tenaga Nasl UNITEN Inst Engn Infrastruct IEI Kajang Selangor Malaysia Univ Malaya Fac Engn Dept Civil Engn Kuala Lumpur Malaysia United Arab Emirates Univ Natl Water & Energy Ctr Al Ain U Arab Emirates

Development, operations and management of multi-objective reservoirs, is vital for timely water supply. Optimisation studies were done at the Klang Gate Dam (KGD) utilising standard optimisation and dynamic programming;according to the technology then. Taking it further, the KGD was studied using the nature-inspired meta-heuristic algorithms (MHAs). The whale Optimisation algorithm (WOA) solves complex technical issues. The Levy flight and distribution (LFWOA) was incorporated to increase productivity at the KGD. The aim of this study to minimise KGD's water deficit with WOA and LFWOA. The study comprises of two sections. The first section examined observed monthly inflow, demand, and storage data from 2001 to 2019, whilst the second compares performances to established MHAs, from 1097 to 2008. In the first section, LFWOA and WOA reliability were 60.53 and 58.33 %,respectively. For 2010, both methods gave similar vulnerability value. The LFWOA scored 1.04 while the WOA scored 1. In second section, the LFWOA satisfied 69.70% of exact demand, while the WOA met 56.06%. LFWOA had attained the least shortage. The LFWOA algorithm was the most robust among the MHAs (1.88). In short, the LFWOA is better on the count of reliability, resilience, and scarcity scores.

关键词： Metaheuristics whale optimization algorithm Levy flight and distribution reservoir operation optimization Klang Gate Dam (KGD)

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Bridge Condition Deterioration Prediction Using the whale optimization algorithm and Extreme Learning Machine

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BUILDINGS 2023年第11期13卷

作者： Jiang, Liming Tang, Qizhi Jiang, Yan Cao, Huaisong Xu, Zhe Chongqing Jiaotong Univ State Key Lab Mt Bridge & Tunnel Engn Chongqing 400074 Peoples R China Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China Chongqing Yuhe Expressway Co Ltd Chongqing 400799 Peoples R China Guangxi Nanbai Expressway Co Ltd Nanning 530029 Peoples R China

To address the problem in model computations and the limited accuracy of current bridge deterioration prediction methods, this paper proposes a novel bridge deterioration prediction meth-od using the whale optimization algorithm and extreme learning machine (WOA-ELM). First, we collected a dataset consisting of 539 sets of bridge inspection data and determined the necessary influencing factors through correlation analysis. Subsequently, the WOA-ELM algorithm was applied to establish a nonlinear mapping relationship between each influencing factor and the bridge condition indicators. Furthermore, the extreme learning machine (ELM), back-propagation neural network (BPNN), decision trees (DT), and support vector machine (SVM) were employed for comparison to validate the superiority of the proposed method. In addition, this paper provides further substantiation of the model's exceptional predictive capabilities across diverse bridge components. The results demonstrate the accurate predictive capability of the proposed method for bridge conditions. Compared with ELM, BPNN, DT, and SVM, the proposed method exhibits significant improvements in predictive accuracy, i.e., the correlation coefficient is increased by 4.1%, 11.4%, 24.5%, and 33.6%, and the root mean square error is reduced by 7.3%, 18.0%, 14.8%, and 18.1%, respectively. Moreover, the proposed method presents considerably enhanced generalization capabilities, resulting in the reduction in mean relative error by 11.6%, 15.3%, 6%, and 16.2%. The proposed method presents a robust framework for proactive bridge maintenance.

关键词： bridge engineering inspection data deterioration condition prediction whale optimization algorithm extreme learning

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Application of Variational Mode Decomposition and whale optimization algorithm to Laser Ultrasonic Signal Denoising

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SENSORS 2023年第1期23卷 354页

作者： Mao, Xing Yang, Quan Wang, Xiaocheng Li, Jingdong Univ Sci & Technol Beijing Inst Engn Technol Beijing 100083 Peoples R China

Laser ultrasound signal echoes are easily drowned out by the surrounding environmental noise in industrial field applications, and it is worthwhile to study methods of retaining the weak ultrasound signal during signal processing. To address this problem, this paper proposes to adopt the parameters optimized by the whale optimization algorithm to the variational mode decomposition (VMD) of laser ultrasound signals. The optimized parameters can avoid the frequency mixing and incomplete noise separation caused by the choice of artificial VMD parameters. The Hausdorff distance is applied in the process of reconstructing the signal to help accurately select the relevant modes and improve the signal-to-noise ratio. Simulation and experimental results show that the proposed method is feasible and effective compared with the other three available denoising methods.

关键词： laser ultrasonic signal denoising variational mode decomposition Hausdorff distance whale optimization algorithm

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A Modified whale optimization algorithm for Pattern Synthesis of Linear Antenna Array

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2021年第5期E104A卷 818-822页

作者： Feng, Wentao Hu, Dexiu Natl Key Lab Sci & Technol Blind Signal Proc Chengdu 610041 86 Sichuan Peoples R China Zhengzhou Inst Informat Sci & Technol Zhengzhou 450002 86 Henan Peoples R China

A modified whale optimization algorithm (MWOA) with dynamic leader selection mechanism and novel population updating procedure is introduced for pattern synthesis of linear antenna array. The current best solution is dynamic changed for each whale agent to overcome premature with local optima in iteration. A hybrid crossover operator is embedded in original algorithm to improve the convergence accuracy of solution. Moreover, the flow of population updating is optimized to balance the exploitation and exploration ability. The modified algorithm is tested on a 28 elements uniform linear antenna array to reduce its side lobe lever and null depth lever. The simulation results show that MWOA algorithm can improve the performance of WOA obviously compared with other algorithms.

关键词： whale optimization algorithm dynamic leader selection null steering sidelobe suppression hybrid crossover

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