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adaptive genetic algorithm-optimized temporal convolutional networks for high-precision ship traffic flow prediction

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EVOLVING SYSTEMS 2025年第1期16卷 1-17页

作者： Li, Yunfan Wang, Qian Kaifeng Univ Sch Finance & Econ Kaifeng 475004 Peoples R China

Timely prediction of Ship Traffic Flow (STF) is essential for managing maritime traffic and preventing congestion. However, existing deep neural network-based STF models often face challenges with hyperparameter selection and limited accuracy improvements. This study introduces a Temporal Convolutional Network (TCN) model optimized by an adaptive genetic algorithm (AGA) to address these issues. The methodology begins with comprehensive data preprocessing, using gate-line-based rules to analyze ship traffic entering and leaving ports, leveraging Automatic Identification System (AIS) data. The AGA-TCN model then employs causally dilated convolutions to capture long-term dependencies and extract frequency domain features, with the AGA dynamically optimizing TCN hyperparameters for specific prediction tasks, resulting in an end-to-end STF prediction framework. AIS data from San Francisco waters, covering the period from June 1, 2022, to December 14, 2022, was used to evaluate the model. The performance of the AGA-TCN model was compared against Particle Swarm Optimization (PSO)-TCN, standard TCN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. These models were chosen for comparison due to their widespread use in time-series prediction tasks, representing a variety of approaches in deep learning and optimization. The experiments demonstrate that the AGA-TCN model outperformed all these models, with improvements in RMSE, MSE, and MAPE of 54.37%, 79.18%, and 27.43%, respectively, over the standard TCN. These results underscore the robustness and high accuracy of the AGA-TCN model in STF prediction, establishing it as a superior approach for this application.

关键词： Temporal convolutional networks adaptive genetic algorithm Ship traffic flow Prediction

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Improving camera parameter estimation using an adaptive genetic algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-15页

作者： Khrouch, Hafsa Mahdaoui, Abdelaaziz Hsaini, Abdellah Marhraoui Merras, Mostafa Chana, Idriss Bouazi, Aziz Moulay Ismail Univ Meknes IMAGE Lab IEVIA Team ESTM BP 3103 Toulal Morocco Moulay Ismail Univ Meknes IMAGE Lab SCIAM Team ESTM BP 3103 Toulal Morocco

In this paper, we propose an adaptive genetic algorithm designed to address the camera calibration problem. This approach facilitates the resolution of a complex optimization challenge. Our objective is to refine the camera calibration results estimated by the analytical method. For this purpose, a study was conducted on the type and probability of crossover, the probability of mutation and on the adaptation of the initialization intervals. This adaptation consists of adjusting the length of the initialization intervals. The main objective is to find an optimal solution for the camera calibration parameters by minimizing the cost function. This function is reformulated from the relationship between the points of the 3D target and their 2D projection in the image. Experimental tests and evaluations were conducted to validate the proposed approach. The results indicate that our algorithm is robust and can achieve very satisfactory calibration results.

关键词： Camera calibration adaptive genetic algorithm Cost function Non-linear optimization

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Underwater polarization de-scattering method with dynamic contrast constraint and adaptive genetic algorithm

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OPTICS AND LASER TECHNOLOGY 2025年 183卷

作者： Li, Yafeng Sun, Xudong Zhang, Jiqing Chen, Yuehan Li, Yudong Fu, Xianping Dalian Maritime Univ Sch Informat Sci & Technol Dalian 116026 Peoples R China

Polarization imaging effectively suppresses backscattering and has attracted widespread attention. However, existing methods struggle to capture the brightest and darkest polarization images and rely on manual selection of background regions to estimate degree of polarization (DoP) of backscattering. To address these challenges, this study proposes an underwater polarization de-scattering method with dynamic contrast constraint and adaptive genetic algorithm. First, we propose an automatic method to compute the brightest and darkest polarization images by taking derivatives of polarization intensity based on the Stokes vector and finding the extreme values to obtain the results directly. Second, we designed dynamic contrast constraint conditions based on image enhancement characteristics and employed an adaptive genetic algorithm to estimate the DoP of backscattering without relying on background regions. Finally, qualitative and quantitative results demonstrate that our method exhibits superior image quality and higher robustness in various turbid water environments, making it suitable not only for underwater targets of different materials but also for underwater media of different turbidity.

关键词： Polarization imaging Underwater polarization de-scattering Dynamic contrast constraint adaptive genetic algorithm

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A novel inverse method for Advanced monitoring of lubrication conditions in sliding bearings through adaptive genetic algorithm

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AIN SHAMS ENGINEERING JOURNAL 2025年第2期16卷

作者： Wu, Zhenpeng Dong, Bowen Nie, Liangyi Kefal, Adnan Hubei Polytech Univ Sch Mech & Elect Engn Huangshi 435003 Peoples R China Hubei Polytech Univ Hubei Key Lab Intelligent Conveying Technol & Devi Huangshi 435003 Peoples R China Dalian Univ Technol Key Lab Solidificat Control & Digital Preparat Tec Dalian 116024 Peoples R China Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkiye

This study introduces an inverse lubrication analysis (ILA) method, a novel approach for simulating the lubrication state of sliding bearings under various load conditions. By integrating experimental pressure data from sliding bearings with an adaptive genetic optimization algorithm, this method precisely calculates the eccentricity, attitude angle, and global pressure distribution of the lubrication film. Unlike traditional forward lubrication analysis (FLA) methods, which indirectly estimate the lubrication film status through loads, the ILA method utilizes direct pressure measurements, ensuring accurate and timely raw data for inverse calculations. This approach rapidly and accurately converts measured data into key parameters, closely aligning simulation results with experimental data. The lubrication states of the experimental sliding bearing under loads of 100 N, 200 N, 300 N, and 400 N were successfully predicted, highlighting the method's reliability in real-world applications. This study provides a new approach and perspective for health monitoring and fault diagnosis of sliding bearings, especially under extreme conditions.

关键词： Inverse lubrication analysis Sliding bearings Hydrodynamic lubrication adaptive genetic algorithm

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Using adaptive genetic algorithm optimize to realize the extended depth-of-field wavefront coding mask for a large field-of-view microscopy system

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OPTICS AND LASERS IN ENGINEERING 2025年 185卷

作者： Zhang, Qinghan Jing, Xudong Sun, Liujie Wei, Xiaoxiao Wan, Xinjun Zheng, Jihong Univ Shanghai Sci & Technol Lab Modern Opt Syst Shanghai 200093 Peoples R China

Wavefront coding technology (WFC) is a computational photography technique that significantly extends a system's depth of field (DOF) while mitigating the impact of aberrations and the environment. A freely inserted wavefront coding microscopy system that realizes high-resolution imaging with large DOF in a wide field of view (FOV) is proposed. On the basis of a designed large DOF microscopy system, an adaptive genetic algorithm is developed to interactively analyze, design, and optimize the phase mask and its effects on the system. The algorithm adaptively adjusts the parameters for selection, crossover, and mutation. Furthermore, it reconstructs the optimization function while considering processability, thereby efficiently and accurately selecting phase mask parameters. The phase mask is prepared and integrated into the system with a freely inserted fixture, which provides flexibility and ease of operation. Wavefront encoding/decoding experiments demonstrate that the system can extend the DOF by approximately +/- 13 times in a large FOV, while slightly decreasing the resolution from 6.20 mu m to 7.81 mu m, and mitigating aberrations and FOV effects. The integrated phase mask does not influence the design of the optical system, is universal, and simplifies the system's structure. This provides advantages in applications such as biological microscopes, endoscopes, and drones, which have strict sample and environmental requirements.

关键词： Wavefront coding Extended depth of field adaptive genetic algorithm Large field of view Microscopy system

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adaptive genetic algorithm Based on Individual Similarity to Solve Multi-Objective Flexible Job-Shop Scheduling Problem

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IEEE ACCESS 2022年 10卷 45748-45758页

作者： Liang, Xu Liu, Yifan Gu, Xiaolin Huang, Ming Guo, Fajun Beijing Informat Sci & Technol Univ Comp Sch Beijing 100192 Peoples R China Dalian Jiaotong Univ Software Technol Inst Dalian 116021 Peoples R China Dalian Jiaotong Univ Sch Comp & Commun Engn Dalian 116021 Peoples R China

Aiming at the coupling of energy consumption and completion time in flexible job-shop scheduling, this paper took makespan and energy consumption as the optimization objectives, established a scheduling model, and proposed a scheduling strategy based on improved genetic algorithm. Firstly, a hybrid initialization method based on global minimum completion time selection and global minimum workload selection is introduced to generate the initial population, and the scale of the initial population is expanded to increase the diversity of the population;Secondly, the generation method of offspring individuals is improved, grouped according to the non-dominated ranking level and crowding degree of individuals in the population, and the self-contained individuals are generated by performing crossover and mutation, neighborhood search simulated annealing and reverse learning crossover mutation operations respectively. Finally, an improved adaptive crossover and mutation operation based on individual similarity is proposed, which is applied to the algorithm to improve the search ability of the algorithm. Relevant experimental results show that the proposed adaptive genetic algorithm based on individual similarity is feasible and effective in flexible job-shop scheduling.

关键词： Statistics Sociology genetic algorithms Energy consumption Search problems Optimization Codes Hybrid initialization adaptive genetic algorithm opposition-based learning adaptive crossover-mutation

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adaptive genetic algorithm with Exploration-Exploitation Tradeoff for Preprocessing Microarray Datasets

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CURRENT BIOINFORMATICS 2017年第5期12卷 441-451页

作者： Rajappan, Sivaraj Rangasamy, DeviPriya Velalar Coll Engn & Technol Dept Comp Sci & Engn Erode Tamil Nadu India Kongu Engn Coll Dept Informat Technol Erode Tamil Nadu India

Background: Microarray gene expression datasets contain huge volume of gene data to be used for cancer analysis but often suffer from "curse of dimensionality" and "missing values". They prevent analysts from extracting right knowledge and often results in instable results. Objective: To address both these issues, the paper proposes a novel algorithm based on genetic algorithm (GA). Method: GA is commonly used for feature selection and treating missing values in microarray datasets. But, it often results in premature convergence due to insufficient exploration and exploitation. In the proposed adaptive genetic algorithm (AGA), genetic parameters are dynamically determined based on the values in current generation in order to improve optimality of the solution. The population is divided into two sub-populations and crossover and mutation are performed in parallel on these sub-populations in order to speed up the execution and also to have modularity in the population for performing these operations. In this paper, the missing values are first imputed using AGA and again AGA is used to select significant features. Results: The proposed methodology is implemented in different real microarray datasets to impute values at different missing proportions and to select prominent features. It is found that the datasets processed with AGA provides better results than the standard methods. Conclusion: AGA can be implemented successfully in all datasets where the number of features is large and missing values are present. AGA preprocesses the datasets and prepares them for better classification.

关键词： Microarray dataset feature selection missing values genetic algorithm classification adaptive genetic algorithm

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adaptive genetic algorithm for solving sugarcane loading stations with multi-facility services problem

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2013年 98卷 85-99页

作者： Neungmatcha, Woraya Sethanan, Kanchana Gen, Mitsuo Theerakulpisut, Somnuk Khon Kaen Univ Fac Engn Res Unit Adv Prod Improvement & Logist Management Dept Ind Engn Khon Kaen 40002 Thailand Fuzzy Log Syst Inst Iizuka Fukuoka 8200067 Japan Khon Kaen Univ Dept Mech Engn Fac Engn Khon Kaen 40002 Thailand

This paper presents a computational tool for operational planning of sugarcane loading stations. The loading stations are used to facilitate the supply of sugarcane to a sugar mill, especially for small-sized sugarcane growers whose fields are located over 30 km away from the sugar mill. The objective of this research is to solve the problems involved in transportation planning and allocation of sugarcane from grower's fields to the loading stations, and from the loading stations to the sugar mill. The decisions consist basically of the determination of proper loading station's locations, the optimal type and number of transloaders in each loading station, and sugarcane field allocation to guarantee a continuous and uniform feeding of sugarcane to the sugar mill. Furthermore, this study is different from that of the general location problem in that it determines suitable types and the number of different multi-facility services (i.e. transloaders) at each of the sugarcane loading stations. In order to solve the problem of sugarcane loading stations with multi-facility services, we apply a mixed-integer programming model that can handle small-scale problems (less than 300 sugarcane fields or nodes). Additionally for large-scale problems, we present a comprehensive decision support system (DSS) with geographical information system (GIS) based on the proposed method, adaptive genetic algorithm (AGA), to solve this problem. Numerical experimental results of the AGA were compared with those obtained from the MPL/CPLEX, traditional genetic algorithm (GA), and the current practices in the sugar mill of our case study. The results demonstrated that the AGA is not only useful for reducing cost when compared to the traditional GA and the current practices, but also for efficient management of a sugarcane supply system. Furthermore, the method of this research should prove beneficial to other similar agro-food sectors in Thailand and around the world. (C) 2013 Elsevier B.V. Al

关键词： Location-allocation problem Multi-facility services adaptive genetic algorithm Sugarcane Loading station Fuzzy logic control

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adaptive genetic algorithm for advanced planning in manufacturing supply chain

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JOURNAL OF INTELLIGENT MANUFACTURING 2006年第4期17卷 509-522页

作者： Moon, Chiung Seo, Yoonho Yun, Youngsu Gen, Mitsuo Korea Univ Dept Ind Syst & Informat Engn Seoul 136713 South Korea Hanyang Univ Dept Informat & Ind Engn Ansan 425791 South Korea Chosun Univ Sch Business Adm Kwangju 501759 South Korea Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Fukuoka Japan

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times. The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach.

关键词： advanced planning manufacturing supply chain scheduling adaptive genetic algorithm

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adaptive genetic algorithm for path planning of loosely coordinated multi-robot manipulators

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Journal of Harbin Institute of Technology(New Series) 2003年第1期10卷 72-76页

作者：高胜赵杰蔡鹤皋 Robot Research Institute Harbin Institute of Technology

adaptive genetic algorithm A SA GA, a novel algorithm, which can dynamically modify the parameters of genetic algorithms in terms of simulated annealing mechanism, is proposed for path planning of loosely coordinated multi robot manipulators. Over the task space of a multi robot, a strategy of decoupled planning is also applied to the evolutionary process, which enables a multi robot to avoid falling into deadlock and calculating of composite C space. Finally, two representative tests are given to validate A SA GA and the strategy of decoupled planning.

关键词： multi robot path planning adaptive genetic algorithm simulated annealing decoupled planning

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