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A Track Segment Association Method Based on heuristic optimization algorithm and Multistage Discrimination

REMOTE SENSING 2025年第3期17卷 500-500页

作者： Chen, Yiming Zhang, Zhikun Zhang, Hui Huang, Weimin Inner Mongolia Univ Coll Elect Informat Engn Hohhot 010021 Peoples R China Inner Mongolia Key Lab Intelligent Commun & Sensin Hohhot 010021 Peoples R China Mem Univ Newfoundland Dept Elect & Comp Engn St John NF A1B 3X5 Canada

The fragmentation of vessel tracks represents a significant challenge in the context of high-frequency surface wave radar (HFSWR) tracking. This paper proposes a new track segment association (TSA) algorithm that integrates optimal tracklet assignment, iterative discrimination, and multi-stage association. This paper reformulates the optimal tracklet assignment task as an optimal state search problem for modeling and solution purposes. To determine whether competing old and new tracklets can be associated, we assume the existence of a public state that represents the correlation between the tracklets. However, due to track fragmentation, this public state remains unknown. We need to search for the optimal public state of all candidate tracklet pairs within a feasible parameter space, using a fitness function value as the evaluation criterion. The old and new tracklets pairs that match the optimal public state are considered optimally associated. Since the solution process involves searching for the optimal state across multiple dimensions, it constitutes a high-dimensional optimization problem. To accomplish this task, the catch fish optimization algorithm (CFOA) is employed for its ability to escape local optima and handle high-dimensional optimization, enhancing the reliability of tracklet assignment. Furthermore, we achieve precise one-to-one associations by assigning new tracklet to old tracklet through the optimal tracklet assignment method we proposed, a process we abbreviate as AN2O, and its inverse process, which assigns old tracklet to new tracklet, abbreviated as AO2N. This dual approach is further complemented by an iterative discrimination mechanism that evaluates unselected tracklets to identify potential associations that may exist. The algorithm effectiveness of the proposed is validated using field experiment data from HFSWR in the Bohai Sea region, demonstrating its capability to accurately process complex tracklet data.

关键词： track segment association optimal tracklet assignment heuristic optimization algorithm multi-target tracking

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Support vector regression with heuristic optimization algorithms for predicting the ground surface displacement induced by EPB shield tunneling

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GONDWANA RESEARCH 2023年 123卷 3-15页

作者： Lu, Dechun Ma, Yiding Kong, Fanchao Guo, Caixia Miao, Jinbo Du, Xiuli Beijing Univ Technol Inst Geotech & Underground Engn Beijing 100124 Peoples R China

Machine learning method with heuristic optimization algorithms is proposed to predict the stratum displacement induced by earth pressure balanced shield tunneling. Support vector regression is used as the machine learning method. Four heuristic intelligent optimization algorithms, namely, genetic algorithm, particle swarm optimization, grey wolf optimizer and sparrow search algorithm, are applied to optimize the two hyperparameters of support vector regression model, namely, penalty factor and bandwidth term. Simulated annealing algorithm is introduced to show the necessity of using heuristic algorithms. Mean square error of k-fold cross validation is considered as the fitness function for optimization algorithms. Normalization method and dummy variables are used for data preprocessing. For 115 samples from field measurement, 92 samples are used as the training set, and 23 samples are used as the test set. Three categories of parameters, namely, shield tunneling parameters, tunnel geometrical parameters and stratum types, are used as input parameters for the proposed method. Correlations among parameters are analyzed by Pearson correlation coefficient. The prediction results show that grey wolf optimizer and sparrow search algorithm are suitable methods for determining hyperparameters of support vector regression due to higher accuracy, efficiency, and stability. (c) 2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

关键词： Support vector regression Determination of hyperparameters heuristic optimization algorithm Ground surface displacement EPB shield tunneling

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Symbiotic mechanism-based honey badger algorithm for continuous optimization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-26页

作者： Xu, Yuefeng Zhong, Rui Cao, Yang Zhang, Chao Yu, Jun Niigata Univ Grad Sch Sci & Technol Niigata Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Japan Univ Toyama Fac Engn Toyama Japan Niigata Univ Inst Sci & Technol Niigata Japan

Honey badger algorithm (HBA) is a novel meta-heuristic algorithm inspired by the foraging behaviors of honey badgers, designed to tackle single-objective continuous optimization problems. While the standard HBA demonstrates strong performance across various optimization problems, it still encounters challenges related to insufficient population diversity, particularly in later stages, hindering its ability to escape local optima. To address these problems, we propose an enhanced variant called the symbiotic mechanism-based HBA (SHBA), which incorporates the cooperative symbiotic mechanism between honey badgers and honey-guide birds. Specifically, it integrates information exchange between two distinct populations and employs multiple strategies to increase population diversity while maintaining efficient search performance. We conducted numerical experiments on CEC2017 and CEC2022 and compared their performance with nine competitive algorithms. Furthermore, the scalability and applicability of the proposed SHBA were assessed across various engineering problems. The statistical results show that the proposed SHBA is significantly competitive, with excellent convergence speed and accuracy in benchmark functions and engineering problems.

关键词： heuristic optimization algorithm Evolutionary computation Honey badger algorithm Symbiotic mechanism

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Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021年第4期31卷 2170-2188页

作者： Das, Manisha Gupta, Deep Radeva, Petia Bakde, Ashwini M. Visvesvaraya Natl Inst Technol Dept Elect & Commun Engn Nagpur Maharashtra India Univ Barcelona Dept Math & Comp Sci Barcelona Spain Comp Vis Ctr Barcelona Spain All India Inst Med Sci Dept Radio Diag Nagpur Maharashtra India

Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.

关键词： CT image fusion meta‐ heuristic optimization algorithm MR multi‐ scale decomposition

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BFGO: Bamboo Forest Growth optimization algorithm

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JOURNAL OF INTERNET TECHNOLOGY 2023年第1期24卷 1-10页

作者： Chu, Shu-Chuan Feng, Qing Zhao, Jia Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang Peoples R China Chaoyang Univ Technol Dept Informat Management Taipei Taiwan

The heuristic optimization algorithm is a popular optimization method for solving optimization problems. However, the main disadvantage of this type of algorithm is the unstable performance, and the performance depends on the specific problem and the designer's experience, so inappropriate algorithms or parameters will lead to poor performance for solving optimization. In this paper, a new meta-heuristic algorithm-bamboo forest growth optimization (BFGO) algorithm is proposed. The BFGO algorithm refers to the growth law of bamboo: first, take root clearly, and then grow wildly. The growth characteristics of bamboo forests and the optimization process of the algorithm are integrated, and the test is carried out on 30 functions of the CEC2017 test set, and the algorithm is applied to four classical problems of engineering optimization. Compared has better performance.

关键词： heuristic optimization algorithm Bamboo forest growth optimization algorithm Engineering optimization

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On-Ramp Merging Strategy with Two-Stage optimization Based on Fully Proactive and Cooperative Merging of Vehicles

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2023年第4期149卷

作者： Wang, ShiHui Zhao, Min Sun, DiHua Zou, Yuanpeng Chongqing Univ Key Lab Dependable Serv Comp Cyber Phys Soc Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Coll Automat 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Sch Foreign Languages & Cultures 174 Shazheng St Chongqing 400044 Peoples R China

The on-ramp area has been a hot spot for research as a bottleneck area. In this paper, an on-ramp cooperative merging control strategy is proposed to make full use of active cooperative vehicles, and alleviate the negative influence of merging on the main road. First, a two-stage framework is proposed to illustrate the merging strategy. At the vehicle level, the cooperative merging controllers of vehicles are designed based on an optimization algorithm that considers the penalty function. At the traffic level, the cooperative distance of the upstream and downstream vehicles at the main road target merging position are optimized to improve merging efficiency. Meanwhile, to ensure the implementation of the proposed strategy in mixed traffic, we propose a method to determine the lead vehicles based on the proximity principle. Numerical experiments were performed to verify the fully proactive and cooperative merging (FPCM) strategy, the effectiveness of the vehicle cooperative controllers, the necessity of optimizing the cooperative distance, the effect of different velocities on the main road and ramp, and the effectiveness of determining the lead vehicles. In the case of multivehicle merging, the proposed strategy can improve merging efficiency by 44.42% and save fuel consumption by 39.11%. In the case of a single merging vehicle, the optimized proposed strategy can improve merging efficiency by 48.4% and save fuel consumption by 31.4%. Finally, the simulation experiments for the different maximum speeds of vehicles show that a sufficient speed adjustment range can improve the advantages of the strategy.

关键词： On-ramp merging Fully proactive and cooperative merging (FPCM) strategy Two-stage optimized framework heuristic optimization algorithm Maximum speed

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Application of Binary Slime Mould algorithm for Solving Unit Commitment Problem

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IEEE ACCESS 2023年 11卷 45279-45300页

作者： Rifat, Md. Sayed Hasan Niloy, Md. Ashaduzzaman Rizvi, Mutasim Fuad Ahmed, Ashik Ahshan, Razzaqul Nengroo, Sarvar Hussain Lee, Sangkeum Islamic Univ Technol Dept Elect & Elect Engn Gazipur 1704 Bangladesh Sultan Qaboos Univ SQU Coll Engn Dept Elect & Comp Engn Muscat 123 Oman Korea Adv Inst Sci & Technol KAIST Cho Chun Shik Grad Sch Mobility Daejeon 34141 South Korea Elect & Telecommun Res Inst ETRI Environm ICT Res Sect Daejeon 34129 South Korea

A challenging engineering optimization problem in electrical power generation is the unit commitment problem (UCP). Determining the scheduling for the economic consumption of production assets over a specific period is the premier objective of UCP. This paper presents a take on solving UCP with Binary Slime Mould algorithm (BSMA). SMA is a recently created optimization method that draws inspiration from nature and mimics the vegetative growth of slime mould. A binarized SMA with constraint handling is proposed and implemented to UCP to generate optimal scheduling for available power resources. To test BSMA as a UCP optimizer, IEEE standard generating systems ranging from 10 to 100 units along with IEEE 118-bus system are used, and the results are then compared with existing approaches. The comparison reveals the superiority of BSMA over all the classical and evolutionary approaches and most of the hybridized methods considered in this paper in terms of total cost and convergence characteristics.

关键词： optimization Costs Convergence Fuels Metaheuristics Power generation heuristic algorithms Binary slime mould algorithm (BSMA) heuristic optimization algorithm unit commitment problem (UCP) economic load dispatch (ELD) power system optimization

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Orthogonal Learning Rafflesia optimization algorithm with Quasi-affine Transformation Evolutionary and Its Application in Wireless Sensor Networks 22

Orthogonal Learning Rafflesia Optimization Algorithm with Qu...

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22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Pan, Jeng-Shyang Zhang, Xin-Yi Chu, Shu-Chuan Zhong, Ning Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China Chaoyang Univ Technol Dept Informat Managemen Taichung Taiwan Maebashi Inst Technol Dept Life Sci & Informat Maebashi Gumma Japan Beijing Univ Technol Fac Informat Technol 4 Beijing Peoples R China

ISBN: (纸本)9798350309188

The Rafflesia optimization algorithm (ROA) is an optimization algorithm that mimics the growth cycle of the Rafflesia. Building upon the ROA, this study introduces a novel heuristic algorithm called Orthogonal Learning Quasi-Affine Transformation Evolutionary Rafflesia optimization algorithm (OLQROA). The QUATRE method and the Orthogonal Learning approach are combined in the OLQROA algorithm. Compare OLQROA with ROA algorithm, improved ROA algorithms, and other three mature algorithms using CEC2017 benchmark function. The outcomes of the experiments show that OLQROA works better than the aforementioned algorithms. Additionally, OLQROA is used to apply three-dimensional wireless sensor coverage, producing superior results in comparison to the aforementioned algorithms.

关键词： heuristic optimization algorithm Rafflesia optimization algorithm Orthogonal learning Quasi-affine transformation evolution Wireless sensor networks

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Study on Compression Bearing Capacity of Tapered Concrete-Filled Double-Skin Steel Tubular Members Based on heuristic-algorithm-Optimized Backpropagation Neural Network Model

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BUILDINGS 2024年第11期14卷 3375-3375页

作者： Liu, Xianghong Dangi, Sital Kumar Yang, Zixuan Song, Yinxuan Sun, Qing Wang, Jiantao Xi An Jiao Tong Univ Dept Civil Engn Xian 710049 Peoples R China Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Peoples R China

A tapered concrete-filled double-skin steel tubular (TCFDST) structure has been used as the main framework in transmission towers, offshore facility platforms, and turbine towers owing to its excellent mechanical properties. In order to solve the difficulties of calculating the axial compressive capacity of TCFDST members due to the variations in cross-section, this paper applied heuristic optimization algorithms such as Genetic algorithms (GAs), Particle Swarm optimization (PSO), Simulated Annealing (SA), and Ant Colony optimization (ACO) to enhance a Backpropagation Neural Network (BPNN) model. A predictive model incorporating both global and local optimization strategies for the axial compressive capacity of a TCFDST structure is proposed. A comprehensive axial database for TCFDST members, comprising 1327 sets of experimental and finite element analysis results, was established, with ten types of component dimensions and material parameters selected as input variables and compressive bearing capacity as the output variable. This study developed and assessed four BPNN models, each optimized by a different heuristic algorithm, against various machine learning algorithms and standards. The heuristic-algorithm-optimized BPNN models demonstrated superior accuracy in predicting the axial compressive capacity of TCFDST members. Through parametric analysis, this study identified the relationship between the model's bearing capacity predictions and each input parameter, confirming the model's broad applicability. The optimized BPNN model, refined with heuristic algorithms, provides a significant reference for addressing the computational challenges associated with the load-bearing capacity of TCFDST structures and facilitating their application.

关键词： tapered concrete-filled double-skin steel tubular members compression bearing capacity heuristic optimization algorithm BPNN model machine learning

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Multi-scale collaborative prediction of optimal configuration for carbon fiber woven composites based on deep learning neural networks

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COMPOSITE STRUCTURES 2024年 339卷

作者： Wang, Zefei Zhao, Changcai Yang, Zhuoyun Wang, Keqi Dong, Guojiang Starostenkov, M. D. Yanshan Univ Key Lab Adv Forging & Stamping Technol & Sci Minist Educ China Qinhuangdao 066004 Peoples R China Dalian Univ Technol State Key Lab Struct Anal Optimizat Dalian 116024 Peoples R China Dalian Univ Technol CAE Software Ind Equipment Dalian 116024 Peoples R China Altai State Tech Univ Lenin St 46 Barnaul 656038 Russia

Since carbon fiber woven composite panels contain complex fiber bundle structures, the aim of this paper is to investigate the factors influencing the macro-equivalent properties and topological flexibility of composite panels resulting from variations in fiber bundle configuration. Firstly, the structural topology optimization design of carbon fiber composite plates was achieved using a multi-scale modeling approach with the variable density method. Nonlinear relationships between carbon fiber bundle configuration parameters and macroscopic equivalent properties, as well as macroscale topological flexibility, were determined using deep learning methods, respectively. The maximum error in the predicted values of the deep learning network model does not exceed 0.5% when compared with the results of the finite element calculations. In addition, utilizing deep learning networks to predict macro-equivalent properties of composites can significantly decrease computational time. The impact of varying the number of filaments in fiber bundles and the spacing of the fiber bundles on the flexibility of the topology was determined using a deep learning network. The optimal topological flexibility, along with its corresponding fiber bundle spacing and number of fiber bundle filaments, are globally searched within the design domain using an adaptive genetic algorithm. The study in this paper can provide a reference for designing fiber bundle configurations of composites for practical applications.

关键词： Carbon fiber woven composite panel members Multi-scale structural optimization Deep learning neural network heuristic optimization algorithm

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