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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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heuristic optimization algorithm for Automated Control Design of Bioleaching of Chalcopyrite

Heuristic Optimization Algorithm for Automated Control Desig...

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4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Li, Wei Yang, Aili Liu, Lei North China Elect Power Univ Energy & Environm Res Ctr Beijing Peoples R China Dalhousie Univ Dept Civil Engn Halifax NS Canada

ISBN: (纸本)9781424447138

Microbially assisted recovery of copper from low- grade chalcopyrite has been reported to be a very difficult process, conventional hydrometallurgical methods were limited by many parameters. This study focus on the design and the training of a Multi- Layer Perceptron classifier for the optimized preparation conditions for bioleaching of chalcopyrite. The proposed approach uses the heuristic Backpropagation Neural Network generation and training (BPNN) algorithm to generate the neural network system. The optimization conditions for bioleaching of chalcopyrite in the system were discussed based on the artificial neural network model, in which the input conditions were selected as stirring speed, volume of inoculum, and process pH. The highest Cu dissolution of bioleaching was regarded as the optimization aim, along with constraints of each factor's bounds. The highest copper recovery of 32.1% is obtained at pH 1.5, stirring speed of 140r/min, and 13% (v/v) inoculum concentration. The BP model based on heuristic optimization algorithm for bioleaching of chalcopyrite has practicability.

关键词： heuristic optimization algorithm Bioleaching of chalcopyrite Process control BP neural network

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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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A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2018年第Part2期81卷 2128-2143页

作者： Nazari-Heris, M. Mohammadi-Ivatloo, B. Gharehpetian, G. B. Univ Tabriz Fac Elect & Comp Engn Smart Energy Syst Lab Tabriz Iran Amirkabir Univ Technol Tehran Polytech Elect Engn Dept Tehran Iran

Combined heat and power economic dispatch (CHPED) aims to minimize the operational cost of heat and power units satisfying several equality and inequality operational and power network constraints. The CHPED should be handled considering valve-point loading impact of the conventional thermal plants, power transmission losses of the system, generation capacity limits of the production units, and heat-power dependency constraints of the cogeneration units. Several conventional optimization algorithms have been firstly presented for providing the optimal production scheduling of power and heat generation units. Recently, experience-based algorithms, which are called heuristic and meta-heuristic optimization procedures, are introduced for solving the CHPED optimization problem. In this paper, a comprehensive review on application of heuristic optimization algorithms for the solution of CHPED problem is provided. In addition, the most popular heuristic and meta-heuristic optimization algorithms are studied in this paper, and implementation of the optimization procedures for the solution of CHPED problem taking into account the objective functions and different constrains are discussed. The main contributions of the reviewed papers are studied and discussed in details. Additionally, main considerations of equality and inequality constraints handled by different research studies are reported in this paper. Five test systems are considered for evaluating the performance of different optimization techniques. Optimal solutions obtained by employment of multiple heuristic and meta-heuristic optimization methods for test instances are demonstrated and the introduced methods are compared in terms of convergence speed, attained optimal solutions, and constrains. The best optimal solutions for five test systems are provided in terms of operational cost by employment of different optimization methods.

关键词： Combined heat and power (CHP) Economic dispatch heuristic optimization algorithm Non-convex optimization

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A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems

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Journal of Modern Power Systems and Clean Energy 2014年第4期2卷 289-297页

作者： Ming NIU Can WAN Zhao XU The Hong Kong Polytechnic University Hong KongChina

Optimal power flow(OPF)is one of the key tools for optimal operation and planning of modern power *** to the high complexity with continuous and discrete control variables,modern heuristic optimization algorithms(HOAs)have been widely employed for the solution of *** paper provides an overview of the latest applications of advanced HOAs in OPF *** most frequently applied HOAs for solving the OPF problem in recent years are covered and briefly introduced,including genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and evolutionary programming(EP),etc.

关键词： heuristic optimization algorithm Optimal power flow Multi-objective optimization Constraint optimization

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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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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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Improved Node Localization for WSN using heuristic optimization Approaches

Improved Node Localization for WSN using Heuristic Optimizat...

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International Conference on Networking and Network Applications (NaNA)

作者： Shieh, Chin-Shiuh Van-Oanh Sai Lin, Yuh-Chung Lee, Tsair-Fwu Trong-The Nguyen Quang-Duy Le Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 80778 Taiwan Tajen Univ Dept Informat Applicat & Management Pingtung Taiwan

ISBN: (纸本)9781467398039

In Wireless Sensor Network, the localization of sensor nodes is an important problem in many applications. Normally in localization problem, the unknown position nodes will be determined their location through information of three or more anchors. In first part, some popular heuristic optimization methods like Genetic algorithm (GA), Particle Swarm optimization (PSO) will be compared with some recent optimization methods like Grey Wolf Optimizer (GWO), Firefly algorithm (FA), and Brain Storm optimization (BSO) algorithms in estimating the location of sensor nodes about accuracy. In second part, the improvement in localization algorithm is also proposed to enhance the number of nodes that can be localized. The results of our proposed improvement will be compared with original algorithm in both number of nodes that can be localized and the execute time with different deployment of networks.

关键词： Improved algorithm node localization wireless sensor network heuristic optimization algorithm comparative study

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Conjunctive use of surface and ground water resources using the ant system optimization

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AGRICULTURAL WATER MANAGEMENT 2016年 173卷 23-34页

作者： Safavi, Hamid R. Enteshari, Sajad Isfahan Univ Technol Dept Civil Engn Esfahan Iran

Conjunctive use of surface and ground water resources forms an important part of integrated water resource planning and management in a basin-wide scale. This paper presents a simulation/optimization model based on artificial neural networks (ANNs) and an ant system optimization (AS) for solving the monthly conjunctive supply of irrigation water. The study area is Najafabad Plain located in the semi-arid west central Iran. The water resources in the region are under increasing pressure to meet the growing irrigation water demand despite the dire decline in its surface water resources due to climate changes. The main objective of the conjunctive use model developed in this study is to minimize the water deficit in the three irrigation zones subject to constraints on groundwater levels and cumulative drawdown for each zone. The results indicate that the simulation model is capable of predicting the behavior of the study aquifer and that it may be used as a decision support system. Moreover, both the simulation and the optimization models are found capable of determining water extraction quantities required so that not only will the present water deficit decline but aquifer conditions will improve as well. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Artificial neural network Simulation Scenario Ant system heuristic optimization algorithm

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An improved gravitational search algorithm for global optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第4期37卷 5039-5047页

作者： Yu Xiaobing Yu Xianrui Chen Hong Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Jiangsu Peoples R China

Gravitational search algorithm (GSA) is inspired by swarm behaviors in nature and physical law based on Newtonian gravity and the laws of motion. There are two key parameters including the number of applied agents (Kbest) and gravitational coefficient (G) to control the search progress in the algorithm. In the conventional GSA, the acceleration of the agents is mainly determined by Kbest and G. Kbest and G are calculated by a monotonically decreasing function, which is not a good schedule for solving complex problems. In order to solve the problem and accelerate the convergence of algorithm, an adaptive GSA is proposed, in which Kbest and G calculation method for strengthening exploitation capability are implemented to achieve better optimization results. Extensive experimental results based on benchmark functions are provided to show the effectiveness of the proposed method. The obtained results have been compared with the results of the original GSA, CGSA, and CLPSO. The comparison results have revealed that the proposed method has good performances.

关键词： heuristic optimization algorithm gravitational search algorithm gravitational coefficient global optimization

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