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golden jackal optimization algorithm with a population quality improvement framework for real-world engineering optimization problems

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EVOLUTIONARY INTELLIGENCE 2025年第3期18卷 1-40页

作者： Xue, Rui Deng, Kefeng Harbin Engn Univ Coll Informat & Commun Engn Nantong St Harbin 150001 Heilongjiang Peoples R China

In this study, a population quality improvement (PQI) framework to enhance the performance of the recently emerged nature-inspired metaheuristic algorithm, named golden jackal optimization (GJO) algorithm, for solving real-world engineering optimization problems is proposed. Firstly, the maximin LHS strategy is employed to generate the initial population, which can efficiently enhance the search space in the early stages. Then a non-linear population reduction mechanism is applied to screen out the individuals with unsatisfying solutions, and it can also reduce the computational complexity. Meanwhile, a novel circular mutation strategy is introduced, which can dynamically adjust the algorithm to explore in a wilder space or to exploit deeper, this method can not only accelerate the convergence of the optimization, but also reduce the risk of trapping in the local optima. Moreover, the proposed framework shows tremendous potential in application to other population-based searching algorithms, due to that the algorithms are improved, while remaining the inherent mathematical structure and searching strategy. The experimental results demonstrate that the proposed framework can efficiently improve the performance of GJO on solving real-world engineering optimization problems, and the proposed PQI-GJO algorithm can save 196.7001 $/h, 0.3595 MW and 0.0552 p.u. in comparison with the state-of-art algorithm JAYA in solving OPF problems for IEEE-57 bus system.

关键词： Real-world engineering optimization problems golden jackal optimization algorithm Maximin Latin hypercube sampling Population quality improvement Circular mutation

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Kernel principal component analysis fault diagnosis method based on improving golden jackal optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING 2024年第5期238卷 874-887页

作者： Zhang, Ruicheng Sun, Weiliang Liang, Weizheng North China Univ Sci & Technol Coll Elect Engn Tangshan Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Hebei Peoples R China

Aiming at the shortcomings of the golden jackal optimization algorithm, such as low convergence accuracy and easy falling into the optimal local solution, an improved golden jackal optimization algorithm was proposed. First, sine and piecewise linear (SPM) chaotic mapping was introduced to increase the population number to achieve the purpose of initial population diversity. The self-adaptive weight and sine-cosine algorithm improved the position update formula of the golden jackal optimization algorithm, so the global search ability of the golden jackal algorithm is improved, and avoid the algorithm that fell into local optimality. Second, simulation experiments with eight standard test functions are performed to prove that the algorithm has excellent optimization ability. The improved golden jackal optimization algorithm was applied to optimize the kernel parameters of hybrid kernel principal component analysis. A fault diagnosis model is proposed to improve the golden jackal algorithm to optimize the kernel principal component analysis. Finally, the proposed method is used to fault diagnosis in the hot strip mill process. According to the study of simulation results, the faulty data can be identified effectively by this method, the accuracy is up to 100%, and the fault false alarm rate is greatly reduced.

关键词： golden jackal optimization algorithm kernel principal component analysis adaptive weight fault diagnosis hot strip mill process

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Enhancing sensor duty cycle in environmental wireless sensor networks using Quantum Evolutionary golden jackal optimization algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第7期20卷 12298-12319页

作者： Lu, Zhonghua Tian, Min Zhou, Jie Liu, Xiang Shihezi Univ Coll Mech & Elect Engn Shihezi 832000 Peoples R China Shihezi Univ Coll informat Sci & Technol Shihezi 832000 Peoples R China

Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary golden jackal optimization algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69%, 20.15% and 26.55% compared to the golden jackal optimization (GJO), Whale optimization algorithm (WOA) and the Simulated Annealing algorithm (SA).

关键词： duty cycle optimization golden jackal optimization algorithm exploration operators network lifetime

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A Multi-Strategy Improved golden jackal optimization algorithm Integrating the golden Sine Mechanism 24

A Multi-Strategy Improved Golden Jackal Optimization Algorit...

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3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy (CSAIDE)

作者： Li, Zhenyu Hua, Zexi Pang, Yanjie SouthWest Jiaotong Univ Chengdu 610031 Sichuan Peoples R China Sichuan Dory Cancon Technol Co Chengdu 610000 Sichuan Peoples R China

ISBN: (纸本)9798400718212

In response to the shortcomings of poor population diversity, weak global search ability, and susceptibility to local optima in the golden jackal optimization algorithm, this paper proposes a Multi-Strategy Improvement GJO (MSIGJO) algorithm that integrates the golden Sine mechanism. Firstly, Latin hypercube sampling is used to initialize the golden jackal population, improving the quality of initial solutions. Secondly, by incorporating the golden sine mechanism as an operator into the search stage of the golden jackal algorithm, the optimization accuracy of the algorithm is improved. Finally, the adaptive t-distribution is used to perturb the optimal individual adaptively, and greedy strategies are employed to find the optimal solution. The comparison test results of MSIGJO and five other intelligent algorithms through 8 benchmark test functions show that the improved algorithm in this paper is superior to different algorithms in terms of convergence speed and optimization.

关键词： Intelligent optimization algorithm golden jackal optimization algorithm Latin hypercube sampling golden Sine mechanism adaptive t-distribution

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Cancer miRNA biomarker classification based on syntax-guided hierarchical attention network optimized with golden jackal optimization algorithm

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartB期95卷

作者： Kumar, P. J. Sathish Pandurangan, Raji Bapu, B. R. Tapas Nagaraju, V. Panimalar Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India Anna Univ Saveetha Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India SA Engn Coll Fac Elect & Commun Engn Chennai Tamil Nadu India Saveetha Sch Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

MicroRNAs (miRNAs) are integral components of genomic data, offering valuable insights into genome sequences. Existing studies have faced challenges in achieving desired classification accuracy. To address this issue, Syntax -Guided Hierarchical Attention Network optimized with golden jackal optimization (SGHAN-GJOAMiRNA-BC) is proposed to classify cancer miRNA biomarkers. Input data is amassed from Cancer Genome dataset. Data pre-processing uses the Z -score normalization method for standardizing the data. The stochastic gradient enhancement -based recursive feature elimination (SGB-RFE) method selects relevant features. Then the Syntax -Guided -Hierarchical Attention Network (SGHAN) classifies cancer miRNA biomarkers for diagnosis, therapy, and prognosis. The golden jackal optimization (GJO) algorithm optimizes SGHAN ensuring precise classification. The efficiency of proposed SGHAN-GJOA-MiRNA-BC technique is evaluated. For cancer genome atlas dataset, it achieves increase in accuracy of 10.95 %, 24.45 %, 11.85 %, 12.87 %, 16.58 %, 10.99 % and the existing techniques Cancer MiRNA biomarker categorization using generative adversarial network (GANMFOA-MiRNA-BC), lung cancer detection utilizing hybrid deep neural network (ML-ABCOA-MiRNA-BC), convolution neural network for breast cancer categorization under RNA-Seq-data (CNN-EOSA-MiRNA-BC), Parallel Bayesian model for breast cancer prediction(DNN-ASCCS-MiRNA-BC), Biomarker identification and cancer survival prediction using Bayesian optimized-DNN (BODNN-CSOA-MiRNA-BC), identifying ovarian cancer 's potential biomarkers (SVM-ICA-MiRNA-BC), a machine learning method for pancreatic cancer diagnosis (ANN-PSO-MiRNA-BC) respectively.

关键词： Cancer genome atlas dataset golden jackal optimization algorithm miRNA biomarker Stochastic gradient boosting based recursive feature elimination Syntax-guided hierarchical attention network

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optimization of rolling bearing dynamic model based on improved golden jackal optimization algorithm and sensitive feature fusion

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2023年 204卷

作者： Pan, Cailu Shang, Zhiwu Liu, Fei Li, Wanxiang Gao, Maosheng Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China Tianjin Modern Electromech Equipment Technol Key L Tianjin 300387 Peoples R China

Some simplified modeling parameters in the traditional rolling bearing dynamics model led to poor consistency between the simulated and measured vibration signals. In addition, the simulated signal has low accuracy, which in turn reduces the accuracy of bearing fault diagnosis based on the simulated signal. Thus, to resolve the above-mentioned issues, this study presents a method for optimizing rolling bearing dynamics models based on an improved golden jackal optimization (IGJO) algorithm and sensitive feature fusion. Firstly, this study proposes an IGJO algorithm using a dimension-by-dimension reverse learning strategy and adaptive weights to address the interplay of dimensions in the multidimensional optimization process and the imbalance between the global and local search abilities of the GJO. Secondly, a fusion strategy for bearing faultsensitive features is proposed based on the binary's improved golden jackal optimization (BIGJO) algorithm and principal component analysis (PCA). In this strategy, the Sigmoid function discretization method is used to obtain the B-IGJO algorithm, which is then applied to the measured signal to select the bearing fault sensitivity features. These features are analyzed using PCA to obtain the fused sensitivity feature expression. Finally, the fusion-feature expressions are used to calculate the fusion-sensitive features of the measured and simulated vibration signals, and then the residuals of the two are used as the objective function for model optimization. The parameters of the rolling bearing dynamics model are updated using the IGJO. The proposed method is experimentally verified through a single pitting fault dynamics model of the outer ring of the rolling bearing. In conclusion, our results confirm the effectiveness and feasibility of the proposed method.

关键词： Rolling bearing Dynamics model parameter optimization golden jackal optimization algorithm Sensitive feature fusion Fault diagnosis

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An efficient image segmentation method for skin cancer imaging using improved golden jackal optimization algorithm

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 149卷 106075-106075页

作者： Houssein, Essam H. Abdelkareem, Doaa A. Emam, Marwa M. Hameed, Mohamed Abdel Younan, Mina Minia Univ Fac Comp & Informat Al Minya Egypt Luxor Univ Fac Comp & Informat Luxor Egypt

Skin cancer is one of the worst cancers nowadays that poses a severe threat to the health and safety of individuals. Therefore, skin cancer classification and early diagnosis are recommended to preserve human life. Multilevel thresholding image segmentation is well-known and influential technique for extracting regions of interest from skin cancer images to improve the classification process. Therefore, this paper proposes an efficient version of the recently developed golden jackal optimization (GJO) algorithm, the opposition-based golden jackal optimizer (IGJO). The IGJO algorithm is used to solve the multilevel thresholding problem using Otsu's method as an objective function. The proposed algorithm is compared with seven other meta-heuristic algorithms: whale optimization algorithm, seagull optimization algorithm, salp swarm algorithm, Harris hawks optimization, artificial gorilla troops optimizer, marine predators' algorithms, and original GJO algorithm. The performance of the proposed algorithm is evaluated using four popular performance measures: peak signal-to-noise ratio, structure similarity index, feature similarity index, and mean square error. Experimental results show that the proposed algorithm outperforms other alternative algorithms in terms of PSNR, SSIM, FSIM, and MSE segmentation metrics and effectively resolves the segmentation problem.

关键词： golden jackal optimization algorithm Image processing Meta-heuristic algorithms Otsu?s method Skin cancer

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Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 282卷

作者： Wang, Yuchao Tong, Kelin Fu, Chunhai Wang, Yuhang Li, Qiuhua Wang, Xingni He, Yunzhe Xu, Lijia Sichuan Agr Univ Coll Elect & Mech Engn Yaan 625099 Sichuan Peoples R China Sichuan Agr Univ Coll Econ Chengdu 611134 Sichuan Peoples R China

Traditional path planning algorithms still face significant challenges in large-scale scenarios with high-density irregular obstacles, such as low search efficiency, limited obstacle avoidance capabilities, and a tendency to get trapped in local optimum. To overcome these challenges, a hybrid route planning algorithm combining the Modified golden jackal optimization (MGJO) algorithm and the Improved Dynamic Window Approach (IDWA) is proposed. To resolve the issue of getting trapped in local optimum and enhance global search efficiency in global path planning, the MGJO algorithm is synthesized based on nonlinear energy attenuation, diverse search strategies, and a guiding mechanism inspired by African vultures. To improve obstacle avoidance efficiency and ensure smoother local paths, the IDWA algorithm is redesigned by optimizing the obstacle distance evaluation function. In global path planning, the MGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions. In three different environments, the average path length of the MGJO algorithm over the original algorithm is improved by 10.76%, 16.72% and 25.46%. In local path planning experiments for mobile robots, the IDWA algorithm avoids the local optimum in small and medium-sized maps. In large maps, it significantly reduces the number of the local optimum occurrences, from 6 times to 2 times. The feasibility of the algorithm is validated in real-world experiments.

关键词： golden jackal optimization algorithm African Vultures optimization algorithm Dynamic Window Approach algorithm Mobile robot Path planning

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Multi-Strategy golden jackal optimization for engineering design

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-60页

作者： Yang, Wenbiao Lai, Tingfeng Fang, Yuhui Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Nanyang 639798 Singapore

The golden jackal optimization algorithm (GJO) is a Swarm Intelligence (SI) algorithm utilized for tackling arduous problems in undefined search spaces. In order to address the issues of slow convergence and difficulty in evading local optima encountered by GJO, the Multi-Strategy golden jackal optimization (MSGJO) algorithm is proposed. Three strategies were implemented to enhance the original GJO. Firstly, the utilization of a Good Point Set, as opposed to random numbers, improves the quality of the initial population. Secondly, the introduction of a Quasi-Opposition-Based Learning strategy facilitates the balancing of algorithmic exploration and exploitation capabilities, leading to enhanced convergence efficiency. Lastly, the maintenance of population diversity during the iterative process is achieved through the adoption of the Cauchy mutation and chaotic circle perturbation strategies. A comparative analysis was conducted on 23 benchmark functions using fourteen SI algorithms. Additionally, MSGJO was applied to the CEC2019 competition suite, successfully solving two distinct engineering design problems. The results demonstrate that MSGJO exhibits significant superiority not only in solving problems involving global optimal solutions but also in addressing challenging constrained problems. The code for this study will be available at https://***/ProfYangPaperCode/MSGJO-code.

关键词： Swarm Intelligence algorithm golden jackal optimization algorithm Quasi-Opposition-Based Learning strategy Benchmark test function Engineering design

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golden jackal optimization-based regression analysis application on volume expansion estimation of cement pastes with MgO expansive additive

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2025年第1期8卷 1-18页

作者： Tian, Yuqing Zhang, Lina Wang, Guozhi Univ Sci & Technol Sch Henan Vocat Zhoukou 466001 Henan Peoples R China

The implementation of massive amounts of fly ash (FA) and MgO expansive additive ( MEA ) is impeded by their limited hydration capacity and the challenges associated with regulating delayed expansion. The current collection of literature lacks sufficient study on the application of learning methods to estimate volume expansion (Ve) of cement paste, specifically when using FA and MEA. The process of designing and confirming methods for the evaluation of Ve involves using a dataset including 170 experimental outcomes obtained from papers. To accomplish this goal, researchers have developed support vector regression (SVR). In the present research, the optimizers known as the Equilibrium optimization algorithm (E OA ), golden jackal optimization algorithms ( GJOA ), and Henry gas solubility optimization ( HGSO ) were chosen for SV R hyperparameters' tuning. The SV RHG, SV R EO and SV R GJ models possess a substantial capacity to precisely forecast the Ve of cement paste containing MAE and FA . For O B J index, the smaller value was attributed to SV RGJ, accounting for 0.0042, almost 50% smaller than SV R EO at 0.0085 and roughly three times slither than SV R HG by 0.0131. The superior performance of the G JO A-optimized SV R model suggests that GJOA is particularly effective in handling the non-linear relationships present in the dataset. This finding has important implications for the design of cement pastes, particularly in optimizing the use of MEA and FA .

关键词： Cement paste Volume expansion Regression analysis Sensitivity analysis golden jackal optimization algorithm MgO expansive additive Regression analysis Sensitivity analysis golden jackal optimization algorithm

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