Log in

Relevant bibliographies by topics / Golden jackal optimization

Contents

Journal articles
Book chapters
Conference papers

Academic literature on the topic 'Golden jackal optimization'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Golden jackal optimization.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Golden jackal optimization"

1

Yuan, Panliang, Taihua Zhang, Liguo Yao, Yao Lu, and Weibin Zhuang. "A Hybrid Golden Jackal Optimization and Golden Sine Algorithm with Dynamic Lens-Imaging Learning for Global Optimization Problems." Applied Sciences 12, no. 19 (2022): 9709. http://dx.doi.org/10.3390/app12199709.

Full text

Abstract:

Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey’s position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems.

APA, Harvard, Vancouver, ISO, and other styles

2

Ragunathan, Ramamoorthi, and Balamurugan Ramadoss. "An improved golden jackal optimization algorithm for combined economic emission dispatch problems." International Journal of Advances in Applied Sciences 13, no. 2 (2024): 249. http://dx.doi.org/10.11591/ijaas.v13.i2.pp249-259.

Full text

Abstract:

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

APA, Harvard, Vancouver, ISO, and other styles

3

Ramamoorthi, Ragunathan, and Ramadoss Balamurugan. "An improved golden jackal optimization algorithm for combined economic emission dispatch problems." International Journal of Advances in Applied Sciences (IJAAS) 13, no. 2 (2024): 249–59. https://doi.org/10.11591/ijaas.v13.i2.pp249-259.

Full text

Abstract:

In this research paper, a new improved golden jackal optimization (IGJO) algorithm is applied to address the combined economic emission dispatch (CEED) problem, along with various thermal generator constraints such as valve point loading (VPL) effect, generator limits (GL) in power system. The hunting behavior of the golden jackals is mimicked in the golden jackal optimization (GJO) algorithm. The main aim of the CEED problem is to find the best optimal generation scheduling while minimizing both fuel cost and emission besides meeting the different power system constraints. The original GJO algorithm faces challenges when dealing with high-dimensional optimization problems, as it tends to get trapped in local optima. To address this issue the opposition-based learning (OBL) method was adopted in this GJO algorithm to obtain the global optimal solution and ensure enhanced performance in finding the solution for the CEED problems. To assess the competitiveness of the IGJO algorithm, it is used for various CEED test problems available in the literature, and results are contrasted with other recent heuristic optimization algorithms. Simulation results show that the proposed IGJO performs more effectively than the other compared algorithms in terms of solution quality, and robustness.

APA, Harvard, Vancouver, ISO, and other styles

4

Ragunathan, Ramamoorthi, and Balamurugan Ramadoss. "Golden jackal optimization for economic load dispatch problems with complex constraints." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 781–93. http://dx.doi.org/10.11591/eei.v13i2.6572.

Full text

Abstract:

This research paper uses the golden jackal optimization (GJO), a novel meta-heuristic algorithm, to address power system economic load dispatch (ELD) problems. The GJO emulates the hunting behavior of golden jackals. GJO algorithm uses the cooperative attacking behavior of golden jackals to tackle complicated optimization problems efficaciously. The objective of ELD problem is to distribute power system load requirement to the different generators with a minimum total fuel cost of generation. ELD problems are highly complex, non-linear, and non-convex optimization problems while considering constraints namely valve point loading effect (VPL) and prohibited operating zones (POZs). The proposed GJO algorithm is applied to solve complex, non-linear, and non-convex ELD problems. Six different test systems having 6, 10, 13, 40, and 140 generators with various constraints are used to validate the usefulness of the suggested GJO method. Simulation outcomes of the test system are compared with various algorithms reported in the algorithms such as particle swarm optimization (PSO), ant colony optimization (ACO), and backtracking search algorithm (BSA). Results show that the proposed GJO algorithm produces minimal fuel cost and has good convergence in solving ELD problems of power system engineering.

APA, Harvard, Vancouver, ISO, and other styles

5

Nanda Kumar, S., and Nalin Kant Mohanty. "Modified Golden Jackal Optimization Assisted Adaptive Fuzzy PIDF Controller for Virtual Inertia Control of Micro Grid with Renewable Energy." Symmetry 14, no. 9 (2022): 1946. http://dx.doi.org/10.3390/sym14091946.

Full text

Abstract:

Frequency regulation of low inertia symmetric micro grids with the incorporation of asymmetric renewable sources such as solar and wind is a challenging task. Virtual Inertia Control (VIC) is the idea of increasing micro grids’ inertia by energy storage systems. In the current study, an adaptive fuzzy PID structure with a derivative filter (AFPIDF) controller is suggested for VIC of a micro grid with renewable sources. To optimize the proposed controllers, a modified Golden Jackal Optimization (mGJO) has been proposed, where variable Sine Cosine adopted Scaling Factor (SCaSF) is employed to adjust the Jackal’s location in the course of search process to improve the exploration and exploitation capability of the original Golden Jackal Optimization (GJO) algorithm. The performance of the mGJO algorithm is verified by equating it with original GJO, as well as Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning Based Optimization (TLBO) and Ant Lion Optimizer (ALO), considering various standard benchmark test functions. In the next stage, conventional PID and proposed FPIDF controller parameters are optimized using the proposed mGJO technique and the superiority of mGJO over other symmetric optimization algorithms is demonstrated. The robustness of the controller is also investigated under intermittent load disturbances, as well as different levels of asymmetric RESs integration.

APA, Harvard, Vancouver, ISO, and other styles

6

师, 尚. "Improved Golden Jackal Optimization Algorithm in PV MPPT." Modeling and Simulation 13, no. 03 (2024): 3525–34. http://dx.doi.org/10.12677/mos.2024.133321.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Wang, Kun, Jinggeng Gao, Xiaohua Kang, and Huan Li. "Improved tri-training method for identifying user abnormal behavior based on adaptive golden jackal algorithm." AIP Advances 13, no. 3 (2023): 035030. http://dx.doi.org/10.1063/5.0147299.

Full text

Abstract:

Identification of abnormal user behavior helps reduce non-technical losses and regulatory operating costs for power marketing departments. Therefore, this paper proposes an adaptive golden jackal algorithm optimization improved tri-training method to identify user abnormal behavior. First, this paper constructs multiple weak learners based on the abnormal behavior data of users, combined with the method of sampling and putting back, and uses the filtering method to select the tri-training base model. Second, aiming at the problem that the traditional optimization algorithm has a slow convergence speed and is easy to fall into local optimization, the adaptive golden jackal algorithm is used to realize the parameter optimization of tri-training. Based on the electricity consumption data of a certain place in the past five years, it is found that the model can provide stable identification results: accuracy = 0.987, f1- score = 0.973.

APA, Harvard, Vancouver, ISO, and other styles

8

Qiu, Feng, Hui Xu, and Fukui Li. "Applying modified golden jackal optimization to intrusion detection for Software-Defined Networking." Electronic Research Archive 32, no. 1 (2023): 418–44. http://dx.doi.org/10.3934/era.2024021.

Full text

Abstract:

<abstract> <p>As a meta-heuristic algorithm, the Golden Jackal Optimization (GJO) algorithm has been widely used in traditional network intrusion detection due to its ease of use and high efficiency. This paper aims to extend its application to the emerging field of Software-Defined Networking (SDN), which is a new network architecture. To adapt the GJO for SDN intrusion detection, a modified Golden Jackal Optimization (mGJO) is proposed to enhance its performance with the use of two strategies. First, an Elite Dynamic Opposite Learning strategy operates during each iteration to find solutions opposite to the current global optimal solutions, which increases population diversity. Second, an updating strategy based on the Golden Sine II Algorithm is utilized in the exploitation phase to update the position information of the golden jackal pairs, which accelerates the search for the best feature subset indexes. To validate the feasibility of the mGJO algorithm, this paper first assesses its optimization capability using benchmark test functions. Then, four UCI datasets and the NSL-KDD dataset are used to test the classification capability of the mGJO algorithm and its application in traditional network intrusion detection. Furthermore, the InSDN dataset is used to validate the feasibility of the mGJO algorithm for SDN intrusion detection. The experimental results show that, when the mGJO algorithm is applied to SDN for intrusion detection, the various indexes of classification and the selection of feature subsets achieve better results.</p> </abstract>

APA, Harvard, Vancouver, ISO, and other styles

9

Das, Himansu, Sanjay Prajapati, Mahendra Kumar Gourisaria, Radha Mohan Pattanayak, Abdalla Alameen, and Manjur Kolhar. "Feature Selection Using Golden Jackal Optimization for Software Fault Prediction." Mathematics 11, no. 11 (2023): 2438. http://dx.doi.org/10.3390/math11112438.

Full text

Abstract:

A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes.

APA, Harvard, Vancouver, ISO, and other styles

10

Jiang, Shijie, Yinggao Yue, Changzu Chen, Yaodan Chen, and Li Cao. "A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application." Biomimetics 9, no. 5 (2024): 270. http://dx.doi.org/10.3390/biomimetics9050270.

Full text

Abstract:

The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Golden jackal optimization"

1

Feng, Jinghui, Xukun Zhang, and Lihua Zhang. "Bio-Inspired Feature Selection via an Improved Binary Golden Jackal Optimization Algorithm." In Knowledge Science, Engineering and Management. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5495-3_5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Al Sulaie, Saleh. "Golden Jackal Optimization with Deep Learning-Based Anomaly Detection in Pedestrian Walkways for Road Traffic Safety." In International Conference on Innovative Computing and Communications. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3010-4_50.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Abdel-salam, Mahmoud, and Aboul Ella Hassanien. "A Novel Dynamic Chaotic Golden Jackal Optimization Algorithm for Sensor-Based Human Activity Recognition Using Smartphones for Sustainable Smart Cities." In Artificial Intelligence for Environmental Sustainability and Green Initiatives. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63451-2_16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Gharehchopogh, Farhad Soleimanian, Seyedali Mirjalili, Gültekin Işık, and Bahman Arasteh. "A new hybrid whale optimization algorithm and golden jackal optimization for data clustering." In Handbook of Whale Optimization Algorithm. Elsevier, 2024. http://dx.doi.org/10.1016/b978-0-32-395365-8.00044-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Golden jackal optimization"

1

Chen, Siwen, and Xiujuan Zheng. "An adaptive golden jackal optimization algorithm for mobile robotic path planning." In International Conference on Mechatronics and Intelligent Control (ICMIC 2024), edited by Kun Zhang and Pascal Lorenz. SPIE, 2025. https://doi.org/10.1117/12.3052803.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

C, Thirumal P., and Aiswarya T. "Glaucoma Detection using Golden Jackal Optimization based MobileNet Dual Attention Classification." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012444.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Lai, Zhaolin, Guangyuan Li, Lue Li, and Xiaoyun Zeng. "An Experience-Exchange Learning Golden Jackal Optimization Algorithm for High-Dimensional Optimization Problems." In 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2024. http://dx.doi.org/10.1109/icecai62591.2024.10675305.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Tubishat, Mohammad, Mustafa Rawshdeh, and Shahed Obeidat. "An Improved Golden Jackal Optimization Based on New Local Search Operator for Global Optimization: Invited Paper." In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2024. http://dx.doi.org/10.1109/wincom62286.2024.10655493.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Alkhayyat, Ahmad, Komuravelly Sudheer Kumar, Murigendrayya M. Hiremath, Abhijeet Das, and Madhura G. K. "Escaping Strategy with Golden Jackal Optimization Based Clustering and Routing in Mobile Ad Hoc Network." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10690997.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Wang, Baiyi, Zhongwen Yi, Xiaoyang Liu, DeZheng Hua, and Xinhua Liu. "Precise motion controller design for a mobile robot based on the improved golden jackal optimization algorithm." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10662722.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Singh, Indu, Manav, Manish Gautam, and Manish Toshwal. "Hybrid Golden Jackal-Sea Lion and Sea Horse Optimization Algorithm for Improved Keystroke Dynamics User Authentication." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747905.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Saradha, M., T. Sasivanan, C. Monishkumar, J. Muralidharan, and K. Nivitha. "Hybrid Golden Jackal and Whale Optimization Algorithm (HGJWOA) for Optimizing Resource and Service Allocation in Edge Computing Environments." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616662.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Geng, Junqi, Haihua Wang, Jie Su, et al. "Coverage optimization of wireless sensor networks with improved golden jackal optimization." In 2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2023. http://dx.doi.org/10.1109/icecai58670.2023.10176640.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Li, Zhenyu, Zexi Hua, and Yanjie Pang. "A Multi-Strategy Improved Golden Jackal Optimization Algorithm Integrating the Golden Sine Mechanism." In CSAIDE 2024: 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy. ACM, 2024. http://dx.doi.org/10.1145/3672919.3673028.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!