An Improved Golden Jackal Optimization Algorithm Using Opposition-Based Learning for Global Optimization and Engineering Problems

IF 2.9 4区计算机科学

International Journal of Computational Intelligence Systems Pub Date : 2023-09-12 DOI:10.1007/s44196-023-00320-8

Sarada Mohapatra, Prabhujit Mohapatra

引用次数: 1

Abstract

Abstract Golden Jackal Optimization (GJO) is a recently developed nature-inspired algorithm that is motivated by the collaborative hunting behaviours of the golden jackals in nature. However, the GJO has the disadvantage of poor exploitation ability and is easy to get stuck in an optimal local region. To overcome these disadvantages, in this paper, an enhanced variant of the golden jackal optimization algorithm that incorporates the opposition-based learning (OBL) technique (OGJO) is proposed. The OBL technique is implemented into GJO with a probability rate, which can assist the algorithm in escaping from the local optima. To validate the efficiency of OGJO, several experiments have been performed. The experimental outcomes revealed that the proposed OGJO has more efficiency than GJO and other compared algorithms.

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面向全局优化和工程问题的基于对立学习的改进金豺优化算法

摘要金豺优化算法(Golden Jackal Optimization, GJO)是一种受自然界中金豺协同狩猎行为的启发而发展起来的自然算法。然而，GJO的缺点是开发能力差，容易陷入最优局部区域。为了克服这些缺点，本文提出了一种金豺优化算法的增强变体，该算法结合了基于对立的学习技术(OBL)。OBL技术以一定的概率率实现在GJO中，可以帮助算法脱离局部最优。为了验证OGJO的有效性，进行了几个实验。实验结果表明，所提出的OGJO算法比GJO算法和其他比较算法具有更高的效率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Computational Intelligence Systems 工程技术-计算机：跨学科应用

自引率

3.40%

发文量

期刊介绍： The International Journal of Computational Intelligence Systems publishes original research on all aspects of applied computational intelligence, especially targeting papers demonstrating the use of techniques and methods originating from computational intelligence theory. The core theories of computational intelligence are fuzzy logic, neural networks, evolutionary computation and probabilistic reasoning. The journal publishes only articles related to the use of computational intelligence and broadly covers the following topics: -Autonomous reasoning- Bio-informatics- Cloud computing- Condition monitoring- Data science- Data mining- Data visualization- Decision support systems- Fault diagnosis- Intelligent information retrieval- Human-machine interaction and interfaces- Image processing- Internet and networks- Noise analysis- Pattern recognition- Prediction systems- Power (nuclear) safety systems- Process and system control- Real-time systems- Risk analysis and safety-related issues- Robotics- Signal and image processing- IoT and smart environments- Systems integration- System control- System modelling and optimization- Telecommunications- Time series prediction- Warning systems- Virtual reality- Web intelligence- Deep learning