A Multi-Strategy Improved Golden Jackal Optimization Algorithm Integrating the Golden Sine Mechanism

被引：0

作者：

Li, Zhenyu ^{[1
]}

Hua, Zexi ^{[1
]}

Pang, Yanjie ^{[2
]}

机构：

[1] SouthWest Jiaotong Univ, Chengdu 610031, Sichuan, Peoples R China

[2] Sichuan Dory Cancon Technol Co, Chengdu 610000, Sichuan, Peoples R China

来源：

PROCEEDINGS OF 2024 3RD INTERNATIONAL CONFERENCE ON CYBER SECURITY, ARTIFICIAL INTELLIGENCE AND DIGITAL ECONOMY, CSAIDE 2024 | 2024年

关键词：

Intelligent optimization algorithm; Golden Jackal optimization algorithm; Latin hypercube sampling; Golden Sine mechanism; adaptive t-distribution;

D O I：

10.1145/3672919.3673028

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：624 / 628

页数：5