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- Title
An Improved Golden Jackal Optimization Algorithm Based on Mixed Strategies.
- Authors
Li, Yancang; Yu, Qian; Wang, Zhao; Du, Zunfeng; Jin, Zidong
- Abstract
In an effort to overcome the problems with typical optimization algorithms' slow convergence and tendency to settle on a local optimal solution, an improved golden jackal optimization technique is proposed. Initially, the development mechanism is enhanced to update the prey's location, addressing the limitation of just relying on local search in the later stages of the algorithm. This ensures a more balanced approach to both algorithmic development and exploration. Furthermore, incorporating the instinct of evading natural predators enhances both the effectiveness and precision of the optimization process. Then, cross-mutation enhances population variety and facilitates escaping from local optima. Finally, the crossbar strategy is implemented to change both the individual and global optimal solutions of the population. This technique aims to decrease blind spots, enhance population variety, improve solution accuracy, and accelerate convergence speed. A total of 20 benchmark functions are employed for the purpose of comparing different techniques. The enhanced algorithm's performance is evaluated using the CEC2017 test function, and the results are assessed using the rank-sum test. Ultimately, three conventional practical engineering simulation experiments are conducted to evaluate the suitability of IWKGJO for engineering issues. The results obtained demonstrate the beneficial effects of the altered methodology and illustrate that the expanded golden jackal optimization algorithm has superior convergence accuracy and a faster convergence rate.
- Subjects
OPTIMIZATION algorithms; MATHEMATICAL optimization; ENGINEERING simulations; ALGORITHMS
- Publication
Mathematics (2227-7390), 2024, Vol 12, Issue 10, p1506
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math12101506