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Adaptive Gazelle optimization algorithm: a novel solution for complex optimization problems
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AI
DOI：10.1007/s10586-024-05046-6.png)
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Abstract
En 中文
The complexity of modern engineering and scientific optimization problems reveals the limitations of traditional meta-heuristic algorithms, including slow convergence, local optimal trapping, and poor adaptability to high-dimensional tasks. To address these challenges, this study proposes the Adaptive Gazelle Optimization Algorithm (AGOA), which combines multivariate enhanced logistic chaos initialization, adaptive Brownian motion for parameter adjustment, and adaptive Levy flight for balancing exploration and exploitation. The predator accumulation effect further improves global search and convergence. Experiments on CEC-2017 and CEC-2022 benchmarks show AGOA’s performance gains, with a 16.11% faster convergence rate and a 20.79% increase in solution accuracy over the original algorithm and others. In 3D trajectory planning, AGOA reduces path length by about 17.70% compared to other algorithms. AGOA has also demonstrated adaptability and effectiveness in applications like structural optimization, PID tuning, and wireless sensor network layout, highlighting its robustness in engineering and science.
Keywords:
Gazelle optimization algorithm
Adaptive
Trajectory planning
PID parameter optimization
Wireless sensor network layout
Journal
C
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Papers:
691
Citations:
1