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ACIVY: An Enhanced IVY Optimization Algorithm with Adaptive Cross Strategies for Complex Engineering Design and UAV
Heming Jia1, Mahmoud Abdel-Salam2, Gang Hu3
1School of Information Engineering, Sanming University, Sanming 365004, China.
The Adaptive Cross Ivy (ACIVY) algorithm enhances optimization by improving population diversity and local optima escape. It outperforms existing methods in complex engineering and path planning tasks.
Area of Science:
- Computational Intelligence
- Metaheuristic Optimization
- Bio-inspired Algorithms
Background:
- The original Ivy Optimization Algorithm (IVYA) has limitations in information exchange and escaping local optima.
- Complex optimization problems require advanced algorithms for efficient and accurate solutions.
Purpose of the Study:
- To introduce the Adaptive Cross Ivy (ACIVY) algorithm, an enhanced bio-inspired metaheuristic.
- To address the limitations of the IVYA algorithm through strategic improvements.
Main Methods:
- ACIVY integrates crisscross, LightTrack, and Top-Guided Adaptive Mutation strategies.
- Evaluations were conducted on CEC2017 and CEC2022 benchmark suites.
- Practical applications included engineering design and UAV path planning.
Main Results:
- ACIVY demonstrated superior performance compared to state-of-the-art algorithms on diverse functions.
- Achieved excellent average rankings: 1.25 (CEC2022) and 1.41 (CEC2017 50D).
- Validated through Wilcoxon signed-rank tests and real-world engineering problems.
Conclusions:
- ACIVY offers enhanced population diversity and effective local optima escape.
- The algorithm provides superior convergence precision, reliability, and computational efficiency.
- ACIVY is a powerful tool for complex optimization challenges in various domains.
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