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An enhanced aquila optimization algorithm with velocity-aided global search mechanism and adaptive opposition-based
Yufei Wang1, Yujun Zhang1, Yuxin Yan2
1School of Electronics and Information Engineering, Jingchu University of Technology, Jingmen 448000, China.
Mathematical Biosciences and Engineering : MBE
|May 10, 2023
Summary
The enhanced Aquila optimization algorithm (VAIAO) improves swarm intelligence by incorporating velocity-aided global search and adaptive opposition-based learning. This boosts exploration and convergence speed for better optimization performance.
Area of Science:
- Computer Science
- Artificial Intelligence
- Optimization Algorithms
Background:
- The Aquila Optimization (AO) algorithm is a recent swarm intelligence method.
- AO exhibits good performance but suffers from slow late convergence.
- Enhancements are needed to improve AO's efficiency and speed.
Purpose of the Study:
- To propose an enhanced Aquila Optimization algorithm (VAIAO).
- To improve AO's global exploration and convergence speed.
- To address the slow late convergence issue in AO.
Main Methods:
- Introduced a velocity-aided global search mechanism.
- Incorporated an adaptive opposition-based learning strategy.
- Integrated these strategies into the AO framework, creating VAIAO.
Main Results:
- VAIAO demonstrated superior performance compared to AO, IAO, and other algorithms.
- The enhanced global exploration ability and convergence speed were confirmed.
- Successful validation on 27 benchmark functions and 5 engineering problems.
Conclusions:
- The proposed VAIAO effectively enhances the Aquila Optimization algorithm.
- The integrated strategies significantly improve exploration and convergence.
- VAIAO offers a more efficient and robust optimization solution.
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