Hybrid Multi-Strategy Improvements for The Aquila Optimizer

Although the original Aquila optimizer shows a strong optimization ability, it will fall into a local optimal solution in many cases. The design of the algorithm is flawed, the switch between exploration and exploitation strategies is extremely rigid, and each strategy is very dependent on the locat...

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Bibliographic Details
Main Authors: Zhang, Yinzhao, Sun, Wei, Hou, Jun, Li, Qianmu
Format: Conference Proceeding
Language:English
Subjects:
Aquila optimizer
Benchmark testing
Big Data
Convergence
intrusion detection
Optimization
Particle swarm optimization
Search problems
swarm intelligence algorithm
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Summary:Although the original Aquila optimizer shows a strong optimization ability, it will fall into a local optimal solution in many cases. The design of the algorithm is flawed, the switch between exploration and exploitation strategies is extremely rigid, and each strategy is very dependent on the location of the prey. To address these issues, this paper proposes the Hybrid Multi-Strategy Aquila Optimizer (HMAO). This algorithm balances the exploration stage and the exploitation stage in the optimization process, introduces a variety of improvement strategies, enhances the global search ability, improves the convergence speed, and greatly avoids the problem of falling into local optimum, and it has a good performance on 23 benchmark functions Algorithm performance is verified.
ISSN:2837-0740
DOI:10.1109/DASC/PiCom/CBDCom/Cy59711.2023.10361401