A Sinh-Cosh-Enhanced DBO Algorithm Applied to Global Optimization Problems
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View abstract on PubMed
Summary
This summary is machine-generated.The new Sinh-Cosh Dung Beetle Optimization (SCDBO) algorithm improves upon DBO by enhancing random initialization and global search capabilities. This advanced metaheuristic algorithm shows superior performance in complex engineering problems.
Area Of Science
- Computational Intelligence
- Metaheuristic Optimization
- Algorithm Development
Background
- The Dung Beetle Optimization (DBO) algorithm offers strong optimization capabilities but suffers from limitations in population initialization, search speed, and global exploration.
- Existing metaheuristic algorithms require enhancements to address challenges in complex problem-solving.
Purpose Of The Study
- To introduce the Sinh-Cosh Dung Beetle Optimization (SCDBO) algorithm, an enhanced version of DBO.
- To improve the random population initialization, search speed, and global search capabilities of the DBO algorithm.
Main Methods
- Developed the SCDBO algorithm by integrating Sinh and Cosh functions into the DBO framework.
- Leveraged nonlinear enhancements through Sinh and Cosh functions to optimize initial distribution and balance beetle development.
- Evaluated SCDBO performance against seven benchmark algorithms using CEC2017 test functions.
Main Results
- SCDBO demonstrated enhanced search efficiency and global exploration capabilities compared to the original DBO.
- The algorithm achieved superior performance on the CEC2017 test suite.
- Successful application to engineering problems including robot arm design, pressure vessel design, and UAV path planning.
Conclusions
- The SCDBO algorithm effectively overcomes the limitations of the original DBO.
- SCDBO exhibits significant potential for solving complex real-world optimization problems.
- The proposed enhancements provide a robust and efficient metaheuristic approach.
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