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Modified cheetah optimizer algorithm for solving constrained optimization problems
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AI
DOI：10.1080/17509653.2025.2587750.png)
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Abstract
En 中文
This research introduces a novel modified cheetah optimizer (MCO) designed to address the complexities encountered in optimization problems. By building on the foundation of the original cheetah optimizer (CO), the algorithm's performance is enhanced through the integration of various opposition-based learning (OBL) variants, which improve its ability to expand the search space and prevent becoming trapped in local optima. Three distinct OBL techniques are incorporated and applied iteratively across the two operational phases of MCO, thereby promoting greater solution diversity and accelerating convergence. The effectiveness of MCO is evaluated through tests conducted using the CEC 2020 benchmark functions. The results demonstrate superior results in terms of both accuracy and computational efficiency when compared to other state-of-the-art algorithms. To further validate its practicality, MCO is applied to four decision-making problems, where it consistently delivers high-quality, feasible solutions with lower computational effort and greater stability than well-established methods. These findings highlight the significant potential of MCO algorithm as a robust and versatile tool for optimization, contributing to the advancement of metaheuristic techniques in real-world applications.
Keywords:
Metaheuristic algorithm
cheetah optimizer
opposition-based learning
swarm intelligence
decision-making problem
Journal
IF:
2.6
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237
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739