HYBRID METAHEURISTIC OPTIMIZATION: COMBINING GREY WOLF OPTIMIZATION WITH FLAMINGO SEARCH ALGORITHM
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no1E.354Keywords:
Optimization Algorithm, Metaheuristic algorithm, Grey Wolf Optimizer, Flamingo Search Algorithm, Hybrid Optimization Algorithm.Abstract
This paper introduces a novel and innovative optimization method by combining two powerful metaheuristic algorithms: the Flamingo Search Algorithm (FSA) and the Grey Wolf Optimization (GWO) algorithm. FSA draws inspiration from the migration and foraging behavior of flamingos, while GWO simulates the hunting mechanism of grey wolves. The proposed hybrid algorithm, named FGWO, leverages the strengths of both FSA and GWO, capitalizing on their exploration and exploitation capabilities, convergence speed, and global search abilities. FGWO utilizes GWO for global solution search and subsequently employs FSA to enhance solution quality. The performance of FGWO is evaluated on a set of benchmark functions and compared against other state-of-the-art algorithms. Our experimental results demonstrate that FGWO outperforms other algorithms in terms of solution quality, stability, and efficiency. This novel hybrid algorithm contributes to the advancement of optimization methods, offering a fresh perspective on integrating diverse metaheuristic algorithms to achieve superior performance.
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