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Abstract
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In numerous engineering fields, the challenge of identifying an optimal solution within a complex, high-dimensional space is a frequent task [1]. Due to the significant computational cost and time involved, deterministic algorithms often fail to achieve optimal solutions in such scenarios. Meta-heuristic optimization algorithms, known for their simplicity and robust search capabilities, have thus gained widespread application in solving intricate optimization problems. Recently, several meta-heuristic approaches, including genetic algorithm (GA) [2], particle swarm optimization (PSO) [3], differential evolution (DE) [4], bat algorithm (BA) [5], have been introduced, drawing inspiration from human cognition, social behaviors in biological groups, and natural phenomena. However, experts widely agree that no single algorithm is universally effective for all optimization problems. This implies that while one method might excel in solving a specific problem, it may perform poorly on others. Consequently, further exploration of diverse meta-heuristic techniques is essential to address various optimization challenges effectively.
Recent research has focused on developing new algorithms, enhancing existing ones, and combining multiple algorithms. Among these, the integration of different meta-heuristic algorithms (MAs) has garnered significant attention from researchers due to its potential to leverage the strengths of individual algorithms and improve overall performance [6]. Several hybrid approaches have demonstrated promising outcomes, such as the combination of firefly algorithm with particle swarm optimization (PSO) [7], the hybridization of the sine-cosine algorithm (SCA) with differential evolution (DE) [8], the integration of particle swarm optimization (PSO) with the gray wolf optimizer (GWO) [9], the hybridization of ant colony optimization (ACO) with particle swarm optimization (PSO) [10], the integration of grey wolf optimizer (GWO) with bat algorithm (BA) [11]
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