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Over the past few years, there has been a significant rise in applying optimization techniques to address various intricate optimization challenges. This marked a departure from traditional mathematical methodologies like dynamic, linear, and nonlinear programming, which were historically employed for managing complex optimization tasks. While these mathematical techniques excel at deriving optimal solutions, they face limitations when confronted with a broad spectrum of nondeterministic polynomial-time complete problems. These problems involve situations where obtaining an exact solution within polynomial time becomes infeasible, leading to exponential increases in time complexity as the input size grows. Consequently, these conventional techniques prove impractical for real-world applications (Wang, 2003; Ragsdell & Phillips, 1976). Metaheuristic algorithms have gained popularity as potent tools for tackling complex optimization problems (El-Hameed et al., 2020; Houssein, Helmy, et al., 2021). This surge in popularity can be attributed to several key factors as follows.
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Metaheuristic algorithms are renowned for their simplicity. Rooted in basic principles or models inspired by nature, these methods are straightforward to understand and implement. Their user-friendly nature empowers practitioners to apply them effectively in real-world scenarios, with the added advantage of being able to customize or develop variants based on existing methodologies easily.
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Metaheuristic algorithms operate as black boxes, providing a diverse set of outputs for a given problem and input parameters. This characteristic enables scholars to readily adjust the architecture and parameters of these algorithms to generate ideal solutions that are perfectly aligned with distinct specifications.
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Randomness plays a pivotal role in metaheuristic algorithms. This inherent stochasticity allows them to traverse the entire search space, preventing them from being trapped in local optima. By employing stochastic elements, metaheuristic algorithms effectively explore the solution space, thereby improving their capacity to identify optimal or near-optimal outcomes.
Moreover, the adaptability of metaheuristic algorithms enables them to excel in solving problems characterized by unknown search spaces or multiple local optima. This adaptability renders them highly versatile and flexible, making them applicable to a wide array of optimization challenges. Whether dealing with nonlinear, non-differentiable, or numerically complex problems replete with local minima, metaheuristic algorithms demonstrate their efficacy. Across various domains, numerous metaheuristic strategies have been developed and effectively implemented. These algorithms are typically classified into three primary categories, as outlined by Hare et al. (2013): evolutionary strategies initiated by Mühlenbein et al. (1988), physical principle-based techniques introduced by Geem et al. (2001), and swarm intelligence-driven approaches illustrated by Krause et al. (2013). Each class offers distinct advantages and is tailored to suit different problem domains, contributing to the diverse toolkit available for optimization practitioners.