## 1 Introduction  
Optimization seeks to identify the most effective solutions while adhering to specific constraints and conditions. It plays a vital role in both theoretical research and real-world engineering applications, where achieving peak performance, efficiency, or minimal cost is often the goal. As technology advances and we take on more complex challenges, the nature of optimization problems has evolved significantly. These problems have grown not only in scale but also in complexity, making the quest for optimal solutions increasingly difficult. While many optimization algorithms exist, reaching the global optimum, especially in large-scale or highly dynamic scenarios, remains a daunting task [1]. In general, numerical optimization algorithms are typically classified into two main groups: deterministic algorithms (also known as exact methods) and stochastic algorithms (also referred to as approximate methods) [2]. Traditional optimization methods are often grounded in exact mathematical formulations and rely on gradient-based information to navigate the solution space. These deterministic methods are well-suited to problems where all parameters are known, the objective function is smooth, and the search space is manageable. However, when faced with non-deterministic polynomial-time hardness (i.e., NP-hard problems), which are common in engineering and logistics, these traditional approaches struggle. High computational costs, long execution times, and a tendency to get trapped in local optima make them impractical for many real-world applications. This has opened the door for approximate optimization algorithms that favor speed, adaptability, and robustness over theoretical precision [3]. Metaheuristic algorithms have gained prominence as promising alternatives to conventional exact methods, especially for solving NP-hard problems. These algorithms are inherently flexible, employing probabilistic search mechanisms that do not require derivative information [4]. They can be applied across a wide array of applications, including those with noisy, nonlinear, or multi-modal objective functions. Their effectiveness mainly comes from their ability to strike a balance between exploring the search space widely (exploration) and focusing on the most promising areas (exploitation), which helps them avoid getting stuck too soon and discover high-quality solutions more quickly. This balance is key to the effectiveness of any metaheuristic approach [5]. Generally, metaheuristics can be grouped into four categories considering their underlying inspiration: evolutionary-based, swarm intelligence-based, physics-inspired, and human behavior-based techniques [6]. These encompass evolutionary approaches inspired by natural selection [7], swarm intelligence based on the collective behavior of animals [8], and physics-inspired models that simulate natural processes like gravity and thermodynamics [9]. A newer branch includes human-inspired algorithms, which model decision-making, negotiation, learning, or social behavior [10]. These human-based algorithms have shown considerable promise in solving complex optimization problems due to their intuitive mechanisms and adaptability, qualities that are especially relevant in today's dynamic problem spaces [11; 12; 13; 14]. Despite their versatility, existing metaheuristics face several limitations that hinder their widespread adoption and performance in highly demanding environments. One major challenge lies in their reliance on static control parameters, which often fail to adapt as the search progresses. This can result in premature convergence or inefficient exploration, especially in dynamic or high-dimensional problems [15]. Additionally, most current algorithms are not designed to fully utilize memory-based learning or historical performance, which limits their capacity to make intelligent, context-aware decisions [6].