# Open Competency Optimization: A Human-Inspired Optimizer for the Dynamic Vehicle-Routing Problem
## 1 Introduction  
In the field of optimization, metaheuristics have become an inevitable issue. A quick review of the literature is enough to see the amazing growth of the number of algorithms. These new tools are obviously an alternative to classic methods as, with these methods, an accurate solution cannot be found or requires a great deal of processing time. These novel kinds of algorithms help to find a satisfactory solution (not necessarily an accurate solution) in a reduced time, given that their main purpose is to efficiently explore the research space within a suitable and adequate time. In his famous book \"Metaheuristics: From Design to Implementation\" [1], [PERSON] proposed a classification of metaheuristics according to their solutions. On one hand, there are single solution-based metaheuristics (S-metaheuristics), and on the other hand, there are population-based metaheuristics (P-metaheuristics). In the first category, a single solution evolves iteratively until the best possible solution is found. Examples of S-metaheuristics include Simulated Annealing, Tabu Search, and others. However, in the second category, a population of solutions (the number of solutions is equal to the size of the population) evolves over generations (iterations) until an optimal solution is found. The list of P-metaheuristics is very exhaustive, and it includes genetic algorithms, particle swarm optimization, ant colony, gray wolf optimizer, artificial bee colony, and many others. Another way to classify these metaheuristics is according to their inspiration. They areinspired by the social behaviors of animals or insects. In other cases, they are derived from physical or chemical phenomena. However, human behavior is rare as an inspiration. This study proposes a new metaheuristic named Open Competency Optimization (OCO), which is based on a specific human learning approach known in the literature as the competency-based approach [2]. To demonstrate the performance of the proposed algorithm, OCO is not applied in a static environment with CEC 2016, 2020, or 2022 [3] or in some benchmarks related to applications in engineering tests, as is common in other research, but in a dynamic optimization problem that is widely used in the literature--the Generalized Dynamic Benchmark Generator (GDBG)--and also in a vital field--dynamic VRP. The goal of applying OCO in dynamic optimization is twofold. First, real-world applications of optimization problems are often dynamic. Second, metaheuristics require several remedies and adjustments to be applied in dynamic optimization. Indeed, in real life, optimization problems are not always stationary and can change stochastically over time. Optimization algorithms must, therefore, be capable of tracking the optimum as it evolves, meaning that they must follow the shifting optimum over time. Dynamic optimization problems are inherently more challenging than static ones, and metaheuristics have been successfully applied across various domains. Metaheuristics ensure convergence toward the desired optimum, meaning that individuals representing good candidate solutions become concentrated at a specific point. The main drawback of applying P-metaheuristics in a dynamic environment is that once the algorithm tends to converge around some optima, it begins to lose its ability to continue searching for a new optimum. Therefore, a key point in optimization approaches is the need to increase or maintain the diversity of the population such that the algorithm retains its ability to explore the search space, even when the population has partially converged to an optimal solution. Various methods and techniques have been developed to address the challenges raised by dynamic environments. We will review the most important approaches, which allow us to improve P-metaheuristics in dynamic environments.