# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
###### Abstract
To address this issue, we have proposed a model that incorporates the concept of deprivation cost to prioritize support for patients in urgent situations [12]. The proposed ERRP model considers time windows, vehicle load capacity, and deprivation cost. By using this model, we can more accurately simulate the actual post-disaster material distribution scenario and obtain a more realistic solution that meets the needs of emergency relief operations. The BSO algorithm, a novel intelligent optimization computing technique, has demonstrated its benefits in handling large-scale, high-dimensional, multi-peak function issues that are challenging for traditional optimization methods to address [13]. In the fight against COVID-2019, the BSO can be used to analyze the severity of COVID-19 in terms of the coagulation index [14] or to perform feature selection for COVID-19 classification [15]. In predictive computing, the BSO algorithm has advantages in terms of fast velocity, accuracy, and reliability in predicting protein structures [16] and also demonstrates the effectiveness in the fast prediction of fixations [17]. In the VRP, the BSO algorithm shows a strong ability to explore space [18]. In addition, the application of the BSO algorithm can be extended to image classification [19] and electromagnetic classification [20]. Since BSO demonstrates powerful performance, in this study, we use the BSO algorithm to solve the model. However, since the original BSO algorithm generates new solutions randomly with fixed probability, searching for the solution space is slow and unstable. For this reason, many scholars have made improvements to the BSO algorithm. The performance of the BSO algorithm can be improved on different autonomous system (AS) ontology alignment tasks using the idea of the compact co-evolutionary [21]. To reduce the limitation of the algorithm's exploration capability by generating new individuals, the alternative search pattern strategy can be used to control the transition between grid-based search operators and the BSO. In this way, the BSO's effectiveness in terms of solution quality and population diversity will significantly improve [22]. Adding a reinforcement learning mechanism to the BSO algorithm can improve the comprehensive performance [23]. The BSO algorithm can also be enhanced to find better or explore viable evolutionary routes and speed up the convergence by using the improved Nelder-Mead and elite learning mechanism [24]. Moreover, combining the BSO algorithm with the orthogonal learning design [25] or the SA algorithm [26] can enhance the algorithm's performance as well. The BSO algorithm, as an emerging metaheuristic algorithm, has attracted significant attention from scholars, who have studied and improved it to promote its application and development in various fields. However, despite these efforts, there are still certain shortcomings that need to be addressed. For example, while the improved BSO algorithms have shown some improvements in convergence speed, they still tend to converge relatively slowly. Additionally, it remains challenging to escape from local optima when using these algorithms. LNS, initially proposed by [PERSON] [27], is a metaheuristic approach that gradually improves an initial solution by iteratively destroying and repairing the solution. The destroy method removes a portion of the current solution, and the repair method rebuilds the destroyed portion. The destroy method often includes stochastic elements to ensure that different portions of the solution are removed each time it is applied. One popular approach is to scan all free customers and insert the one with the lowest insertion cost, repeating until all customers are served [28]. LNS is widely used in solving various types of VRP problems due to its powerful local search capability. [PERSON] et al. [29] improved the consistency of solution arrival time by incorporating LNS into solving the consistent vehicle routing problem (ConVRP).