# An Improved Spider Wasp Optimizer for Green Vehicle Route Planning in Flower Collection
## 1 Introduction  
Under general logistics transportation conditions, the damage rate for fresh flowers can reach as high as 30% [1]. Consequently, freshly harvested flowers must be immediately transported to refrigerated warehouses using refrigerated vehicles. This requirement arises due to the delicate nature of flowers, which are highly perishable and sensitive to environmental factors such as temperature and humidity. The use of refrigerated vehicles is therefore crucial to maintaining their quality during transit. Although cold chain logistics can effectively preserve the quality of perishable products such as flowers, it is associated with higher energy consumption and carbon emissions compared to conventional logistics [2]. Therefore, cold chain logistics enterprises need to accelerate their transition to a low-carbon economy while exploring sustainable development pathways [3]. This requires innovations in technology and the optimization of logistics processes to reduce the carbon footprint of these operations. This imposes higher requirements on route planning in cold chain logistics, mandating not only stringent control over total transportation costs but also the effective minimization of carbon emissions. An increasing number of scholars are incorporating environmental factors, such as carbon emissions, into the study of cold chain vehicle routing planning, thereby giving rise to the concept of green cold chain vehicle routing planning [4, 5]. This approach aimsto reduce the environmental impact of cold chain logistics while maintaining operational efficiency. Green cold chain vehicle routing planning is a variant of the Vehicle Routing Problem (VRP) [6]. The VRP was first introduced by [PERSON] and [PERSON] in 1959 [7] and has since become a widely studied combinatorial optimization problem in the fields of operations research and optimization. Designing optimization models that can be applied in real-world scenarios has long been a key focus for scholars. Similarly, developing highly efficient algorithms has also been a major emphasis. Regarding algorithms, exact algorithms invariably encounter the challenge of exponential complexity, whereas classical heuristic algorithms frequently become trapped in local optima [8]. Consequently, many researchers now prefer to use metaheuristic algorithms to solve the VRP [9; 10], as these algorithms not only generate high-quality solutions but also offer faster solution times when dealing with complex and large-scale problems. The contributions of this study are as follows:  
1. For the flower collection problem, a green cold chain vehicle routing planning model incorporating hard time window constraints was developed. This model considers fixed costs, refrigeration costs, transportation costs, carbon emissions costs, flower damage costs, and time penalty costs. It addresses the practical aspects of flower collection by introducing a new method for calculating refrigeration costs. The model strictly adheres to customer-specified collection times to optimize customer satisfaction. 2. To address the challenges of complex optimization objectives and extensive solution spaces, a novel Genetic Neighborhood Comprehensive Spider Wasp Algorithm (GN_CSWA) is proposed. The algorithm combines random generation with the nearest neighbor algorithm to generate diverse and high-quality initial solutions. It employs three selection strategies--roulette wheel selection, elite selection, and best individual retention strategy--to select individuals for the next iteration, ensuring that the selected population has good genetic material and sufficient diversity. The initial search is conducted using the Partial Mapping Crossover (PMX) operator. Based on the time-distance matrix and elite chromosome information, two new search operators are designed. Meanwhile, in-route and out-route search operators, as well as two mutation operators, are introduced to conduct further searches. 3.