High-Performance Computing Analysis and Location Selection of Logistics Distribution Center Space Based on Whale Optimization Algorithm

Abstract

As a meta-heuristic algorithm based on swarm intelligence, the WOA algorithm has few control parameters and searches for the optimal solution by encircling the prey, searching for the prey, and attacking the bubble net. During the whole process, only two internal parameters A and C are utilized for the control of the exploration and development process. BWOA is simple to implement. In the process of algorithm execution, the initial population, global exploration, and local development stages have shortcomings. Therefore, it is necessary to optimize the WOA algorithm. Based on WOA, this study conducts a high-performance computing analysis and location selection of logistics distribution center space. It is concluded that: (1) by using the combination of direct logistics distribution and hierarchical logistics distribution, the WOA algorithm optimizes the cross selection strategy, the population fitness S-LO is improved, the quality of LA is guaranteed, and the chaotic S-LO mapping eliminates inferior individuals in the population. Direct distribution is carried out for bulky goods and important distribution customers, and hierarchical logistics distribution is used for customers in intensive logistics distribution destinations. (2) WOA uses the second reverse learning, chaotic mapping, and logistic chaotic mapping to improve the location update mode. The direct distribution method is mostly used for the logistics business with short journeys, fixed distribution points, and more goods delivered at one time, and logistics enterprises do not need to store and distribute goods. The uniform ergodicity of the Tent chaotic map and logistic chaotic map is improved. Ka adaptive inertia weights are a good complement to optimize the limitations of the Ao whale algorithm. (3) The inertia weight of the levy flight behavior can play a powerful role in balancing the global exploration ability and optimization performance of the intelligent algorithm. The long-term short-distance search of HED and the long-distance jump of KVAR are combined. Variant individuals undergo vector synthesis. It reduces the construction and operation costs of logistics sites and is suitable for logistics distribution under specific conditions.

Selection of the Location of the Distribution Center for Agricultural Products

“…Supporting this, Mihajlović et al (2019) note that the location selection problem plays a crucial role in logistics, making it very important to find the most desirable distribution centre location. The choice of location for distribution centres can expedite sales and procurement while reducing logistics costs, providing economic benefits to both suppliers and customers (Yang, Song, 2022). In essence, the selection of a distribution centre is a crucial strategic decision for every company (Agrebi, Abed, 2021).…”

The Application of the Improved Jellyfish Search Algorithm in a Site Selection Model of an Emergency Logistics Distribution Center Considering Time Satisfaction

“…With the increase in the number of variables and constraints in research on siting problems, various new heuristics, such as particle swarm optimization (PSO), grey wolf optimization (GWO), the whale optimization algorithm (WOA), etc. [ 15 , 16 , 17 , 18 , 19 ], have been proposed by different scholars in recent years to solve the siting problem and conduct simulation studies. Although these heuristic algorithms have been well validated and applied in problem-solving, they often have disadvantages such as premature local convergence and poor robustness, which may lead to results with poor accuracy when solving large data models.…”

The Improvement of the Honey Badger Algorithm and Its Application in the Location Problem of Logistics Centers

“…Another aspect is to use various intelligent optimization algorithms to solve the problem of logistics site selection-path optimization. Among them, most scholars use heuristic algorithms or meta-heuristics or hybrid heuristics [5][6][7][8][9][10][11][12][13][14][15], such as cuckoo search algorithm, gray wolf optimization algorithm, ant colony algorithm, genetic algorithm, simulated annealing algorithm, differential evolution strategy algorithm, etc., which are suitable for mediumscale and large-scale problems. The authors of [5] use the longhorn beetle search-rainfall algorithm for the site selection of logistics center; in [6], the differential evolution strategy is used to improve the cuckoo search algorithm to improve the accuracy of logistics center site selection.…”

“…The authors of [7] use gray wolf optimization algorithm to optimize this problem; ref. [8] improved the whale optimization algorithm to study this problem; ref. [9] uses the improved ant colony algorithm to optimize logistics and distribution routes.…”