2022
DOI: 10.1155/2022/2055241
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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.… Show more

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Cited by 5 publications
(4 citation statements)
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“…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).…”
Section: Introductionmentioning
confidence: 99%
“…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).…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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.…”
Section: Introduction 1current Situation Of Urban Logistics Enterprisesmentioning
confidence: 99%
“…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.…”
Section: Introduction 1current Situation Of Urban Logistics Enterprisesmentioning
confidence: 99%