Green supply chain management and coordinated optimization by an improved sparrow search algorithm.

ABSTRACT: This study addresses key limitations of traditional algorithms in green supply chain management, including vulnerability to local optima, unsystematic population initialization, and inadequate parameter tuning. To overcome these challenges, a collaborative optimization framework is developed by integrating an Improved Sparrow Search Algorithm (ISSA) with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), forming the ISSA-NSGA-III model. The model begins with Tent chaotic mapping to generate a well-distributed initial population, ensuring broad coverage of the solution space. An adaptive periodic convergence factor is then introduced to dynamically balance global exploration and local exploitation during the optimization process. To further strengthen search performance, Lévy fligh

SUBMITTER: Wang G 

PROVIDER: S-EPMC12541026 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature