Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Abstract

Effectively managing complex logistics data is essential for development sustainability and growth, especially in optimizing distribution routes. This article addresses the limitations of current logistics path optimization methods, such as inefficiencies and high operational costs. To overcome these drawbacks, we introduce the Hybrid Firefly-Spotted Hyena Optimization (HFSHO) algorithm, a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm (FO) with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm (SHO). HFSHO aims to improve logistics path optimization and reduce operational costs. The algorithm's effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm (ACO), Cuckoo Search Algorithm (CSA) and Jaya Algorithm (JA). The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions, including Schwefel's, Rastrigin, Ackley, Sphere and the ZDT and DTLZ Function suite. HFSHO outperforms these algorithms, achieving a minimum path distance of 546 units, highlighting its prowess in logistics path optimization. This comprehensive evaluation authenticates HFSHO's exceptional performance across various logistic optimization scenarios. These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation, with HFSHO emerging as an efficient choice. Through the synergistic use of FO and SHO, HFSHO achieves a 15% improvement in convergence, heightened operational efficiency and substantial cost reductions in logistics operations. It presents a promising solution for optimizing logistics paths, offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.