Improved Swarm Intelligence-Based Logistics Distribution Optimizer: Decision Support for Multimodal Transportation of Cross-Border E-Commerce

Real-time processing demands are also increasing significantly with the proliferation of Internet of Things (IoT)-based smart systems. At the same time, the volume of data generated, and computational workload are also increasing significantly. In this regard, fog-cloud architectures are proposed to alleviate the excessive load on cloud servers. However, since the resources of fog nodes and the virtual machines on them are limited, efficient management mechanisms are required. As the volume and amount of data increases, computational and processing costs increase, and delays are inevitable. However, these requirements need to be resolved to increase QoS and customer satisfaction. One of the most important criteria to achieve this goal is accurate and effective task allocation and scheduling. Since the scheduling is a type of NP-hard problem, the metaheuristic approach is used. In this study, an Enhanced variant of the Sand Cat Swarm Optimization algorithm (ESCSO) is proposed to efficient scheduling according to tasks priorities and a suitable fitness function based on completion (makespan) time, energy consumption and execution cost parameters is defined. In the proposed algorithm, global search ability and population diversity of the SCSO is improved based on the chaotic map. Also, its exploration and exploitation mechanisms are enhanced based on Brownian and Levy motion strategies. Besides, the control mechanism of the phases transition is redefined to increase balancing performance. The proposed method is compared with SCSO, Improved Artificial Hummingbird Algorithm (IAHA), Discrete Moth Flame Optimization (DMFO), Enhanced Particle Swarm Optimization (EPSO), Chaotic-Whale Optimization Algorithm (CWOA), Modified Harris-Hawks Optimization (MHHO), and Hybrid Artificial Ecosystem Optimization and Salp Swarm Algorithm (AEOSSA) algorithms and analyzed on the three parameters in two different scenarios. The obtained results present that the ESCSO algorithm outperform others in all cases.

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Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data

Zhang,

Guo

2024

Applied Sciences

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This study deeply integrates multimodal data analysis and big data technology, proposing a multimodal learning framework that consolidates various information sources, such as user geographic location, behavior data, and product attributes, to achieve a more comprehensive understanding and prediction of consumer behavior. By comparing the performance of unimodal and multimodal approaches in handling complex cross-border e-commerce data, it was found that multimodal learning models using the Adam optimizer significantly outperformed traditional unimodal learning models in terms of prediction accuracy and loss rate. The improvements were particularly notable in training loss and testing accuracy. This demonstrates the efficiency and superiority of multimodal methods in capturing and analyzing heterogeneous data. Furthermore, the study explores and validates the potential of big data and multimodal learning methods to enhance customer satisfaction in the cross-border e-commerce environment. Based on the core findings, specific applications of big data technology in cross-border e-commerce operations were further explored. A series of innovative strategies aimed at improving operational efficiency, enhancing consumer satisfaction, and increasing global market competitiveness were proposed.

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Improved Swarm Intelligence-Based Logistics Distribution Optimizer: Decision Support for Multimodal Transportation of Cross-Border E-Commerce

Cited by 4 publications

References 39 publications

Advances in Sand Cat Swarm Optimization: A Comprehensive Study

Advances in Sand Cat Swarm Optimization: A Comprehensive Study

A multi-strategy sand cat swarm optimization to efficient resource allocation and scheduling in fog-cloud environment

Research on Multimodal Prediction of E-Commerce Customer Satisfaction Driven by Big Data

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