Optimizing Artificial Neural Networks with Swarm Intelligence Algorithms for Biotechnological Applications in Logistics

Junliang Wu
Nanchang JiaoTong Institute, Nanchang 330100, Jiangxi, China
Liqing Mao
Nanchang JiaoTong Institute, Nanchang 330100, Jiangxi, China

DOI:https://doi.org/10.5912/jcb1356

Abstract:

Artificial Neural Networks (ANNs) are intricate mathematical models inspired by the human neural system, holding great promise in the development of artificial intelligence. In the context of biotechnological applications within logistics, ANNs offer robust adaptability and fault tolerance. However, to unlock their full potential, the integration of swarm intelligence algorithms for parameter optimization has gained attention. This paper addresses the intersection of artificial intelligence, biotechnology, and logistics. Specifically, we focus on the critical task of efficiently constructing neural network models to enhance their performance in biotechnological logistics applications. Two key research areas are explored: Innovative Optimization Strategies: We propose novel strategies for optimizing ANNs using swarm intelligence algorithms. These strategies incorporate inverse learning techniques and neighborhood perturbation operations with dynamic probabilities. They expedite the escape from local optima and enhance the accurate exploration of adjacent regions. Such optimization is vital for effectively adapting ANNs to the complex challenges presented by biotechnological logistics. Freight Volume Forecasting in Biotechnology Logistics: Our research investigates the multifaceted factors influencing freight volume in the context of biotechnological logistics. We consider both the demands of the biotechnology sector and the logistical supply chain, identifying key indicators with strong correlations to freight volume. This analysis aids in refining predictions and optimizing resource allocation in biotechnological logistics. This paper underscores the convergence of artificial neural networks and swarm intelligence algorithms, emphasizing their application within the biotechnological logistics domain. The proposed optimization strategies aim to elevate the performance of ANNs in addressing the unique challenges posed by biotechnological logistics, ultimately fostering efficiency and precision. We outline future research directions to further harness the potential of these techniques for biotechnological applications in logistics.