A Novel Artificial Eagle-Inspired Optimization Algorithm for Trade Hub Location and Allocation Method

Area of Science:

• Operations Research
• Computational Intelligence
• Optimization Algorithms

Background:

• Efficient goods transfer between towns is crucial for economic activity.
• Existing methods for trade hub location and allocation may not be cost-effective or optimal.
• Minimizing construction and transportation costs is a key objective in logistics network design.

Purpose of the Study:

• To propose a novel method for trade hub location and allocation.
• To develop an artificial eagle optimization algorithm (AEOA) for solving this complex problem.
• To reduce overall costs associated with goods transfer.

Main Methods:

• Formulated a trade hub location and allocation model with construction and transportation costs as the objective function.
• Developed a novel artificial eagle optimization algorithm (AEOA) inspired by eagle migration behaviors.
• Incorporated situational awareness, free exploration, and formation flying strategies into the AEOA.

Main Results:

• The AEOA demonstrated superior performance on 11 out of 12 benchmark functions compared to eight other algorithms.
• Quantitative analysis confirmed the AEOA's faster convergence speed and enhanced stability.
• Simulated case studies showed the method's ability to select optimal hubs and reduce costs.

Conclusions:

• The proposed trade hub location and allocation method, powered by AEOA, effectively minimizes logistics costs.
• The AEOA is a robust and efficient algorithm for complex optimization problems.
• This approach provides a valuable tool for governmental decision-making in logistics planning, as demonstrated in the Henan province case study.