Computing with Bees: Attacking Complex Transportation Engineering Problems-Reference-Cited by-同舟云学术

Computing with Bees: Attacking Complex Transportation Engineering Problems

Published:2003-09 Issue:03 Volume:12 Page:375-394
ISSN:0218-2130
Container-title:International Journal on Artificial Intelligence Tools
language:en
Short-container-title:Int. J. Artif. Intell. Tools

Author:

Lučić Panta¹,Teodorović Dušan¹

Affiliation:

1. The Charles E. Via Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Northern Virginia Center, 7054 Haycock Road, Falls Church, VA 22043-2311, U.S.A.

Abstract

The Bee System (an artificial bee swarm) is introduced in this paper. The proposed approach is applied to the Traveling Salesman Problem. The obtained results are very promising. The potential applications of the developed Bee System in the field of transportation engineering are discussed. The Bee System represents the new, successful application of emergent techniques based on natural metaphors, such as simulated annealing, genetic algorithms, and neural networks, to the complex engineering and management problems.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218213003001289

Reference52 articles.

1. Equation of State Calculations by Fast Computing Machines

2. Optimization by Simulated Annealing

3. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm

Cited by 125 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Metaheuristics and Machine Learning Convergence;Advances in Systems Analysis, Software Engineering, and High Performance Computing;2024-06-30

2. A comprehensive review on optimization-based image watermarking techniques for copyright protection;Expert Systems with Applications;2024-05

3. Formulation of the airport collaborative gate allocation problem and the Bee Colony Optimization solution approach;Engineering Applications of Artificial Intelligence;2024-02

4. NSGA-II-DL: Metaheuristic Optimal Feature Selection With Deep Learning Framework for HER2 Classification in Breast Cancer;IEEE Access;2024

5. AI‐Enabled Techniques for Intelligent Transportation System for Smarter Use of the Transport Network for Healthcare Services;Blockchain and Deep Learning for Smart Healthcare;2023-11-14