A Multi-world Intelligent Genetic Algorithm to Interactively Optimize Large-scale TSP

To optimize large-scale distribution networks, solving about 1000 middle scale (around 40 cities) TSPs (traveling salesman problems) within an interactive length of time (max. 30 seconds) is required. Yet, expert-level (less than 3% of errors) accuracy is necessary. To realize the above requirements...

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Bibliographic Details
Main Authors: Sakurai, Y., Onoyama, T., Kubota, S., Nakamura, Y., Tsuruta, S.
Format: Conference Proceeding
Language:English
Subjects:
Cities and towns
Costs
Delay
Genetic algorithms
Humans
Intelligent networks
Large-scale systems
Production facilities
Software engineering
Traveling salesman problems
Online Access:Request full text
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Summary:To optimize large-scale distribution networks, solving about 1000 middle scale (around 40 cities) TSPs (traveling salesman problems) within an interactive length of time (max. 30 seconds) is required. Yet, expert-level (less than 3% of errors) accuracy is necessary. To realize the above requirements, a multi-world intelligent GA method was developed. This method combines a high-speed GA with an intelligent GA holding problem-oriented knowledge that is effective for some special location patterns. If conventional methods were applied, solutions for more than 20 out of 20,000 cases were below expert-level accuracy. However, the developed method could solve all of 20,000 cases at expert-level
DOI:10.1109/IRI.2006.252421