How Autonomy Oriented Computing (AOC) tackles a computationally hard optimization problem

Xiao Feng Xie*, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

The hard computational problems, such as the traveling salesman problem (TSP), are relevant to many tasks of practical interest, which normally can be well formalized but are difficult to solve. This paper presents an extended multi agent optimization system, called MAOSE, for supporting cooperative problem solving on a virtual landscape and achieving high-quality solution(s) by the self-organization of autonomous entities. The realization of an optimization algorithm then can be described in three parts: a) encode the representation of the problem, which provides the virtual landscape and possible auxiliary knowledge; b) construct the memory elements at the initialization stage; and c) design the generate-and-test behavior guided by the law of socially-biased individual learning, through tailoring to the domain structure. The implementation is demonstrated on the TSP in details. The extensive experimental results on real-world instances in TSPLIB show its efficiency as comparing to other algorithms.

Original languageEnglish
Title of host publicationProceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
Pages646-653
Number of pages8
DOIs
Publication statusPublished - 2006
EventFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS - Hakodate, Japan
Duration: 8 May 200612 May 2006

Publication series

NameProceedings of the International Conference on Autonomous Agents
Volume2006

Conference

ConferenceFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Country/TerritoryJapan
CityHakodate
Period8/05/0612/05/06

Scopus Subject Areas

  • Engineering(all)

User-Defined Keywords

  • Autonomy oriented computing (AOC)
  • Cooperative problem solving
  • Emergent and collective behavior
  • Global optimization
  • Multiagent system
  • Search
  • Traveling salesman problem (TSP)

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