A New Ant Colony Optimization Algorithm with an Escape Mechanism for Scheduling Problems

  • Authors:

  • Tsai-Duan Lin;Chuin-Chieh Hsu;Da-Ren Chen;Sheng-Yung Chiu

  • Affiliations:

  • Department of Information Management, Hwa Hsia Institute of Technology, Taipei County, Taiwan 23554;Department of Information Management, National Taiwan University of Science and Technology, Email:M9209004@mail.ntust.edu.tw, Taipei, Taiwan 69042;Department of Information Management, Hwa Hsia Institute of Technology, Taipei County, Taiwan 23554;Department of Information Management, National Taiwan University of Science and Technology, Email:M9209004@mail.ntust.edu.tw, Taipei, Taiwan 69042

  • Venue:
  • ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
  • Year:
  • 2009

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Abstract

Ant colony optimization (ACO) algorithm is an evolutionary technologyoften used to resolve difficult combinatorial optimization problems, such as single machine scheduling problems, flow shop or job shop scheduling problems, etc. In this study, we propose a new ACO algorithm with an escape mechanism modifying the pheromone updating rules to escape local optimal solutions. The proposed method is used to resolve a single machine total weighted tardiness problem, a flow shop scheduling problem for makespan minimization, and a job shop scheduling problem for makespan minimization. Compared with existing algorithms, the proposed algorithm will resolve the scheduling problems with less artificial ants and obtain better or at least the same, solution quality.