On the Pheromone Update Rules of Ant Colony Optimization Approaches for the Job Shop Scheduling Problem

Authors:
Dong Do Duc;Huy Q. Dinh;Huan Hoang Xuan
Affiliations:
Department of Computer Science, College of Technology, Vietnam National University, Hanoi, Hanoi, Vietnam;Gregor Mendel Institute of Molecular Plant Biology, Vienna, Austria and Center for Integrative Bioinformatics Vienna, Vienna, Austria;Department of Computer Science, College of Technology, Vietnam National University, Hanoi, Hanoi, Vietnam
Venue:
PRIMA '08 Proceedings of the 11th Pacific Rim International Conference on Multi-Agents: Intelligent Agents and Multi-Agent Systems
Year:
2008

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Abstract

Ant Colony Optimization (ACO) system is an intelligent multi-agent system of the interacting artificial ants to solve the combinatorial optimization problems. Applying ACO approach in the typical NP-hard problem like job shop scheduling (JSS) problem is still an impressive and attractive challenge with the community. This paper proposes two improvements of ACO algorithm based on the convergence property of pheromone trails. Our improvements are better in both terms of accuracy and running time than the state-of-the-art Max-Min ant system by the simulation with the standard data sets.