A mixed ant colony algorithm for function optimization

Authors:
Hong-yan Shi;Zhao-yu Bei
Affiliations:
School of Information Science & Engineering, Shenyang University of Technology, China;School of Information Science & Engineering, Shenyang University of Technology, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

Ant colony algorithm(ACA) is a novel simulated evolutionary algorithm, which is based on the process of ants in the nature searching for food. ACA has many good features in optimization, but it has the limitations of stagnation and poor convergence, and is easy to fall in local optimization. Pointing at these disadvantages, Artificial fish-swarm algorithm(AFSA) is presented to conquer the disadvantages. The algorithm of rapid search capability of AFSA and the good search characteristics of ACO, and the convergent speed of the presented algorithm avoiding being trapped in local optimum is improved.