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Adaptive Ant Colony Optimization with Cranky Ants

  • Masaya YoshikawaEmail author
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 52)

Abstract

Ant Colony Optimization (ACO) is the algorithm inspired by the feeding behavior of ants and its search mechanism is based on the positive feedback reinforcement using pheromone communication. This chapter discusses a new adaptive ACO algorithm and its characteristics are as follows: (1) a novel cranky ant who behaves strangely is introduced to strengthen the pressure of diversification, (2) a new observation technique for the convergence behavior is employed to judge whether it is trapping at local optimal solution. Experiments using benchmark data prove that the proposed algorithm with the cranky ants and the observation technique enables to control the trade-off between intensification and diversification, in comparison with conventional ACO.

Ant Colony Optimization Cranky ant Adaptive optimization Intensification and diversification Convergence behavior 

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Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Department of Information EngineeringMeijo UniversityNagoyaJapan

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