Convergence time analysis of ant system algorithm
by Yu-shan Zhang; Zhi-feng Hao; Han Huang
International Journal of Modelling, Identification and Control (IJMIC), Vol. 15, No. 4, 2012

Abstract: Ant colony optimisation (ACO) which is one of the most popular algorithms in machine learning has been used widely to solve combinatorial optimisation problems. However, there are few studies for its runtime analysis which can reflect the computational complexity of ACO algorithm. The presented paper proposes a method for analysing the convergence time of ant system algorithm with pheromone rate. The analysis is a process of estimating the iteration time that pheromone rate attains the objective value and the mean convergence time based on the objective pheromone rate in expectation. The proposed method can be used to analyse the computational complexity of other ACO algorithms. Finally, a brief ant system algorithm is analysed as an example of using the method.

Online publication date: Sat, 29-Nov-2014

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