Abstract
BackgroundAnt colony algorithm has emerged recently as a new meta-heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems. However, the classical ant colony algorithm also has its defects of stagnation and premature. This paper aims at remedying these problems.ResultsIn this paper, we propose an adaptive ant colony algorithm that simulates the behaviour of biological immune system. The solutions of the problem are much more diversified than traditional ant colony algorithms.ConclusionThe proposed method for improving the performance of traditional ant colony algorithm takes into account the polarization of the colonies, and adaptively adjusts the distribution of the solutions obtained by the ants. This makes the solutions more diverse so as to avoid the stagnation and premature phenomena.
Highlights
Ant colony algorithm has emerged recently as a new meta-heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems
With the further study in this area, ant colony algorithm is widely applied to the problems of Quadratic Assignment Problem (QAP) [4], the Frequence Assignment Problem (FAP) [5], the Sequential Ordering Problem (SOP) and some other NP-complete problems
We simulate the behaviours of immune recomposition, immune memory, immune selection and density control etc.; design diversity guaranteed ant colony algorithm (DGAA) to solve different kinds of optimization problems
Summary
Ant colony algorithm has emerged recently as a new meta-heuristic method, which is inspired from the behaviours of real ants for solving NP-hard problems. With the further study in this area, ant colony algorithm is widely applied to the problems of Quadratic Assignment Problem (QAP) [4], the Frequence Assignment Problem (FAP) [5], the Sequential Ordering Problem (SOP) and some other NP-complete problems. It demonstrates its superiority of solving complicated combinatorial optimization problems. AC algorithms have been inspired by colonies of real ants, which deposit a chemical substance called pheromone on the ground This substance influences the choices they make: the more pheromone is on a particular path, the larger the probability is that an ant selects the path.
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