Extended Trail Reinforcement Strategies for Ant Colony Optimization
Ant colony optimization (ACO) is a metaheuristic inspired by the foraging behavior of biological ants that was successfully applied for solving computationally hard problems. The fundamental idea that drives the ACO is the usage of pheromone trails for accumulating experience about the problem that is been solved. The best performing ACO algorithms typically use one, in some sense “the best”, solution to reinforce trail components. Two main trail reinforcement strategies are used in ACO algorithms: iteration best and global best strategy. This paper extends the reinforcement strategies by using the information from an arbitrary number of previous iterations of the algorithm. The influence of proposed strategies on algorithmic behavior is analyzed on different classes of optimization problems. The conducted experiments showed that using the proposed strategies can improve the algorithm’s performance. To compare the strategies we use the Mann–Whitney and Kruskal – Wallis statistical tests.
Keywordsreinforcement strategy pheromone trail MAX-MIN ant system Ant colony optimization Swarm intelligence combinatorial optimization parameter settings
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