Naslov
Extended Trail Reinforcement Strategies for Ant Colony Optimization

Autori
Ivković, Nikola ; Maleković, Mirko ; Golub, Marin

Izvornik
Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science (0302-9743) 7076 (2011), 1; 662-669

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
reinforcement strategy; pheromone trail; MAX-MIN ant system; Ant colony optimization; Swarm intelligence; combinatorial optimization; parameter settings

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti

POVEZANOST RADA