Second Order Swarm Intelligence
Abstract
An artificial Ant Colony System (ACS) algorithm to solve general-purpose combinatorial Optimization Problems (COP) that extends previous AC models [21] by the inclusion of a negative pheromone, is here described. Several Travelling Salesman Problem (TSP) were used as benchmark. We show that by using two different sets of pheromones, a second-order co-evolved compromise between positive and negative feedbacks achieves better results than single positive feedback systems. The algorithm was tested against known NP-complete combinatorial Optimization Problems, running on symmetrical TSP's. We show that the new algorithm compares favourably against these benchmarks, accordingly to recent biological findings by Robinson [26,27], and Gruter [28] where "No entry" signals and negative feedback allows a colony to quickly reallocate the majority of its foragers to superior food patches. This is the first time an extended ACS algorithm is implemented with these successful characteristics.
Keywords
Cite
@article{arxiv.1306.3018,
title = {Second Order Swarm Intelligence},
author = {Vitorino Ramos and David M. S. Rodrigues and Jorge Louçã},
journal= {arXiv preprint arXiv:1306.3018},
year = {2013}
}
Comments
10 pages, 5 figures, accepted to International Conference on Hybrid Artificial Intelligence Systems (HAIS 2013), Lecture Notes in Artificial Intelligence, LNAI Springer Series