Ant Colony Optimization (ACO) heuristic provides a relatively easy and direct method to handle problem's constraints (through introducing the so called solution construction process), while in the other heuristics, constraint-handling is normally sophisticated. But this makes its solving process slow for the solution construction process occupies most part of its computation time. In this paper, we propose a strategy to hybridize Hopfield discrete neural networks (HDNN) with ACO heuristic for maximum independent set (MIS) problems. Several simulation instances showed that the strategy can greatly improve ACO heuristic performance not only in time cost but also in solution quality. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Li, Y., Xu, Z., & Cao, F. (2008). An improvement to ant colony optimization heuristic. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5263 LNCS, pp. 816–825). Springer Verlag. https://doi.org/10.1007/978-3-540-87732-5_90
Mendeley helps you to discover research relevant for your work.