Ant-Q Hyper-Heuristic Approach for solving 2-Dimensional Cutting Stock Problem

Hyper-heuristics are new approaches which aim at raising the level of abstraction when solving combinatorial optimisation problems. In this paper we introduce a new hyper-heuristic model, namely Ant-Q hyper-heuristic, which transliterates the significant learning ability of Ant-Q algorithm proposed by Gambardella and Dorigo, for building good sequences of low-level heuristics aimed at gradually constructing final solutions. This approach was applied to 2-dimensional Cutting Stock Problem and tested through a large set of benchmark problems. The results have shown that the Ant-Q hyper heuristic is able to outperform single heuristics, well known metaheuristics and be competitive to other hyperheuristics from the literature.

• Type: Conference
• Year: 2011
• Where: IEEE Symposium Series on Computational Intelligence (SSCI 2011)
• Authors: Imen Khamassi, Moez Hammami, Khaled Ghédira
• Attachments: 1 file(s)
• Keywords: Cutting Stock Problem, Hyper Heuristic, Optimisation Problems