A general meta-heuristic based solver for combinatorial optimisation problems

Marcus Randall*, David Abramson

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)


In recent years, there have been many studies in which tailored heuristics and meta-heuristics have been applied to specific optimisation problems. These codes can be extremely efficient, but may also lack generality. In contrast, this research focuses on building a general-purpose combinatorial optimisation problem solver using a variety of meta-heuristic algorithms including Simulated Annealing and Tabu Search. The system is novel because it uses a modelling environment in which the solution is stored in dense dynamic list structures, unlike a more conventional sparse vector notation. Because of this, it incorporates a number of neighbourhood search operators that are normally only found in tailored codes and it performs well on a range of problems. The general nature of the system allows a model developer to rapidly prototype different problems. The new solver is applied across a range of traditional combinatorial optimisation problems. The results indicate that the system achieves good performance in terms of solution quality and runtime.

Original languageEnglish
Pages (from-to)185-210
Number of pages26
JournalComputational Optimization and Applications
Issue number2
Publication statusPublished - 1 Nov 2001


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