A general meta-heuristic based solver for combinatorial optimisation problems

In recent years, there have been many studies in which tailored heuristics and meta-heuristics have been applied to specific optimisation problems. Th ese codes can be extremely efficient, but may also lack generality. In cont rast, this research focuses on building a general-purpose combinatorial opt imisation problem solver using a variety of meta-heuristic algorithms inclu ding Simulated Annealing and Tabu Search. The system is novel because it us es a modelling environment in which the solution is stored in dense dynamic list structures, unlike a more conventional sparse vector notation. Becaus e 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 o f problems. The general nature of the system allows a model developer to ra pidly prototype different problems. The new solver is applied across a rang e of traditional combinatorial optimisation problems. The results indicate that the system achieves good performance in terms of solution quality and runtime.