Haiku - a Scala combinator toolkit for semi-automated composition of metaheuristics

There is an emerging trend towards the automated design of metaheuristics at the software component level. In principle, metaheuristics have a relatively clean decomposition, where well-known frameworks such as ILS and EA are parametrised by variant components for acceptance, perturbation etc. Automated generation of these frameworks is not so simple in practice, since the coupling between components may be implementation specific. Compositionality is the ability to freely express a space of designs ‘bottom up’ in terms of elementary components: previous work in this area has used combinators, a modular and functional approach to componentisation arising from foundational Computer Science. In this article, we describeHaiku, a combinator tool-kit written in the Scala language, which builds upon previous work to further automate the process by automatically composing the external dependencies of components. We provide examples of use and give a case study in which a programatically-generated heuristic is applied to the Travelling Salesman Problem within an Evolutionary Strategies framework

Conflict-free routing of multi-stop warehouse trucks

The recent interest in greater vehicular autonomy for factory and warehouse automation has stimulated research in conflict-free routing: a challenging network routing problem in which vehicles may not pass each other. Motivated by a real-world case study, we consider one such application: truck movements in a tightly constrained warehouse. We propose an extension of an existing conflict-free routing algorithm to consider multiple stopping points per route. A high level metaheuristic is applied to determine the route construction and assignment of vehicles to routes

Extending the ‘Open-Closed Principle’ to automated algorithm configuration

Metaheuristics are an effective and diverse class of optimization algorithms: a means of obtaining solutions of acceptable quality for otherwise intractable problems. The selection, construction, and configuration of a metaheuristic for a given problem has historically been a manually intensive process based on experience, experimentation, and reasoning by metaphor. More recently, there has been interest in automating the process of algorithm configuration. In this paper, we identify shared state as an inhibitor of progress for such automation. To solve this problem, we introduce the Automated Open Closed Principle (AOCP), which stipulates design requirements for unintrusive reuse of algorithm frameworks and automated assembly of algorithms from an extensible palette of components. We demonstrate how the AOCP enables a greater degree of automation than previously possible via an example implementation.PostprintPeer reviewe