JAMES: An object-oriented Java framework for discrete optimization using local search metaheuristics

This paper describes JAMES, a modern object-oriented Java framework for discrete optimization using local search algorithms that exploits the generality of such metaheuristics by clearly separating search implementation and application from problem specification. A wide range of generic local searches are provided, including (stochastic) hill climbing, tabu search, variable neighbourhood search and parallel tempering. These can be applied easily to any user-defined problem by plugging in a custom neighbourhood for the corresponding solution type. The performance of several different search algorithms can be assessed and compared in order to select an appropriate optimization strategy. Also, the influence of parameter values can be studied. Implementations of specific components are included for subset selection, such as a predefined solution type, a generic problem definition and several subset neighbourhoods used to modify the set of selected items. Additional components for other types of problems (e.g. permutation problems) are provided through an extensions module. Releases of JAMES are deployed to the Maven Central Repository so that the framework can easily be included as a dependency in other Java applications. The project is fully open source and hosted on GitHub. More information can be found at http://www.jamesframework.org.