Advanced search
1 file | 374.44 KB

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

Author
Organization
Abstract
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.
Keywords
Java framework, object-oriented architecture, local search, Discrete optimization, metaheuristics

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 374.44 KB

Citation

Please use this url to cite or link to this publication:

Chicago
De Beukelaer, Herman, Guy F Davenport, Geert De Meyer, and Veerle Fack. 2015. “JAMES : a Modern Object-oriented Java Framework for Discrete Optimization Using Local Search Metaheuristics.” In Conference Proceedings : 4th International Symposium & 26th National Conference on Operational Research, ed. Michael Doumpos and Evangelos Grigoroudis, 134–138. Hellenic Operational Research Society.
APA
De Beukelaer, H., Davenport, G. F., De Meyer, G., & Fack, V. (2015). JAMES : a modern object-oriented Java framework for discrete optimization using local search metaheuristics. In M. Doumpos & E. Grigoroudis (Eds.), Conference proceedings : 4th international symposium & 26th national conference on operational research (pp. 134–138). Presented at the 4th International symposium and 26th National conference on Operational Research, Hellenic Operational Research Society.
Vancouver
1.
De Beukelaer H, Davenport GF, De Meyer G, Fack V. JAMES : a modern object-oriented Java framework for discrete optimization using local search metaheuristics. In: Doumpos M, Grigoroudis E, editors. Conference proceedings : 4th international symposium & 26th national conference on operational research. Hellenic Operational Research Society; 2015. p. 134–8.
MLA
De Beukelaer, Herman, Guy F Davenport, Geert De Meyer, et al. “JAMES : a Modern Object-oriented Java Framework for Discrete Optimization Using Local Search Metaheuristics.” Conference Proceedings : 4th International Symposium & 26th National Conference on Operational Research. Ed. Michael Doumpos & Evangelos Grigoroudis. Hellenic Operational Research Society, 2015. 134–138. Print.
@inproceedings{5976034,
  abstract     = {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.},
  author       = {De Beukelaer, Herman and Davenport, Guy F and De Meyer, Geert and Fack, Veerle},
  booktitle    = {Conference proceedings : 4th international symposium \& 26th national conference on operational research},
  editor       = {Doumpos, Michael and Grigoroudis,  Evangelos},
  isbn         = {9786188036147},
  language     = {eng},
  location     = {Chania, Crete, Greece},
  pages        = {134--138},
  publisher    = {Hellenic Operational Research Society},
  title        = {JAMES : a modern object-oriented Java framework for discrete optimization using local search metaheuristics},
  url          = {http://www.helors2015.tuc.gr/Proceedings.pdf},
  year         = {2015},
}