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Recent trends in metaheuristics for stochastic combinatorial optimization

Walter Gutjahr
Published Online: 2011-03-25 | DOI: https://doi.org/10.2478/s13537-011-0003-3

Abstract

This short overview is an addendum to a recent literature survey by Bianchi et al. on metaheuristics for stochastic combinatorial optimization (SCO). It outlines some new developments that occurred in this field during the last few years. Special attention is given to multi-objective SCO as well as to combinations of metaheuristics with mathematical programming.

Keywords: metaheuristics; combinatorial optimization; stochastic optimization; optimization under uncertainty; multiobjective optimization

  • [1] Bianchi L., Dorigo M., Gambardella L., Gutjahr W.J., A survey on metaheuristics for stochastic combinatorial optimization, NAT COMP, 2009, 8, 239–287 http://dx.doi.org/10.1007/s11047-008-9098-4CrossrefGoogle Scholar

  • [2] Jin Y., Branke J., Evolutionary optimization in uncertain environments — a survey, IEEE T EVOLUT COMPUT, 2005, 9, 303–17 http://dx.doi.org/10.1109/TEVC.2005.846356CrossrefGoogle Scholar

  • [3] Gendreau M., Potvin Y., Handbook of Metaheuristics, 2nd Edition, International Series in Operations Research and Management Science vol. 146, Springer, New York, 2010 http://dx.doi.org/10.1007/978-1-4419-1665-5CrossrefGoogle Scholar

  • [4] Birattari M., Balaprakash P., Stützle T., Dorigo M., Estimation-based local search for stochastic combinatorial optimization using delta evaluations: a case study on the probabilistic traveling salesman problem, INFORMS J COMPUT, 2008, 20, 644–658 http://dx.doi.org/10.1287/ijoc.1080.0276CrossrefGoogle Scholar

  • [5] Balaprakash P., Birattari M., Stützle T., Dorigo M., Adaptive sample size and importance sampling in estimationbased local search for the probabilistic traveling salesman problem, EUR J OPER RES, 2009, 199, 98–110 http://dx.doi.org/10.1016/j.ejor.2008.11.027CrossrefGoogle Scholar

  • [6] Balaprakash P., Birattari M., Stützle T., Dorigo M., Estimation-based metaheuristics for the probabilistic traveling salesman problem, COMPUT OPER RES, 2010, 37, 1939–1951 http://dx.doi.org/10.1016/j.cor.2009.12.005CrossrefGoogle Scholar

  • [7] Hansen P., Mladenovic N., Moreno Perez J., Variable neighbourhood search: methods and applications, ANN OPER RES, 2010, 175, 367–407 http://dx.doi.org/10.1007/s10479-009-0657-6CrossrefGoogle Scholar

  • [8] Gutjahr W.J., Katzensteiner S., Reiter P., A VNS algorithm for noisy problems and its application to project portfolio analysis, In: Hromkovic J. et al. (Ed.), Proc. SAGA 2007 (Stochastic Algorithms: Foundations and Applications) (2007, Berlin Heidelberg), Springer, 2007, 93–104 Google Scholar

  • [9] Schilde M., Doerner K.F., Hartl R.F., Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports, Dept. of Business Administration, University of Vienna, 2010 Google Scholar

  • [10] Dorigo M., Stützle T., Ant Colony Optimization: Overview and Recent Advances, In: Gendreau M., Potvin J.-Y. (Eds.), Handbook of Metaheuristics, International Series in Operations Research and Management Science vol. 146, Springer US, New York, 2010 Google Scholar

  • [11] Poli R., Kennedy J., Blackwell T., Particle swarm optimization, SWARM INTELLIGENCE, 2007, 1, 33–57 http://dx.doi.org/10.1007/s11721-007-0002-0CrossrefGoogle Scholar

  • [12] Balaprakash P., Birattari M., Stützle T., Yuan Z., Dorigo M., Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem, SWARM INTELLIGENCE, 2009, 3, 223–242 http://dx.doi.org/10.1007/s11721-009-0031-yCrossrefGoogle Scholar

  • [13] Marinakis Y., Marinaki M., A hybrid multi-swarm particle swarm optimization algorithm for the probabilistic traveling salesman problem, COMPUT OPER RES, 2010, 37, 432–442 http://dx.doi.org/10.1016/j.cor.2009.03.004CrossrefGoogle Scholar

  • [14] Gutjahr W.J., S-ACO: An Ant-Based Approach to Combinatorial Optimization under Uncertainty, In: Dorigo M., Birattari M., Blum C., Gambardella L.M., Mondada F., Stützle T., Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004 (2004, Berlin Heidelberg New York), Springer LNCS, 2004, 238–249 Google Scholar

  • [15] Vitanov I.V., Vitanov V.I., Harrison D.K., Buffer capacity allocation using ant colony optimisation algorithm, In: Rossetti M.D., Hill R.R., Johansson B., Dunkin A., Ingalls R.G., Proceedings of the 2009 Winter Simulation Conference (2009, Austin, TX), WSC, 2009, 3158–3168 Google Scholar

  • [16] Brailsford S.C., Gutjahr W.J., Rauner M., Zeppelzauer W., Combined discrete-event simulation and ant colony optimisation approach for selecting optimal screening policies for diabetic retinopathy, COMPUTATIONAL MANAGEMENT SCIENCE, 2007, 4, 59–83 http://dx.doi.org/10.1007/s10287-006-0008-xCrossrefGoogle Scholar

  • [17] Brailsford S.C., Tutorial: Advances and challenges in healthcare simulation modeling, In: Henderson S.G., Biller B., Hsieh M.-H., Shortle J., Tew J.D., Barton R.R. (Eds.), Proceedings of the 2007 Winter Simulation Conference (2007, Washington, DC), WSC, 2007, 1436–1448 Google Scholar

  • [18] Birattari M., Balaprakash P., Dorigo M., The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty, In: Doerner K.F., Gendreau M., Greistorfer P., Gutjahr W., Hartl R.F., Reimann M., Metaheuristics, Operations Research/Computer Science Interfaces vol. 39, Springer US, New York, 2007 Google Scholar

  • [19] Escobar A.H., Romero R.A., Gallego R.A., Transmission network expansion planing considering uncertainty in generation and demand, Proc. Transmission and Distribution Conference and Exposition: Latin America, 2008 IEEE/PES (2008, Bogota, Colombia), IEEE/PES, 2008, 1–6 Google Scholar

  • [20] Gutjahr W.J., Stochastic search in metaheuristics, In: Gendreau M., Potvin Y., Handbook of Metaheuristics, 2nd Edition, International Series in Operations Research and Management Science vol. 146, Springer, New York, 2010 Google Scholar

  • [21] Chang H.S., On convergence of evolutionary computation for stochastic combinatorial optimization, The Institute for Systems Research, James Clark School of Engineering, Univ. of Maryland, 2009, 16 Google Scholar

  • [22] Hannah L., Powell W., Evolutionary Policy Iteration Under a Sampling Regime for Stochastic Combinatorial Optimization, IEEE T AUTOMAT CONTR, 2010, 55, 1254–1257 http://dx.doi.org/10.1109/TAC.2010.2042766CrossrefGoogle Scholar

  • [23] Prudius A.A., Andradottir S., Two simulated annealing algorithms for noisy objective functions, In: Kuhl M.E., Steiger N.M., Armstrong F.B., Joines J.A. (Eds.), Proc. Winter Simulation Conference 2005 (2005, Orlando, FL), WSC, 2005, 797–802 Google Scholar

  • [24] Prudius A.A., Adaptive random search methods for simulation optimization, PhD thesis, Georgia Institute of Technology, USA, 2007 Google Scholar

  • [25] Branke J., Meisel S., Schmidt C., Simulated annealing in the presence of noise, J HEURISTICS, 2008, 14, 627–654 http://dx.doi.org/10.1007/s10732-007-9058-7CrossrefGoogle Scholar

  • [26] Fink T.M.A., Inverse protein folding, hierarchical optimisation and tie knots, PhD thesis, University of Cambridge, 1998 Google Scholar

  • [27] Maniezzo V., Stützle T., Voss S., Matheuristics: Hybridizing Metaheuristics and Mathematical Programming Springer, New York Dordrecht Heidelberg London, 2009 Google Scholar

  • [28] Till J., Sand G., Urselmann M., Engell S., A hybrid evolutionary algorithm for solving two-stage stochastic integer programs in chemical batch scheduling, COMPUT CHEM ENG, 2007, 31, 630–647 http://dx.doi.org/10.1016/j.compchemeng.2006.09.003CrossrefGoogle Scholar

  • [29] Tometzki T., Engell S., Hybrid evolutionary optimizatio of two-stage stochastic integer programming problems: an empirical investigation, EVOL COMPUT, 2009, 17, 511–526 http://dx.doi.org/10.1162/evco.2009.17.4.17404CrossrefGoogle Scholar

  • [30] Hvattum L.M., Lokketangen A., Using scenario trees and progressive hedging for stochastic inventory routing problems, J HEURISTICS, 2009, 15, 527–557 http://dx.doi.org/10.1007/s10732-008-9076-0CrossrefGoogle Scholar

  • [31] Crainic T.G., Fu X., Gendreau M., Rei W., Wallace S.W., Progressive hedging-based meta-heuristics for stochastic network design, CIRRELT Montreal, January 2009, 03 Google Scholar

  • [32] Rei W., Gendreau M., Soriano P., A hybrid Monte Carlo branching algorithm for the single vehicle routing problem with stochastic demands, TRANSPORT SCI, 2010, 44, 136–146 http://dx.doi.org/10.1287/trsc.1090.0295CrossrefGoogle Scholar

  • [33] Fischetti M., Lodi A., Local branching, MATH PROGRAM B, 2003, 98, 23–47 http://dx.doi.org/10.1007/s10107-003-0395-5CrossrefGoogle Scholar

  • [34] Hannah L., Powell W., Stewart J., One-stage R & D portfolio optimization with an application to solid oxide fuel cells, ENERGY SYSTEMS, 2010, 1, 141–163 http://dx.doi.org/10.1007/s12667-009-0008-3CrossrefGoogle Scholar

  • [35] Norkin V.I., Ermoliev Y.M., Ruszczynski A., On optimal allocation of indivisibles under uncertainty, OPER RES, 1998, 46, 381–395 http://dx.doi.org/10.1287/opre.46.3.381CrossrefGoogle Scholar

  • [36] Norkin V.I., Pflug G.Ch., Ruszczynski A., A branch and bound method for stochastic global optimization, MATH PROGRAM, 1998, 83, 425–450 CrossrefGoogle Scholar

  • [37] Caballero R., Cerdá E., del Mar Munoz M., Rey L., Stochastic approach versus multiobjective approach for obtaining efficient solutions in stochastic multiobjective programming problems, EUR J OPER RES, 2004, 158, 633–648 http://dx.doi.org/10.1016/S0377-2217(03)00371-0CrossrefGoogle Scholar

  • [38] Claro J., de Sousa J., A multiobjective metaheuristic for a mean-risk multistage capacity investment problem, J HEURISTICS, 2010, 16, 85–115 http://dx.doi.org/10.1007/s10732-008-9090-2CrossrefGoogle Scholar

  • [39] Claro J., de Sousa J., A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem, COMPUT OPTIM APPL, 2010, 46, 427–450 http://dx.doi.org/10.1007/s10589-008-9197-2CrossrefGoogle Scholar

  • [40] Hughes E.J., Evolutionary multi-objective ranking with uncertainty and noise, In: Zitzler E., Deb K., Thiele L., Coello Coello C.A., Corne D. (Eds.), Proc. EMO’ 01 (Evolutionary Multicriterion Optimization) (2001, Berlin), Springer, 2001, 329–343 Google Scholar

  • [41] Teich J., Pareto-front exploration with uncertain objectives, In: Zitzler E., Deb K., Thiele L., Coello Coello C.A., Corne D. (Eds.), Proc. EMO’ 01 (Evolutionary Multicriterion Optimization) (2001, Berlin), Springer, 2001, 314–328 Google Scholar

  • [42] Eskandari H., Rabelo L., Mollaghasemi M., Multiobjective simulation optimization using an enhanced genetic algorithm, In: Kuhl M.E., Steiger N.M., Armstrong F.B., Joines J.A., Proceedings of the 37th conference on Winter simulation, WSC’ 05 (Winter Simulation Conference) (2005, Orlando, Florida), WSC, 2005, 833–841 Google Scholar

  • [43] Ding H., Benyucef L., Xie X., A simulation-based multi-objective genetic algorithm approach for networked enterprises optimization, ENG APPL ARTIF INTEL, 2006, 19, 609–623 http://dx.doi.org/10.1016/j.engappai.2005.12.008CrossrefGoogle Scholar

  • [44] Amodeo L., Prins C., Sanchez D., Comparison of Metaheuristic Approaches for Multi-objective Simulation-Based Optimization in Supply Chain Inventory Management, In: Giacobini M., Brabazon A., Cagnoni S., Caro G.A., Ekart A., Esparcia-Alcázar A.I., Farooq M., Fink A., Machado P., McCormack J., O’Neill M., Neri F., Preuß M., Rothlauf F., Tarantino E., Yang S. (Eds.), Applications of Evolutionary Computing, Lecture Notes in Computer Science vol. 5484, Springer, Berlin, Heidelberg, 2009 http://dx.doi.org/10.1007/978-3-642-01129-0_90CrossrefGoogle Scholar

  • [45] Eskandari H., Geiger Ch., Evolutionary multiobjective optimization in noisy problem environments, J HEURISTICS, 2009, 15, 559–595 http://dx.doi.org/10.1007/s10732-008-9077-zCrossrefGoogle Scholar

  • [46] Syberfeldt A., Ng A., John R.I., Moore Ph., Multi-objective evolutionary simulation-optimisation of a real-world manufacturing problem, ROBOT CIM-INT MANUF, 2009, 25, 926–931 http://dx.doi.org/10.1016/j.rcim.2009.04.013CrossrefGoogle Scholar

  • [47] Gutjahr W.J., Two metaheuristics for multiobjective stochastic combinatorial optimization, In: Lupanov O.B., Kasim-Zade O.M., Chaskin A.V., Steinhoefl K. (Eds.), Proc. SAGA 2005 (Stochastic Algorithms: Foundations and Applications) (2005, Berlin Heidelberg), Springer, 2005, 116–125 Google Scholar

  • [48] Gutjahr W.J., A provably convergent heuristic for stochastic bicriteria integer programming, J HEURISTICS, 2009, 15, 227–258 http://dx.doi.org/10.1007/s10732-008-9071-5CrossrefGoogle Scholar

  • [49] Gutjahr W.J., Reiter P., Bi-objective project portfolio selection and staff assignment under uncertainty, OPTIMIZATION, 2010, 59, 417–445 http://dx.doi.org/10.1080/02331931003700699CrossrefGoogle Scholar

  • [50] Gutjahr W.J., Runtime Analysis of an Evolutionary Algorithm for Stochastic Multi-Objective Combinatorial Optimization, Dept. of Statistics and Decision Support Systems, University of Vienna, 2010 Google Scholar

  • [51] Basseur M., Zitzler E., Handling uncertainty in indicator-based multiobjective optimization, INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE RESEARCH, 2006, 2, 255–272 http://dx.doi.org/10.5019/j.ijcir.2006.66CrossrefGoogle Scholar

  • [52] Liefooghe A., Basseur M., Jourdan L., Talbi E.-G., Combinatorial Optimization of Stochastic Multi-objective Problems: An Application to the Flow-Shop Scheduling Problem, In: Obayashi S., Deb K., Poloni C., Hiroyasu T., Murata T. (Eds.), Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science vol. 4403, Springer, Berlin, Heidelberg, 2007 Google Scholar

  • [53] Liefooghe A., Basseur M., Jourdan L., Talbi E.-G., ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization, In: Obayashi S., Deb K., Poloni C., Hiroyasu T., Murata T. (Eds.), Evolutionary Multi-Criterion Optimization, Lecture Notes in Computer Science vol. 4403, Springer, Berlin, Heidelberg, 2007 Google Scholar

About the article

Published Online: 2011-03-25

Published in Print: 2011-03-01


Citation Information: Open Computer Science, ISSN (Online) 2299-1093, DOI: https://doi.org/10.2478/s13537-011-0003-3.

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