Article Outline
Keywords and Phrases
Introduction
Definitions
Local Search
Metaheuristics
Metaheuristic Methods
Simple Local Search Based Metaheuristics
Simulated Annealing
Tabu Search
Evolutionary Algorithms
Swarm Intelligence
Miscellaneous
General Frames
Adaptive Memory Programming
A Pool Template
Partial Optimization Metaheuristic Under Special Intensification Conditions
Hybrids with Exact Methods
Optimization Software Libraries
Applications
Conclusions
References
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aarts EHL, Lenstra JK (eds) (1997) Local Search in Combinatorial Optimization. Wiley, Chichester
Aarts EHL, Verhoeven M (1997) Local search. In: Dell'Amico M, Maffioli F, Martello S (eds) Annotated Bibliographies in Combinatorial Optimization. Wiley, Chichester, pp 163–180
Achterberg T, Berthold T (2007) Improving the feasibility pump. Discret Optim 4:77–86
Adenso-Diaz B, Laguna M (2006) Fine-tuning of algorithms using fractional experimental designs and local search. Oper Res 54:99–114
Ahuja RK, Ergun O, Orlin JB, Punnen AB (2002) A survey of very large-scale neighborhood search techniques. Discret Appl Math 123:75–102
Alba E (ed) (2005) Parallel Metaheuristics. Wiley, Hoboken
Alba E, Marti R (eds) (2006) Metaheuristic Procedures for Training Neural Networks. Springer, New York
Althöfer I, Koschnick KU (1991) On the convergence of ‘threshold accepting’. Appl Math Optim 24:183–195
Bäck T, Fogel DB, Michalewicz Z (eds) (1997) Handbook of Evolutionary Computation. Institute of Physics Publishing, Bristol
Barr RS, Golden BL, Kelly JP, Resende MGC, Stewart WR (1995) Designing and reporting on computational experiments with heuristic methods. J Heuristics 1:9–32
Bastos MB, Ribeiro CC (2002) Reactive tabu search with path relinking for the Steiner problem in graphs. In: Ribeiro CC, Hansen P (eds) Essays and Surveys in Metaheuristics. Kluwer, Boston, pp 39–58
Battiti R, Tecchiolli G (1994) The reactive tabu search. ORSA J Comput 6:126–140
Bertsekas DP, Tsitsiklis JN, Wu C (1997) Rollout algorithms for combinatorial optimization. J Heuristics 3:245–262
Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: Overview conceptual comparison. ACM Comput Surv 35:268–308
Bonabeau E, Dorigo M, Theraulaz G (eds) (1999) Swarm Intelligence – From Natural to Artificial Systems. Oxford University Press, New York
Burke EK, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: An emerging direction in modern search technology. In: Glover FW, Kochenberger GA (eds) Handbook of Metaheuristics. Kluwer, Boston, pp 457–474
Caseau Y, Laburthe F, Silverstein G (1999) A meta-heuristic factory for vehicle routing problems. Lect Notes Comput Sci 1713:144–158
Cerulli R, Fink A, Gentili M, Voß S (2006) Extensions of the minimum labelling spanning tree problem. J Telecommun Inf Technol 4/2006:39–45
Charon I, Hudry O (1993) The noising method: A new method for combinatorial optimization. Oper Res Lett 14:133–137
Crainic TG, Toulouse M, Gendreau M (1997) Toward a taxonomy of parallel tabu search heuristics. INFORMS J Comput 9:61–72
de Backer B, Furnon V, Shaw P, Kilby P, Prosser P (2000) Solving vehicle routing problems using constraint programming and metaheuristics. J Heuristics 6:501–523
Di Gaspero L, Schaerf A (2003) EASYLOCAL++: An object-oriented framework for the flexible design of local-search algorithms. Softw Pr Experience 33:733–765
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst, Man Cybern B 26:29–41
Dorigo M, Stützle T (2004) Ant Colony Optimization. MIT Press, Cambridge
Dörner KF, Gendreau M, Greistorfer P, Gutjahr WJ, Hartl RF, Reimann M (eds) (2007) Metaheuristics: Progress in Complex Systems Optimization. Springer, New York
Dowsland KA (1993) Simulated annealing. In: Reeves C (ed) Modern Heuristic Techniques for Combinatorial Problems. Halsted, Blackwell, pp 20–69
Dreo J, Petrowski A, Siarry P, Taillard E (2006) Metaheuristics for Hard Optimization. Springer, Berlin
Dueck G, Scheuer T (1990) Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. J Comput Phys 90:161–175
Duin CW, Voß S (1994) Steiner tree heuristics – a survey. In: Dyckhoff H, Derigs U, Salomon M, Tijms HC (eds) Operations Research Proceedings. Springer, Berlin, pp 485–496
Duin CW, Voß S (1999) The pilot method: A strategy for heuristic repetition with application to the Steiner problem in graphs. Netw 34:181–191
Faigle U, Kern W (1992) Some convergence results for probabilistic tabu search. ORSA J Comput 4:32–37
Festa P, Resende MGC (2004) An annotated bibliography of GRASP. Technical report, AT&T Labs Research, Florham Park
Fink A, Voß S (2002) HotFrame: A heuristic optimization framework. In: Voß S, Woodruff DL (eds) Optimization Software Class Libraries. Kluwer, Boston, pp 81–154
Fischetti M, Glover F, Lodi A (2005) The feasibility pump. Math Program A 104:91–104
Fischetti M, Lodi A (2003) Local branching. Math Program B 98:23–47
Fogel DB (1993) On the philosophical differences between evolutionary algorithms and genetic algorithms. In: Fogel DB, Atmar W (eds) Proceedings of the Second Annual Conference on Evolutionary Programming, Evolutionary Programming Society, La Jolla, pp 23–29
Fogel DB (1995) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, New York
Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8:156–166
Glover F (1986) Future paths for integer programming links to artificial intelligence. Comput Oper Res 13:533–549
Glover F (1990) Tabu search – Part II. ORSA J Comput 2:4–32
Glover F (1995) Scatter search and star-paths: beyond the genetic metaphor. OR Spektrum 17:125–137
Glover F (1997) Tabu search and adaptive memory programming – Advances, applications challenges. In: Barr RS, Helgason RV, Kennington JL (eds) Interfaces in computer science and operations research: Advances in metaheuristics, optimization and stochastic modeling technologies. Kluwer, Boston, pp 1–75
Glover F, Laguna M (1997) Tabu Search. Kluwer, Boston
Glover FW, Kochenberger GA (eds) (2003) Handbook of Metaheuristics. Kluwer, Boston
Goldberg DE (1989) Genetic Algorithms in Search, Optimization, Machine Learning. Addison-Wesley, Reading
Golden BL, Raghavan S, Wasil EA (eds) (2005) The Next Wave in Computing, Optimization, Decision Technologies. Kluwer, Boston
Gomes AM, Oliveira JF (2006) Solving irregular strip packing problems by hybridising simulated annealing and linear programming. Eur J Oper Res 171:811–829
Greistorfer P, Voß S (2005) Controlled pool maintenance for meta-heuristics. In: Rego C, Alidaee B (eds) Metaheuristic optimization via memory evolution. Kluwer, Boston, pp 387–424
Gutenschwager K, Niklaus C, Voß S (2004) Dispatching of an electric monorail system: Applying meta-heuristics to an online pickup and delivery problem. Transp Sci 38:434–446
Hajek B (1988) Cooling schedules for optimal annealing. Math Oper Res 13:311–329
Hansen P, Mladenović N (1999) An introduction to variable neighborhood search. In: Voß S, Martello S, Osman IH, Roucairol C (eds) Meta-heuristics: Advances and trends in local search paradigms for optimization. Kluwer, Boston, pp 433–458
Hart JP, Shogan AW (1987) Semi-greedy heuristics: An empirical study. Oper Res Lett 6:107–114
Harvey W, Ginsberg M (1995) Limited discrepancy search. In: Proceedings of the 14th IJCAI. Morgan Kaufmann, San Mateo, pp 607–615
Hertz A, Kobler D (2000) A framework for the description of evolutionary algorithms. Eur J Oper Res 126:1–12
Hoffmeister F, Bäck T (1991) Genetic algorithms and evolution strategies: Similarities and differences. Lect Notes Comput Sci 496:455–469
Holland JH (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor
Hooker JN (1995) Testing heuristics: We have it all wrong. J Heuristics 1:33–42
Hoos HH, Stützle T (2005) Stochastic Local Search – Foundations and Applications. Elsevier, Amsterdam
Ibaraki T, Nonobe K, Yagiura M (eds) (2005) Metaheuristics: Progress as Real Problem Solvers. Springer, New York
Ingber L (1996) Adaptive simulated annealing (ASA): Lessons learned. Control Cybern 25:33–54
Jaszkiewicz A (2004) A comparative study of multiple-objective metaheuristics on the bi-objective set covering problem and the pareto memetic algorithm. Ann Oper Res 131:215–235
Johnson DS, Aragon CR, McGeoch LA, Schevon C (1989) Optimization by simulated annealing: An experimental evaluation; part i, graph partitioning. Oper Res 37:865–892
Kennedy J, Eberhart RC (2001) Swarm Intelligence. Elsevier, Amsterdam
Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680
Laguna M, Martí R (2003) Scatter Search. Kluwer, Boston
Lin S, Kernighan BW (1973) An effective heuristic algorithm for the traveling-salesman problem. Oper Res 21:498–516
McGeoch C (1996) Toward an experimental method for algorithm simulation. INFORMS J Comput 8:1–15
Meloni C, Pacciarelli D, Pranzo M (2004) A rollout metaheuristic for job shop scheduling problems. Ann Oper Res 131:215–235
Michalewicz Z (1999) Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin
Michalewicz Z, Fogel DB (2004) How to Solve It: Modern Heuristics, 2nd edn. Springer, Berlin
Moscato P (1993) An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search. Ann Oper Res 41:85–121
Osman IH, Kelly JP (eds) (1996) Meta-Heuristics: Theory and Applications. Kluwer, Boston
Pearl J (1984) Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading
Pesant G, Gendreau M (1999) A constraint programming framework for local search methods. J Heuristics 5:255–279
Pesch E, Glover F (1997) TSP ejection chains. Discret Appl Math 76:165–182
Polya G (1945) How to solve it. Princeton University Press, Princeton
Rayward-Smith VJ, Osman IH, Reeves CR, Smith GD (eds) (1996) Modern Heuristic Search Methods. Wiley, Chichester
Reeves CR, Rowe JE (2002) Genetic Algorithms: Principles and Perspectives. Kluwer, Boston
Rego C, Alidaee B (eds) (2005) Metaheuristic optimization via memory and evolution. Kluwer, Boston
Resende MGC, de Sousa JP (eds) (2004) Metaheuristics: Computer Decision-Making. Kluwer, Boston
Ribeiro CC, Hansen P (eds) (2002) Essays and Surveys in Metaheuristics. Kluwer, Boston
Sakawa M (2001) Genetic algorithms and fuzzy multiobjective optimization. Kluwer, Boston
Schwefel HP, Bäck T (1998) Artificial evolution: How and why? In: Quagliarella D, Périaux J, Poloni C, Winter G (eds) Genetic Algorithms and Evolution Strategy in Engineering and Computer Science: Recent Advances and Industrial Applications, Wiley, Chichester, pp 1–19
Shaw P (1998) Using constraint programming local search methods to solve vehicle routing problems. Working paper, ILOG SA, Gentilly
Smith K (1999) Neural networks for combinatorial optimisation: A review of more than a decade of research. INFORMS J Comput 11:15–34
Sniedovich M, Voß S (2006) The corridor method: A dynamic programming inspired metaheuristic. Control Cybern 35:551–578
Storer RH, Wu SD, Vaccari R (1995) Problem and heuristic space search strategies for job shop scheduling. ORSA J Comput 7:453–467
Taillard E, Voß S (2002) POPMUSIC - partial optimization metaheuristic under special intensification conditions. In: Ribeiro CC, Hansen P (eds) Essays and Surveys in Metaheuristics. Kluwer, Boston, pp 613–629
Taillard ÉD, Gambardella LM, Gendreau M, Potvin JY (2001) Adaptive memory programming: A unified view of meta-heuristics. Eur J Oper Res 135:1–16
Vaessens RJM, Aarts EHL, Lenstra JK (1998) A local search template. Comput Oper Res 25:969–979
Verhoeven MGA, Aarts EHL (1995) Parallel local search techniques. J Heuristics 1:43–65
Voß S (1993) Intelligent Search. Manuscript, TU Darmstadt
Voß S (1993) Tabu search: applications and prospects. In: Du DZ, Pardalos P (eds) Network Optimization Problems. World Scientific, Singapore, pp 333–353
Voß S (1996) Observing logical interdependencies in tabu search: Methods and results. In: Rayward-Smith VJ, Osman IH, Reeves CR, Smith GD (eds) Modern Heuristic Search Methods. Wiley, Chichester, pp 41–59
Voß S (2001) Meta-heuristics: The state of the art. Lect Notes Artif Intell 2148:1–23
Voß S, Fink A, Duin C (2004) Looking ahead with the pilot method. Ann Oper Res 136:285–302
Voß S, Martello S, Osman IH, Roucairol C (eds) (1999) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer, Boston
Voß S, Woodruff DL (eds) (2002) Optimization Software Class Libraries. Kluwer, Boston
Watson JP, Whitley LD, Howe AE (2005) Linking search space structure, run-time dynamics, and problem difficulty: A step toward demystifying tabu search. J Artif Intell Res 24:221–261
Whitley D, Rana S, Dzubera J, Mathias KE (1996) Evaluating evolutionary algorithms. Artif Intell 85:245–276
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82
Woodruff DL (1998) Proposals for chunking and tabu search. Eur J Oper Res 106:585–598
Woodruff DL (1999) A chunking based selection strategy for integrating meta-heuristics with branch and bound. In: Voß S, Martello S, Osman IH, Roucairol C (eds) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer, Boston, pp 499–511
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag
About this entry
Cite this entry
Voß, S. (2008). Metaheuristics . In: Floudas, C., Pardalos, P. (eds) Encyclopedia of Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74759-0_367
Download citation
DOI: https://doi.org/10.1007/978-0-387-74759-0_367
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74758-3
Online ISBN: 978-0-387-74759-0
eBook Packages: Mathematics and StatisticsReference Module Computer Science and Engineering