Hybrid Metaheuristics for Multi-objective combinatorial optimization

Management Science

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Published

Standard

Hybrid Metaheuristics for Multi-objective combinatorial optimization. / Ehrgott, Matthias; Gandibleux, Xavier.
Hybrid Metaheuristics: An Emerging Approach to Optimization. ed. / Christian Blum; Maria José Blesa Aguilera ; Andrea Roli; Michael Sampels. Berlin: Springer, 2008. p. 221-259 (Studies in Computational Intelligence; Vol. 114).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter

Harvard

Ehrgott, M & Gandibleux, X 2008, Hybrid Metaheuristics for Multi-objective combinatorial optimization. in C Blum, MJ Blesa Aguilera , A Roli & M Sampels (eds), Hybrid Metaheuristics: An Emerging Approach to Optimization. Studies in Computational Intelligence, vol. 114, Springer, Berlin, pp. 221-259.

APA

Ehrgott, M., & Gandibleux, X. (2008). Hybrid Metaheuristics for Multi-objective combinatorial optimization. In C. Blum, M. J. Blesa Aguilera , A. Roli, & M. Sampels (Eds.), Hybrid Metaheuristics: An Emerging Approach to Optimization (pp. 221-259). (Studies in Computational Intelligence; Vol. 114). Springer.

Vancouver

Ehrgott M, Gandibleux X. Hybrid Metaheuristics for Multi-objective combinatorial optimization. In Blum C, Blesa Aguilera MJ, Roli A, Sampels M, editors, Hybrid Metaheuristics: An Emerging Approach to Optimization. Berlin: Springer. 2008. p. 221-259. (Studies in Computational Intelligence).

Author

Ehrgott, Matthias ; Gandibleux, Xavier. / Hybrid Metaheuristics for Multi-objective combinatorial optimization. Hybrid Metaheuristics: An Emerging Approach to Optimization. editor / Christian Blum ; Maria José Blesa Aguilera ; Andrea Roli ; Michael Sampels. Berlin : Springer, 2008. pp. 221-259 (Studies in Computational Intelligence).

Bibtex

@inbook{ff07133f4b4e44bcb7b9a2fb01c3461e,

title = "Hybrid Metaheuristics for Multi-objective combinatorial optimization",

abstract = "Many real-world optimization problems can be modelled as combinatorial optimization problems. Often, these problems are characterized by their large size and the presence of multiple, conflicting objectives. Despite progress in solving multi-objective combinatorial optimization problems exactly, the large size often means that heuristics are required for their solution in acceptable time. Since the middle of the nineties the trend is towards heuristics that “pick and choose” elements from several of the established metaheuristic schemes. Such hybrid approximation techniques may even combine exact and heuristic approaches. In this chapter we give an overview over approximation methods in multi-objective combinatorial optimization. We briefly summarize “classical” metaheuristics and focus on recent approaches, where metaheuristics are hybridized and/or combined with exact methods.",

author = "Matthias Ehrgott and Xavier Gandibleux",

year = "2008",

language = "English",

isbn = "978-3-540-78294-0",

series = "Studies in Computational Intelligence",

publisher = "Springer",

pages = "221--259",

editor = "Christian Blum and {Blesa Aguilera }, {Maria Jos{\'e}} and Andrea Roli and Michael Sampels",

booktitle = "Hybrid Metaheuristics",

}

RIS

TY - CHAP

T1 - Hybrid Metaheuristics for Multi-objective combinatorial optimization

AU - Ehrgott, Matthias

AU - Gandibleux, Xavier

PY - 2008

Y1 - 2008

N2 - Many real-world optimization problems can be modelled as combinatorial optimization problems. Often, these problems are characterized by their large size and the presence of multiple, conflicting objectives. Despite progress in solving multi-objective combinatorial optimization problems exactly, the large size often means that heuristics are required for their solution in acceptable time. Since the middle of the nineties the trend is towards heuristics that “pick and choose” elements from several of the established metaheuristic schemes. Such hybrid approximation techniques may even combine exact and heuristic approaches. In this chapter we give an overview over approximation methods in multi-objective combinatorial optimization. We briefly summarize “classical” metaheuristics and focus on recent approaches, where metaheuristics are hybridized and/or combined with exact methods.

AB - Many real-world optimization problems can be modelled as combinatorial optimization problems. Often, these problems are characterized by their large size and the presence of multiple, conflicting objectives. Despite progress in solving multi-objective combinatorial optimization problems exactly, the large size often means that heuristics are required for their solution in acceptable time. Since the middle of the nineties the trend is towards heuristics that “pick and choose” elements from several of the established metaheuristic schemes. Such hybrid approximation techniques may even combine exact and heuristic approaches. In this chapter we give an overview over approximation methods in multi-objective combinatorial optimization. We briefly summarize “classical” metaheuristics and focus on recent approaches, where metaheuristics are hybridized and/or combined with exact methods.

M3 - Chapter

SN - 978-3-540-78294-0

T3 - Studies in Computational Intelligence

SP - 221

EP - 259

BT - Hybrid Metaheuristics

A2 - Blum, Christian

A2 - Blesa Aguilera , Maria José

A2 - Roli, Andrea

A2 - Sampels, Michael

PB - Springer

CY - Berlin

ER -

Research