A General Framework for Constructive Meta-heuristics

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

2 Downloads (Pure)

Abstract

Meta-heuristic search algorithms, by their very nature, are applicable across a range of optimisation problems. In practice however, meta-heuristics have been tailored to solve particular problems. Recent work by Randall and Abramson (2001b)has successfully shown that iterative meta-heuristics, such as simulated annealing and tabu search, can be successfully generalised to solve a range of problems without modification though the use of a uniform representation language. Constructive meta-heuristics, such as ant colony optimisation and generalised random adaptive search procedures, pose more substantial problems to achieve this same level of generalistaion. This paper investigates the issues involved and suggests some measures by which generalisation could be achieved.
Original languageEnglish
Title of host publicationOperations Research/Management Science at Work
EditorsErhan Kozan, Azuma Ohuchi
Place of PublicationBoston
PublisherKLUWER ACADEMIC PUBL
Pages111-128
Number of pages18
ISBN (Electronic)978-1-4615-0819-9
ISBN (Print)978-1-4613-5254-9
DOIs
Publication statusPublished - 2002

Fingerprint

Metaheuristics
Optimization problem
Simulated annealing
Language
Heuristic search
Ant colony optimization
Tabu search

Cite this

Randall, M. (2002). A General Framework for Constructive Meta-heuristics. In E. Kozan, & A. Ohuchi (Eds.), Operations Research/Management Science at Work (pp. 111-128). Boston: KLUWER ACADEMIC PUBL. https://doi.org/10.1007%2F978-1-4615-0819-9_7
Randall, Marcus. / A General Framework for Constructive Meta-heuristics. Operations Research/Management Science at Work. editor / Erhan Kozan ; Azuma Ohuchi. Boston : KLUWER ACADEMIC PUBL, 2002. pp. 111-128
@inbook{8fb130217b934e68909db8067b4bba0f,
title = "A General Framework for Constructive Meta-heuristics",
abstract = "Meta-heuristic search algorithms, by their very nature, are applicable across a range of optimisation problems. In practice however, meta-heuristics have been tailored to solve particular problems. Recent work by Randall and Abramson (2001b)has successfully shown that iterative meta-heuristics, such as simulated annealing and tabu search, can be successfully generalised to solve a range of problems without modification though the use of a uniform representation language. Constructive meta-heuristics, such as ant colony optimisation and generalised random adaptive search procedures, pose more substantial problems to achieve this same level of generalistaion. This paper investigates the issues involved and suggests some measures by which generalisation could be achieved.",
author = "Marcus Randall",
year = "2002",
doi = "10.1007{\%}2F978-1-4615-0819-9_7",
language = "English",
isbn = "978-1-4613-5254-9",
pages = "111--128",
editor = "Erhan Kozan and Azuma Ohuchi",
booktitle = "Operations Research/Management Science at Work",
publisher = "KLUWER ACADEMIC PUBL",

}

Randall, M 2002, A General Framework for Constructive Meta-heuristics. in E Kozan & A Ohuchi (eds), Operations Research/Management Science at Work. KLUWER ACADEMIC PUBL, Boston, pp. 111-128. https://doi.org/10.1007%2F978-1-4615-0819-9_7

A General Framework for Constructive Meta-heuristics. / Randall, Marcus.

Operations Research/Management Science at Work. ed. / Erhan Kozan; Azuma Ohuchi. Boston : KLUWER ACADEMIC PUBL, 2002. p. 111-128.

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

TY - CHAP

T1 - A General Framework for Constructive Meta-heuristics

AU - Randall, Marcus

PY - 2002

Y1 - 2002

N2 - Meta-heuristic search algorithms, by their very nature, are applicable across a range of optimisation problems. In practice however, meta-heuristics have been tailored to solve particular problems. Recent work by Randall and Abramson (2001b)has successfully shown that iterative meta-heuristics, such as simulated annealing and tabu search, can be successfully generalised to solve a range of problems without modification though the use of a uniform representation language. Constructive meta-heuristics, such as ant colony optimisation and generalised random adaptive search procedures, pose more substantial problems to achieve this same level of generalistaion. This paper investigates the issues involved and suggests some measures by which generalisation could be achieved.

AB - Meta-heuristic search algorithms, by their very nature, are applicable across a range of optimisation problems. In practice however, meta-heuristics have been tailored to solve particular problems. Recent work by Randall and Abramson (2001b)has successfully shown that iterative meta-heuristics, such as simulated annealing and tabu search, can be successfully generalised to solve a range of problems without modification though the use of a uniform representation language. Constructive meta-heuristics, such as ant colony optimisation and generalised random adaptive search procedures, pose more substantial problems to achieve this same level of generalistaion. This paper investigates the issues involved and suggests some measures by which generalisation could be achieved.

U2 - 10.1007%2F978-1-4615-0819-9_7

DO - 10.1007%2F978-1-4615-0819-9_7

M3 - Chapter

SN - 978-1-4613-5254-9

SP - 111

EP - 128

BT - Operations Research/Management Science at Work

A2 - Kozan, Erhan

A2 - Ohuchi, Azuma

PB - KLUWER ACADEMIC PUBL

CY - Boston

ER -

Randall M. A General Framework for Constructive Meta-heuristics. In Kozan E, Ohuchi A, editors, Operations Research/Management Science at Work. Boston: KLUWER ACADEMIC PUBL. 2002. p. 111-128 https://doi.org/10.1007%2F978-1-4615-0819-9_7