OptLets: A Generic Framework for Solving Arbitrary Optimization Problems

Christoph Breitschopf; Günther Blaschek; Thomas Scheidl

OptLets: A Generic Framework for Solving Arbitrary Optimization Problems

Christoph Breitschopf, Günther Blaschek, Thomas Scheidl

Institute of Business Informatics - Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Meta-heuristics are an effective paradigm for solving large-scale combinatorial optimization problems. However, the development of such algorithms is often very time-consuming as they have to be designed for a concrete problem class with little or no opportunity for reuse. In this paper, we present a generic software framework that is able to handle different types of combinatorial optimization problems by coordinating so-called OptLets that work on a set of solutions to a problem. The framework provides a high degree of self-organization and offers a generic and concise interface to reduce the adaptation effort for new problems as well as to integrate with external systems. The performance of the OptLets framework is demonstrated by solving the well-known Traveling Salesman Problem.

Original language	English
Title of host publication	WSEAS Transactions on Information Science and Applications (Special Issue: Selected papers from the 6th WSEAS Int. Conference on Evolutionary Computing, Lisbon, Portugal, June 16-18, 2005)
Volume	2
Publication status	Published - Jun 2005

Fields of science

102 Computer Sciences

Cite this

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title = "OptLets: A Generic Framework for Solving Arbitrary Optimization Problems",

abstract = "Meta-heuristics are an effective paradigm for solving large-scale combinatorial optimization problems. However, the development of such algorithms is often very time-consuming as they have to be designed for a concrete problem class with little or no opportunity for reuse. In this paper, we present a generic software framework that is able to handle different types of combinatorial optimization problems by coordinating so-called OptLets that work on a set of solutions to a problem. The framework provides a high degree of self-organization and offers a generic and concise interface to reduce the adaptation effort for new problems as well as to integrate with external systems. The performance of the OptLets framework is demonstrated by solving the well-known Traveling Salesman Problem.",

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year = "2005",

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OptLets: A Generic Framework for Solving Arbitrary Optimization Problems. / Breitschopf, Christoph; Blaschek, Günther; Scheidl, Thomas.
WSEAS Transactions on Information Science and Applications (Special Issue: Selected papers from the 6th WSEAS Int. Conference on Evolutionary Computing, Lisbon, Portugal, June 16-18, 2005). Vol. 2 2005.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

TY - GEN

T1 - OptLets: A Generic Framework for Solving Arbitrary Optimization Problems

AU - Breitschopf, Christoph

AU - Blaschek, Günther

AU - Scheidl, Thomas

PY - 2005/6

Y1 - 2005/6

N2 - Meta-heuristics are an effective paradigm for solving large-scale combinatorial optimization problems. However, the development of such algorithms is often very time-consuming as they have to be designed for a concrete problem class with little or no opportunity for reuse. In this paper, we present a generic software framework that is able to handle different types of combinatorial optimization problems by coordinating so-called OptLets that work on a set of solutions to a problem. The framework provides a high degree of self-organization and offers a generic and concise interface to reduce the adaptation effort for new problems as well as to integrate with external systems. The performance of the OptLets framework is demonstrated by solving the well-known Traveling Salesman Problem.

AB - Meta-heuristics are an effective paradigm for solving large-scale combinatorial optimization problems. However, the development of such algorithms is often very time-consuming as they have to be designed for a concrete problem class with little or no opportunity for reuse. In this paper, we present a generic software framework that is able to handle different types of combinatorial optimization problems by coordinating so-called OptLets that work on a set of solutions to a problem. The framework provides a high degree of self-organization and offers a generic and concise interface to reduce the adaptation effort for new problems as well as to integrate with external systems. The performance of the OptLets framework is demonstrated by solving the well-known Traveling Salesman Problem.

M3 - Conference proceedings

VL - 2

BT - WSEAS Transactions on Information Science and Applications (Special Issue: Selected papers from the 6th WSEAS Int. Conference on Evolutionary Computing, Lisbon, Portugal, June 16-18, 2005)

ER -