The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization

This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top of the non-optimizing relational constraint solver Kodkod. We compare the perf...

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Main Authors: Jackson, Daniel, Estler, H.-Christian, Rayside, Derek
Other Authors: Daniel Jackson
Published: 2009
Online Access:http://hdl.handle.net/1721.1/46322
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author Jackson, Daniel
Estler, H.-Christian
Rayside, Derek
author2 Daniel Jackson
author_facet Daniel Jackson
Jackson, Daniel
Estler, H.-Christian
Rayside, Derek
author_sort Jackson, Daniel
collection MIT
description This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top of the non-optimizing relational constraint solver Kodkod. We compare the performance of this new algorithm against two algorithms from the literature [Gavanelli 2002, Lukasiewycz et alia 2007, Laumanns et alia 2006]) on three micro-benchmark problems (n-Queens, n-Rooks, and knapsack) and on two aerospace case studies. Results indicate that the new algorithm is better for the kinds of many-objective problems that our aerospace collaborators are interested in solving. The new algorithm returns Pareto-optimal solutions as it computes.
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spelling mit-1721.1/463222019-04-12T07:36:35Z The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization Jackson, Daniel Estler, H.-Christian Rayside, Derek Daniel Jackson Software Design This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm. The algorithm is implemented on top of the non-optimizing relational constraint solver Kodkod. We compare the performance of this new algorithm against two algorithms from the literature [Gavanelli 2002, Lukasiewycz et alia 2007, Laumanns et alia 2006]) on three micro-benchmark problems (n-Queens, n-Rooks, and knapsack) and on two aerospace case studies. Results indicate that the new algorithm is better for the kinds of many-objective problems that our aerospace collaborators are interested in solving. The new algorithm returns Pareto-optimal solutions as it computes. 2009-07-03T19:15:04Z 2009-07-03T19:15:04Z 2009-07-03 http://hdl.handle.net/1721.1/46322 MIT-CSAIL-TR-2009-033 Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported http://creativecommons.org/licenses/by-nc-nd/3.0/ 20 p. application/pdf application/postscript
spellingShingle Jackson, Daniel
Estler, H.-Christian
Rayside, Derek
The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization
title The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization
title_full The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization
title_fullStr The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization
title_full_unstemmed The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization
title_short The Guided Improvement Algorithm for Exact, General-Purpose, Many-Objective Combinatorial Optimization
title_sort guided improvement algorithm for exact general purpose many objective combinatorial optimization
url http://hdl.handle.net/1721.1/46322
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