A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization
Most of the engineering problems are modeled as evolutionary multiobjective optimization problems, but they always ask for only one best solution, not a set of Pareto optimal solutions. The decision maker's subjective information plays an important role in choosing the best solution from severa...
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2011/695087 |
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doaj-c75f70f2862546a585758697109805cf2020-11-24T22:58:33ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472011-01-01201110.1155/2011/695087695087A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective OptimizationShuang Wei0Henry Leung1Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB, T2N 1N4, CanadaDepartment of Electrical and Computer Engineering, University of Calgary, Calgary, AB, T2N 1N4, CanadaMost of the engineering problems are modeled as evolutionary multiobjective optimization problems, but they always ask for only one best solution, not a set of Pareto optimal solutions. The decision maker's subjective information plays an important role in choosing the best solution from several Pareto optimal solutions. Generally, the decision-making processing is implemented after Pareto optimality. In this paper, we attempted to incorporate the decider's subjective sense with Pareto optimality for chromosomes ranking. A new ranking method based on subjective probability theory was thus proposed in order to explore and comprehend the true nature of the chromosomes on the Pareto optimal front. The properties of the ranking rule were proven, and its transitivity was presented as well. Simulation results compared the performance of the proposed ranking approach with the Pareto-based ranking method for two multiobjective optimization cases, which demonstrated the effectiveness of the new ranking approach.http://dx.doi.org/10.1155/2011/695087 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shuang Wei Henry Leung |
spellingShingle |
Shuang Wei Henry Leung A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization Mathematical Problems in Engineering |
author_facet |
Shuang Wei Henry Leung |
author_sort |
Shuang Wei |
title |
A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization |
title_short |
A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization |
title_full |
A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization |
title_fullStr |
A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization |
title_full_unstemmed |
A Novel Ranking Method Based on Subjective Probability Theory for Evolutionary Multiobjective Optimization |
title_sort |
novel ranking method based on subjective probability theory for evolutionary multiobjective optimization |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2011-01-01 |
description |
Most of the engineering problems are modeled as evolutionary multiobjective optimization problems, but they always ask for only one best solution, not a set of Pareto optimal solutions. The decision maker's subjective information plays an important role in choosing the best solution from several Pareto optimal solutions. Generally, the decision-making processing is implemented after Pareto optimality. In this paper, we attempted to incorporate the decider's subjective sense with Pareto optimality for chromosomes ranking. A new ranking method based on subjective probability theory was thus proposed in order to explore and comprehend the true nature of the chromosomes on the Pareto optimal front. The properties of the ranking rule were proven, and its transitivity was presented as well. Simulation results compared the performance of the proposed ranking approach with the Pareto-based ranking method for two multiobjective optimization cases, which demonstrated the effectiveness of the new ranking approach. |
url |
http://dx.doi.org/10.1155/2011/695087 |
work_keys_str_mv |
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