A modified indicator-based evolutionary algorithm (mIBEA)
Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off soluti...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
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2017
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| Online Access: | https://eprints.nottingham.ac.uk/41420/ |
| _version_ | 1848796269216006144 |
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| author | Li, Wenwen Özcan, Ender John, Robert Drake, John H. Neumann, Aneta Wagner, Markus |
| author_facet | Li, Wenwen Özcan, Ender John, Robert Drake, John H. Neumann, Aneta Wagner, Markus |
| author_sort | Li, Wenwen |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off solutions. One commonly used approach of this nature is the indicator-based evolutionary algorithm (IBEA). In this study, we highlight the solution distribution issues within IBEA and propose a modification of the original approach by embedding an additional Pareto-dominance based component for selection. The improved performance of the proposed modified IBEA (mIBEA) is empirically demonstrated on the well-known DTLZ set of benchmark functions. Our results show that mIBEA achieves comparable or better hypervolume indicator values and epsilon approximation values in the vast majority of our cases (13 out of 14 under the same default settings) on DTLZ1-7. The modification also results in an over 8-fold speed-up for larger populations. |
| first_indexed | 2025-11-14T19:45:18Z |
| format | Conference or Workshop Item |
| id | nottingham-41420 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:45:18Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-414202020-05-04T18:48:53Z https://eprints.nottingham.ac.uk/41420/ A modified indicator-based evolutionary algorithm (mIBEA) Li, Wenwen Özcan, Ender John, Robert Drake, John H. Neumann, Aneta Wagner, Markus Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the search process towards a good set of trade-off solutions. One commonly used approach of this nature is the indicator-based evolutionary algorithm (IBEA). In this study, we highlight the solution distribution issues within IBEA and propose a modification of the original approach by embedding an additional Pareto-dominance based component for selection. The improved performance of the proposed modified IBEA (mIBEA) is empirically demonstrated on the well-known DTLZ set of benchmark functions. Our results show that mIBEA achieves comparable or better hypervolume indicator values and epsilon approximation values in the vast majority of our cases (13 out of 14 under the same default settings) on DTLZ1-7. The modification also results in an over 8-fold speed-up for larger populations. 2017-06-05 Conference or Workshop Item PeerReviewed Li, Wenwen, Özcan, Ender, John, Robert, Drake, John H., Neumann, Aneta and Wagner, Markus (2017) A modified indicator-based evolutionary algorithm (mIBEA). In: IEEE Congress on Evolutionary Computation 2017, 5-9 June 2017, Donostia-San Sebastian, Spain. Sociology Statistics Evolutionary computation Electronic mail Optimization Benchmark testing Computer science http://ieeexplore.ieee.org/abstract/document/7969423/ |
| spellingShingle | Sociology Statistics Evolutionary computation Electronic mail Optimization Benchmark testing Computer science Li, Wenwen Özcan, Ender John, Robert Drake, John H. Neumann, Aneta Wagner, Markus A modified indicator-based evolutionary algorithm (mIBEA) |
| title | A modified indicator-based evolutionary algorithm (mIBEA) |
| title_full | A modified indicator-based evolutionary algorithm (mIBEA) |
| title_fullStr | A modified indicator-based evolutionary algorithm (mIBEA) |
| title_full_unstemmed | A modified indicator-based evolutionary algorithm (mIBEA) |
| title_short | A modified indicator-based evolutionary algorithm (mIBEA) |
| title_sort | modified indicator-based evolutionary algorithm (mibea) |
| topic | Sociology Statistics Evolutionary computation Electronic mail Optimization Benchmark testing Computer science |
| url | https://eprints.nottingham.ac.uk/41420/ https://eprints.nottingham.ac.uk/41420/ |