Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization
Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/16/11/504 |
| _version_ | 1797460504028905472 |
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| author | Adekunle Rotimi Adekoya Mardé Helbig |
| author_facet | Adekunle Rotimi Adekoya Mardé Helbig |
| author_sort | Adekunle Rotimi Adekoya |
| collection | DOAJ |
| description | Dynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM. |
| first_indexed | 2024-03-09T17:05:59Z |
| format | Article |
| id | doaj.art-f5af94f916be405ead4127b1eef4b10b |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T17:05:59Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-f5af94f916be405ead4127b1eef4b10b2023-11-24T14:24:21ZengMDPI AGAlgorithms1999-48932023-10-01161150410.3390/a16110504Decision-Maker’s Preference-Driven Dynamic Multi-Objective OptimizationAdekunle Rotimi Adekoya0Mardé Helbig1Computer Science Division, Stellenbosch University, Stellenbosch 7600, South AfricaDepartment of Computer Science, University of Pretoria, Hatfield 0002, South AfricaDynamic multi-objective optimization problems (DMOPs) are optimization problems where elements of the problems, such as the objective functions and/or constraints, change with time. These problems are characterized by two or more objective functions, where at least two objective functions are in conflict with one another. When solving real-world problems, the incorporation of human decision-makers (DMs)’ preferences or expert knowledge into the optimization process and thereby restricting the search to a specific region of the Pareto-optimal Front (POF) may result in more preferred or suitable solutions. This study proposes approaches that enable DMs to influence the search process with their preferences by reformulating the optimization problems as constrained problems. The subsequent constrained problems are solved using various constraint handling approaches, such as the penalization of infeasible solutions and the restriction of the search to the feasible region of the search space. The proposed constraint handling approaches are compared by incorporating the approaches into a differential evolution (DE) algorithm and measuring the algorithm’s performance using both standard performance measures for dynamic multi-objective optimization (DMOO), as well as newly proposed measures for constrained DMOPs. The new measures indicate how well an algorithm was able to find solutions in the objective space that best reflect the DM’s preferences and the Pareto-optimality goal of dynamic multi-objective optimization algorithms (DMOAs). The results indicate that the constraint handling approaches are effective in finding Pareto-optimal solutions that satisfy the preference constraints of a DM.https://www.mdpi.com/1999-4893/16/11/504dynamic multi-objective optimizationconstrained optimizationdecision-maker preferencedifferential evolutionperformance measures |
| spellingShingle | Adekunle Rotimi Adekoya Mardé Helbig Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization Algorithms dynamic multi-objective optimization constrained optimization decision-maker preference differential evolution performance measures |
| title | Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization |
| title_full | Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization |
| title_fullStr | Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization |
| title_full_unstemmed | Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization |
| title_short | Decision-Maker’s Preference-Driven Dynamic Multi-Objective Optimization |
| title_sort | decision maker s preference driven dynamic multi objective optimization |
| topic | dynamic multi-objective optimization constrained optimization decision-maker preference differential evolution performance measures |
| url | https://www.mdpi.com/1999-4893/16/11/504 |
| work_keys_str_mv | AT adekunlerotimiadekoya decisionmakerspreferencedrivendynamicmultiobjectiveoptimization AT mardehelbig decisionmakerspreferencedrivendynamicmultiobjectiveoptimization |