A targeted reverse mapping machine learning approach for non-dominated solutions in multi-objective optimization
| dc.contributor.advisor | Ibrahimi, Mehran | |
| dc.contributor.advisor | Rahnamayan, Shahryar | |
| dc.contributor.author | Kermani Poor, Masoud | |
| dc.date.accessioned | 2024-08-27T17:36:38Z | |
| dc.date.available | 2024-08-27T17:36:38Z | |
| dc.date.issued | 2024-08-01 | |
| dc.degree.discipline | Computer Science | |
| dc.degree.level | Master of Science (MSc) | |
| dc.description.abstract | Multi-objective optimization problems aim to identify solutions that maximize or minimize conflicting objectives. Population-based multi-objective algorithms, inspired by biological populations, are effective but often provide limited solutions within the decisionmakers’ region of interest (ROI) on the Pareto front. Recent advancements in machine learning have shown promise in generating solutions, yet they suffer from a lack of control and require knowledge of objective function attributes. This study proposes a framework using Gaussian process regression and artificial neural networks to generate innovative solutions in the ROI. By employing diverse sampling techniques and integrating long term memory, the framework can produce more than twice as many solutions in the ROI, as demonstrated in experiments with real-world problems and various benchmark functions. | |
| dc.description.sponsorship | University of Ontario Institute of Technology | |
| dc.identifier.uri | https://ontariotechu.scholaris.ca/handle/10155/1828 | |
| dc.language.iso | en | |
| dc.subject.other | Multi-objective optimization | |
| dc.subject.other | Machine learning | |
| dc.subject.other | Decision-maker | |
| dc.subject.other | Region of Interest | |
| dc.subject.other | Machine learning assisted many-objective optimization | |
| dc.title | A targeted reverse mapping machine learning approach for non-dominated solutions in multi-objective optimization | |
| dc.type | Thesis |