Multi-objective optimization on dynamic complex networks
| dc.contributor.author | Liu, Ying Ying | |
| dc.contributor.examiningcommittee | Irani, Pourang (Computer Science) | en_US |
| dc.contributor.examiningcommittee | Muthukumarana, Saman (Statistics) | en_US |
| dc.contributor.examiningcommittee | Nayak, Amiya (University of Ottawa) | en_US |
| dc.contributor.supervisor | Thulasiraman, Parimala | |
| dc.date.accessioned | 2022-12-14T20:50:51Z | |
| dc.date.available | 2022-12-14T20:50:51Z | |
| dc.date.copyright | 2022-12-14 | |
| dc.date.issued | 2022-12-14 | |
| dc.date.submitted | 2022-12-14T19:37:26Z | en_US |
| dc.degree.discipline | Computer Science | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Many real world applications modelled as complex networks optimize multiple conflicting objective functions that change frequently over time. Solving multi-objective optimization problems (MOP) on complex networks, either static or dynamic, is NP-hard. Evolutionary multi-objective optimization (EMO) heuristics based on Pareto criterion (PC) dominance and non-Pareto criterion (NPC) decomposition techniques have been successfully used to solve dynamic MOP (DMOP). However, addressing both convergence and diversity, simultaneously, in either PC or NPC evolution is a challenge. Moreover, existing metrics are not robust to evaluate EMO algorithms for solving varying real-world applications that exhibit dynamic behaviour. The contribution of this thesis is therefore two-fold: (i) we propose a co-evolutionary bi-criterion algorithm: Cooperative, Concurrent, Coevolutionary Multi-Objective (Co3_MO), that exploits the strengths of PC and NPC evolution to solve DMOP; (ii) we propose a new evaluation framework and metrics to measure convergence, diversity and dynamism of dynamic EMO algorithms. We study our proposed algorithm, framework and metrics on the multi-objective travelling salesperson problem (MTSP) incorporating two popular complementary evolutionary techniques (NSGA-II and MOEA/D). We perform various experiments on both static and dynamic MTSP datasets and show that Co3_MO with local search and hybrid operators performs better than stand-alone EMO techniques. We also show that our proposed framework and metrics are flexible to incorporate domain-specific knowledge of an application to capture convergence, diversity and dynamism of the evaluated EMO algorithms. | en_US |
| dc.description.note | February 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/37005 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | evolutionary dynamic multi-objective optimization | en_US |
| dc.subject | dynamic complex networks | en_US |
| dc.subject | dynamic multi-objective travelling salesperson problem | en_US |
| dc.subject | bicriterion coevolution | en_US |
| dc.subject | local search | en_US |
| dc.subject | genetic operator | en_US |
| dc.subject | performance evaluation | en_US |
| dc.subject | severity change | en_US |
| dc.title | Multi-objective optimization on dynamic complex networks | en_US |
| dc.type | doctoral thesis | en_US |
| local.subject.manitoba | no | en_US |