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dc.contributor.authorMaltese, Justin
dc.date.accessioned2016-05-09T20:41:38Z
dc.date.available2016-05-09T20:41:38Z
dc.identifier.urihttp://hdl.handle.net/10464/9277
dc.description.abstractMany real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectMulti-objective Optimizationen_US
dc.subjectMany-objective Optimizationen_US
dc.subjectComputational Intelligenceen_US
dc.subjectPareto Optimalityen_US
dc.subjectOptimization Algorithmsen_US
dc.titleA Scalability Study and New Algorithms for Large-Scale Many-Objective Optimizationen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US


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