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dc.contributor.authorSen Gupta, Arpi
dc.date.accessioned2019-10-11T17:11:21Z
dc.date.available2019-10-11T17:11:21Z
dc.identifier.urihttp://hdl.handle.net/10464/14540
dc.description.abstractMany-objective optimization problems (MaOPs) are multi-objective optimization problems which have more than three objectives. MaOPs face significant challenges because of search efficiency, computational cost, decision making, and visualization. Many well-known multi-objective evolutionary algorithms do not scale well with an increasing number of objectives. The objective reduction can alleviate such difficulties. However, most research in objective reduction use non-dominated sorting or Pareto ranking. However, Pareto is effective in problems having less than four objectives. In this research, we use two approaches to objective reduction: random-based and linear coefficient-based. We use the sum of ranks instead of Pareto Ranking. When applied to many-objective problems, the sum of ranks has outperformed many other optimization approaches. We also use the age layered population structure (ALPS). We use ALPS in our approach to remove premature convergence and improve results. The performance of the proposed methods has been studied extensively on the famous benchmark problem DTLZ. The original GA and ALPS outperform the objective reduction algorithms in many test cases of DTLZ. Among all reduction algorithms, a linear coefficient based reduction algorithm provides better performance for some problems in this test suite. Random based reduction is not an appropriate strategy for reducing objectives.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectobjective reductionen_US
dc.subjectmany-objective optimizationen_US
dc.subjectrandom reductionen_US
dc.subjectcorrelation reductionen_US
dc.titleObjective reduction in many-objective optimization problemsen_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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