{"id":{"repo_id":"ncsu","oai_identifier":"oai:repository.lib.ncsu.edu:1840.16/272"},"canonical_url":"https://search.dev.ndltd.org/etd/ncsu/oai:repository.lib.ncsu.edu:1840.16/272","repository":{"repo_id":"ncsu","name":"North Carolina State University","base_url":"https://repository.lib.ncsu.edu/server/oai/request"},"display":{"title":"Tchebycheff Method-based Evolutionary Algorithm for Multiobjective Optimization","abstract":"In the operations research literature, the Tchebycheff method has been demonstrated to be a useful approach for exploring the non-dominated solutions for multiobjective optimization problems. While this method has been investigated with mathematical programming-based solution approaches, its application with modern heuristic search procedures is lacking. As heuristic search procedures continue to show promise as practical solution approaches for realistic engineering problems typically with multiple design objectives, the need for their applications in multiobjective optimization is becoming increasingly important. This paper investigates a new evolutionary algorithm-based multiobjective optimization procedure that builds upon the Tchebycheff method. By embedding a beneficial seeding approach, the efficiency of the algorithm is expectedly enhanced. This Tchebycheff Method-based Evolutionary Algorithm (TMEA) is tested and evaluated using a suite of 2-objective test problems, representing a range of complexities in the decision space as well as in the objective space. The performance of TMEA with those of other multiobjective evolutionary algorithms are compared using several performance metrics that are reported in the literature. For the problems considered in this paper, TMEA performs relatively well in generating non-dominated solutions that are close to the known Pareto set and are well distributed in the non-inferior space.","abstract_html":"In the operations research literature, the Tchebycheff method has been demonstrated to be a useful approach for exploring the non-dominated solutions for multiobjective optimization problems. While this method has been investigated with mathematical programming-based solution approaches, its application with modern heuristic search procedures is lacking. As heuristic search procedures continue to show promise as practical solution approaches for realistic engineering problems typically with multiple design objectives, the need for their applications in multiobjective optimization is becoming increasingly important. This paper investigates a new evolutionary algorithm-based multiobjective optimization procedure that builds upon the Tchebycheff method. By embedding a beneficial seeding approach, the efficiency of the algorithm is expectedly enhanced. This Tchebycheff Method-based Evolutionary Algorithm (TMEA) is tested and evaluated using a suite of 2-objective test problems, representing a range of complexities in the decision space as well as in the objective space. The performance of TMEA with those of other multiobjective evolutionary algorithms are compared using several performance metrics that are reported in the literature. For the problems considered in this paper, TMEA performs relatively well in generating non-dominated solutions that are close to the known Pareto set and are well distributed in the non-inferior space.","abstract_has_math":false,"creators":["Rao, Sunil Murali"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["S.R. Ranjithan, Committee Chair","Dr. E.D. 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I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. 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This paper investigates a new evolutionary algorithm-based multiobjective optimization procedure that builds upon the Tchebycheff method. By embedding a beneficial seeding approach, the efficiency of the algorithm is expectedly enhanced. This Tchebycheff Method-based Evolutionary Algorithm (TMEA) is tested and evaluated using a suite of 2-objective test problems, representing a range of complexities in the decision space as well as in the objective space. The performance of TMEA with those of other multiobjective evolutionary algorithms are compared using several performance metrics that are reported in the literature. 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I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report."],"dc:subject":["Multiobjective Optimization","Evolutionary computation"],"dc:title":["Tchebycheff Method-based Evolutionary Algorithm for Multiobjective Optimization"]},"updated_at":"2026-08-21T22:21:56Z"}