{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162904"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162904","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Metaheuristic Optimization for Automatic Arrangement of Power Electronics Components in a Shipboard Electrical Distribution System","abstract":"This thesis proposes a novel methodology for the automatic placement of Power Electronics Building Blocks (PEBBs) in modular, integrated power corridor designs. These building blocks, which are created and tested offsite for a variety of applications, are currently placed manually during the design process, a method that is time-consuming and suboptimal. To address this challenge, we reduce the placement problem to a 2D bin-packing problem, leveraging a hybrid approach combining Genetic Algorithms and Simulated Annealing. This approach enables the generation of optimized placements that find the extremes of arbitrary heuristics, including minimizing routing distance and power density, effectively improving both design efficiency and system performance. The proposed methodology offers a significant step toward automating and optimizing the layout of power electronic components in complex systems.","abstract_html":"This thesis proposes a novel methodology for the automatic placement of Power Electronics Building Blocks (PEBBs) in modular, integrated power corridor designs. These building blocks, which are created and tested offsite for a variety of applications, are currently placed manually during the design process, a method that is time-consuming and suboptimal. To address this challenge, we reduce the placement problem to a 2D bin-packing problem, leveraging a hybrid approach combining Genetic Algorithms and Simulated Annealing. This approach enables the generation of optimized placements that find the extremes of arbitrary heuristics, including minimizing routing distance and power density, effectively improving both design efficiency and system performance. The proposed methodology offers a significant step toward automating and optimizing the layout of power electronic components in complex systems.","abstract_has_math":false,"creators":["Lohier, Sebastien"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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These building blocks, which are created and tested offsite for a variety of applications, are currently placed manually during the design process, a method that is time-consuming and suboptimal. To address this challenge, we reduce the placement problem to a 2D bin-packing problem, leveraging a hybrid approach combining Genetic Algorithms and Simulated Annealing. This approach enables the generation of optimized placements that find the extremes of arbitrary heuristics, including minimizing routing distance and power density, effectively improving both design efficiency and system performance. 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These building blocks, which are created and tested offsite for a variety of applications, are currently placed manually during the design process, a method that is time-consuming and suboptimal. To address this challenge, we reduce the placement problem to a 2D bin-packing problem, leveraging a hybrid approach combining Genetic Algorithms and Simulated Annealing. This approach enables the generation of optimized placements that find the extremes of arbitrary heuristics, including minimizing routing distance and power density, effectively improving both design efficiency and system performance. 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