Publicação
Origami toolkit: metaheuristic framework for combinatorial optimization problems: a machine learning tool for problem-solving and decision-making
| Resumo: | The optimization of real-world problems is a challenging activity, difficult to be formulated and solved. Metaheuristics are recognized as powerful "optimization problem" solvers, and sometimes they are the only feasible approach. Metaheuristics are general heuristic that works in a meta-level - which can be applied to a wide variety of optimization problems. Metaheuristics can be accommodated to include problem specificities. However, these inclusions require a set of efforts to adapt the metaheuristic algorithm for a determined problem. In this master thesis, it will be researched and explored alternatives to develop a metaheuristic framework. Consequently, putting the metaheuristics on a higher level of abstraction. With this in mind, the framework is an approach to eliminate the necessity to adapt the metaheuristic to the problem peculiarities. Moreover, it also considers defining a standardization for problem formulation and object creation. |
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| Autores principais: | Peres, Fernando Augusto Junqueira |
| Assunto: | Metaheuristic Metaheuristic Framework Machine Learning Software Framework Problem-solving Combinatorial Optimization Problem Problem formulation |
| Ano: | 2020 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The optimization of real-world problems is a challenging activity, difficult to be formulated and solved. Metaheuristics are recognized as powerful "optimization problem" solvers, and sometimes they are the only feasible approach. Metaheuristics are general heuristic that works in a meta-level - which can be applied to a wide variety of optimization problems. Metaheuristics can be accommodated to include problem specificities. However, these inclusions require a set of efforts to adapt the metaheuristic algorithm for a determined problem. In this master thesis, it will be researched and explored alternatives to develop a metaheuristic framework. Consequently, putting the metaheuristics on a higher level of abstraction. With this in mind, the framework is an approach to eliminate the necessity to adapt the metaheuristic to the problem peculiarities. Moreover, it also considers defining a standardization for problem formulation and object creation. |
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