KP01 Solved by an n-Dimensional Sampling and Clustering Heuristic
In the field of optimization, NP-Hard problems play an important role concerning its real-world applications, such as resource allocation, scheduling, planning, logistics, etc. In this paper, we propose a heuristic search algorithm based on Montecarlo along with a clustering strategy that analyzes d...
| Autores: | , , , |
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| Tipo de documento: | capítulo de livro |
| Data de publicação: | 2022 |
| País: | España |
| Recursos: | Universitat Autònoma de Barcelona |
| Repositório: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglês |
| OAI Identifier: | oai:ddd.uab.cat:308733 |
| Acesso em linha: | https://ddd.uab.cat/record/308733 https://dx.doi.org/urn:doi:10.1007/978-3-031-08754-7_31 |
| Access Level: | Acceso aberto |
| Palavra-chave: | 01 Knapsack Problem Clustering Heuristic method |
| Resumo: | In the field of optimization, NP-Hard problems play an important role concerning its real-world applications, such as resource allocation, scheduling, planning, logistics, etc. In this paper, we propose a heuristic search algorithm based on Montecarlo along with a clustering strategy that analyzes density and performs k-means partitions to solve the classic binary Knapsack Problem (KP01). Our heuristic method, which was designed to solve combinatorial optimization problems, has evolved and can adapt to other optimization problems, such as the KP01 that can be organized in an n-Dimensional search space. Regarding the methodology, we substantially reduced the search space while the areas of interest were located in the clustering stage, which brings us closer to the best solutions. After the experiments, we obtained a high-quality solution, which resulted in an average success rate of above 90%. |
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