Addressing The Knapsack Challenge Through Cultural Algorithm Optimization
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866917592760844288 |
|---|---|
| author | Vahdatpour, Mohammad Saleh |
| author_facet | Vahdatpour, Mohammad Saleh |
| contents | The "0-1 knapsack problem" stands as a classical combinatorial optimization conundrum, necessitating the selection of a subset of items from a given set. Each item possesses inherent values and weights, and the primary objective is to formulate a selection strategy that maximizes the total value while adhering to a predefined capacity constraint. In this research paper, we introduce a novel variant of Cultural Algorithms tailored specifically for solving 0-1 knapsack problems, a well-known combinatorial optimization challenge. Our proposed algorithm incorporates a belief space to refine the population and introduces two vital functions for dynamically adjusting the crossover and mutation rates during the evolutionary process. Through extensive experimentation, we provide compelling evidence of the algorithm's remarkable efficiency in consistently locating the global optimum, even in knapsack problems characterized by high dimensions and intricate constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03324 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Addressing The Knapsack Challenge Through Cultural Algorithm Optimization Vahdatpour, Mohammad Saleh Neural and Evolutionary Computing The "0-1 knapsack problem" stands as a classical combinatorial optimization conundrum, necessitating the selection of a subset of items from a given set. Each item possesses inherent values and weights, and the primary objective is to formulate a selection strategy that maximizes the total value while adhering to a predefined capacity constraint. In this research paper, we introduce a novel variant of Cultural Algorithms tailored specifically for solving 0-1 knapsack problems, a well-known combinatorial optimization challenge. Our proposed algorithm incorporates a belief space to refine the population and introduces two vital functions for dynamically adjusting the crossover and mutation rates during the evolutionary process. Through extensive experimentation, we provide compelling evidence of the algorithm's remarkable efficiency in consistently locating the global optimum, even in knapsack problems characterized by high dimensions and intricate constraints. |
| title | Addressing The Knapsack Challenge Through Cultural Algorithm Optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2401.03324 |