Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm
| Autor: | Prachi Agrawal, Ali Wagdy Mohamed, Talari Ganesh |
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| Rok vydání: | 2021 |
| Předmět: |
Mathematical optimization
021103 operations research Computer science 0211 other engineering and technologies Binary number Computational intelligence 02 engineering and technology General Medicine Set (abstract data type) Reduction (complexity) Local optimum Knapsack problem Robustness (computer science) Convergence (routing) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing |
| Zdroj: | Complex & Intelligent Systems. 8:43-63 |
| ISSN: | 2198-6053 2199-4536 |
| DOI: | 10.1007/s40747-021-00351-8 |
| Popis: | This article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy. |
| Databáze: | OpenAIRE |
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