Hybrid Cat-Particle Swarm Optimization Algorithm on Bounded Knapsack Problem with Multiple Constraints
- 10.2991/acsr.k.220202.045How to use a DOI?
- Hybrid cat-particle swarm optimization; Metaheuristic; Modified bounded knapsack problem
Optimization problems have become interesting problems to discuss, including the knapsack problem. There are many types and variations of knapsack problems. In this paper, the authors introduce a new hybrid metaheuristic algorithm to solve the modified bounded knapsack problem with multiple constraints we call it modified bounded knapsack problem with multiple constraints (MBKP-MC). Authors combine two popular metaheuristic algorithms, Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). The algorithm is named Hybrid Cat-Particle Swarm Optimization (HCPSO). The results of the implementation of the algorithm are compared with PSO and CSO algorithms. Based on the experimental results, it is known that the HCPSO algorithm is suitable and can reach to good-quality solution within a reasonable computation time. In addition, the new proposed algorithm performs better than the PSO and CSO on all MBKP-MC data used.
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Kiswara Agung Santoso AU - Muhammad Bagus Kurniawan AU - Ahmad Kamsyakawuni AU - Abduh Riski PY - 2022 DA - 2022/02/08 TI - Hybrid Cat-Particle Swarm Optimization Algorithm on Bounded Knapsack Problem with Multiple Constraints BT - Proceedings of the International Conference on Mathematics, Geometry, Statistics, and Computation (IC-MaGeStiC 2021) PB - Atlantis Press SP - 244 EP - 248 SN - 2352-538X UR - https://doi.org/10.2991/acsr.k.220202.045 DO - 10.2991/acsr.k.220202.045 ID - Santoso2022 ER -