Multidimensional Knapsack 0-1 Solution With Algorithm Evolution Pso-Ga
Keywords:Particle swarm optimization, Genetic Algorithm, Evolusi
This paper develops the particle swarm optimization (PSO) method and uses a genetic algorithm (GA) by changing the distribution of articles in the initialization of the initial position. PSO at this time the search and speed of particles will always go to the best solution so that by narrowing the search area will be faster by updating the best position of PSO. While the Genetic algorithm plays a role to get an expanded search area for PSO solutions by utilizing crossover and mutation in GA. So that GA will expand the range of candidates for the best solution in PSO. From each of the advantages of PSO Update and GA will be combined to get Evolutionary PSO-GA (EVPGA) that can minimize error and speed up computation (itation) in finding the best solution. By using the Multidimensional Knapsack data set, the results of EVPGA get an average speed of 24.9s with an error of 1.49%.
bdollahi, S., Deldari, A., Asadi, H., Montazerolghaem, A., & Mazinani, S. M. (2021). Flow-Aware Forwarding in SDN Datacenters Using a Knapsack-PSO-Based Solution. IEEE Transactions on Network and Service Management, 18(3), 2902–2914. https://doi.org/10.1109/TNSM.2021.3064974
Cui, Y., Qiao, J., & Meng, X. (2020). Multi-stage multi-objective particle swarm optimization algorithm based on the evolutionary information of population. 2020 Chinese Automation Congress (CAC), 3412–3417. https://doi.org/10.1109/CAC51589.2020.9326666
Gupta, I. K. (2018). A hybrid GA-GSA algorithm to solve multidimensional knapsack problem. 2018 4th International Conference on Recent Advances in Information Technology (RAIT), 1–6. https://doi.org/10.1109/RAIT.2018.8389069
Hayashida, T., Nishizaki, I., Sekizaki, S., & Takamori, Y. (2019). Improvement of Two-swarm Cooperative Particle Swarm Optimization Using Immune Algorithms and Swarm Clustering. 2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA), 101–107. https://doi.org/10.1109/IWCIA47330.2019.8955042
Li, Y., & Zhang, Y. (2020). Hyper-parameter estimation method with particle swarm optimization (arXiv:2011.11944). arXiv. http://arxiv.org/abs/2011.11944
Liu, S., Gao, X., He, H., & Zhang, S. (2019). Self-adaptive chaotic local search particle swarm optimization for propylene explosion region parameter identification. 2019 Chinese Control And Decision Conference (CCDC), 1702–1707. https://doi.org/10.1109/CCDC.2019.8833290
Liu, Z. (2020). An Analysis of Particle Swarm Optimization of Multi-objective Knapsack Problem. 2020 9th International Conference on Industrial Technology and Management (ICITM), 302–306. https://doi.org/10.1109/ICITM48982.2020.9080345
Luo, D., Ji, W., & Hu, X. (2023). Parameter Optimization and Control Strategy of Hybrid Electric Vehicle Transmission System based on Improved GA Algorithm. Processes, 11(5), 1554. https://doi.org/10.3390/pr11051554
Saire, J. E. C., & Singh, A. (2019). A Comparative Analysis of Quantum Inspired Evolutionary Algorithm with Differential Evolution, Evolutionary Strategy and Particle Swarm Optimization. 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 1–6. https://doi.org/10.1109/LA-CCI47412.2019.9037039
Wang, X., & Li, C. (2018). Prediction Model of MBR Membrane Flux for Elman Neural Network Based on PSO-GA Hybrid Algorithm. 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 737–740. https://doi.org/10.1109/IMCCC.2018.00159
How to Cite
Copyright (c) 2023 Yudistira Arya Sapoetra, Azwar Riza Habibi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.