References
- A. Abraham, H. Guo, H. Liu, Swarm intelligence: foundations, perspectives and applications, in: Swarm Intelligent Systems, Springer, 2006, pp. 3-25.
- E. Sanchez, G. Squillero, A. Tonda, Industrial Applications of Evolutionary Algorithms.
- A.A. Freitas, A review of evolutionary algorithms for e-commerce, in: E-Commerce and Intelligent Methods, Springer, 2002, pp. 159-179.
- C. Grosan, A. Abraham, Hybrid evolutionary algorithms: methodologies, architectures, and reviews, in: Hybrid Evolutionary Algorithms, Springer, 2007, pp. 1-17.
- Y.-T. Kao, E. Zahara, A hybrid genetic algorithm and particle swarm optimization for multimodal functions, Appl. Soft Comput. 8 (2) (2008) 849-857. https://doi.org/10.1016/j.asoc.2007.07.002
- P.-H. Chen, S.M. Shahandashti, Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints, Autom. ConStruct. 18 (4) (2009) 434-443. https://doi.org/10.1016/j.autcon.2008.10.007
- P.C. Ma, F. Tao, Y.L. Liu, L. Zhang, H.X. Lu, Z. Ding, A hybrid particle swarm optimization and simulated annealing algorithm for job-shop scheduling, in: 2014 IEEE International Conference on Automation Science and Engineering (CASE), IEEE, 2014, pp. 125-130.
- Q. Shen, W.-M. Shi, W. Kong, Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data, Comput. Biol. Chem. 32 (1) (2008) 53-60. https://doi.org/10.1016/j.compbiolchem.2007.10.001
- A. Jamasb, S.-H. Motavalli-Anbaran, K. Ghasemi, A novel hybrid algorithm of particle swarm optimization and evolution strategies for geophysical nonlinear inverse problems, Pure Appl. Geophys. 176 (4) (2019) 1601-1613. https://doi.org/10.1007/s00024-018-2059-7
- T. Shankar, S. Shanmugavel, A. Rajesh, Hybrid hsa and pso algorithm for energy efficient cluster head selection in wireless sensor networks, Swarm Evol. Comput. 30 (2016) 1-10. https://doi.org/10.1016/j.swevo.2016.03.003
- J. Sun, H. Zhang, A. Zhou, Q. Zhang, K. Zhang, A new learning-based adaptive multi-objective evolutionary algorithm, Swarm Evol. Comput. 44 (2019) 304-319. https://doi.org/10.1016/j.swevo.2018.04.009
- Y. Zhang, X. Liu, F. Bao, J. Chi, C. Zhang, P. Liu, Particle Swarm Optimization with Adaptive Learning Strategy, Knowledge-Based Systems, 2020, 105789.
- A. Assad, K. Deep, A hybrid harmony search and simulated annealing algorithm for continuous optimization, Inf. Sci. 450 (2018) 246e266.
- A. Saad, S.A. Khan, A. Mahmood, A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design, Swarm Evol. Comput. 38 (2018) 187e201.
- A. Kaur, K. Kumar, A reinforcement learning based evolutionary multiobjective optimization algorithm for spectrum allocation in cognitive radio networks, Phys. Commun. 43 (2020), 101196. https://doi.org/10.1016/j.phycom.2020.101196
- H. Samma, C.P. Lim, J.M. Saleh, A new reinforcement learning-based memetic particle swarm optimizer, Appl. Soft Comput. 43 (2016) 276-297. https://doi.org/10.1016/j.asoc.2016.01.006
- M.I. Radaideh, K. Shirvan, Rule-based reinforcement learning methodology to inform evolutionary algorithms for constrained optimization of engineering applications, Knowl. Base Syst. 217 (2021), 106836. https://doi.org/10.1016/j.knosys.2021.106836
- M.M. Drugan, Reinforcement learning versus evolutionary computation: a survey on hybrid algorithms, Swarm Evol. Comput. 44 (2019) 228-246. https://doi.org/10.1016/j.swevo.2018.03.011
- C.M. del Campo, J. Francois, H. Lopez, Axial: a system for boiling water reactor fuel assembly axial optimization using genetic algorithms, Ann. Nucl. Energy 28 (16) (2001) 1667-1682. https://doi.org/10.1016/S0306-4549(01)00002-0
- J. Francois, C. Martin-del Campo, R. Francois, L. Morales, A practical optimization procedure for radial bwr fuel lattice design using tabu search with a multiobjective function, Ann. Nucl. Energy 30 (12) (2003) 1213-1229. https://doi.org/10.1016/S0306-4549(03)00055-0
- T. Rogers, J. Ragusa, S. Schultz, R.S. Clair, Optimization of pwr fuel assembly radial enrichment and burnable poison location based on adaptive simulated annealing, Nucl. Eng. Des. 239 (6) (2009) 1019-1029. https://doi.org/10.1016/j.nucengdes.2009.02.005
- A. Charles, G. Parks, Application of differential evolution algorithms to multiobjective optimization problems in mixed-oxide fuel assembly design, Ann. Nucl. Energy 127 (2019) 165-177. https://doi.org/10.1016/j.anucene.2018.12.002
- F. Khoshahval, A. Zolfaghari, H. Minuchehr, M. Abbasi, A new hybrid method for multi-objective fuel management optimization using parallel pso-sa, Prog. Nucl. Energy 76 (2014) 112-121. https://doi.org/10.1016/j.pnucene.2014.05.014
- A. Zameer, S.M. Mirza, N.M. Mirza, Core loading pattern optimization of a typical two-loop 300 mwe pwr using simulated annealing (sa), novel crossover genetic algorithms (ga) and hybrid ga (sa) schemes, Ann. Nucl. Energy 65 (2014) 122-131. https://doi.org/10.1016/j.anucene.2013.10.024
- O. Safarzadeh, A. Zolfaghari, M. Zangian, O. Noori-kalkhoran, Pattern optimization of pwr reactor using hybrid parallel artificial bee colony, Ann. Nucl. Energy 63 (2014) 295-301. https://doi.org/10.1016/j.anucene.2013.08.011
- E. Schlunz, P.M. Bokov, J.H. van Vuuren, Multiobjective in-core nuclear fuel management optimisation by means of a hyperheuristic, Swarm Evol. Comput. 42 (2018) 58-76. https://doi.org/10.1016/j.swevo.2018.02.019
- G.T. Parks, Multiobjective pressurized water reactor reload core design by nondominated genetic algorithm search, Nucl. Sci. Eng. 124 (1) (1996) 178-187. https://doi.org/10.13182/NSE96-A24233
- V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, et al., Human-level control through deep reinforcement learning, Nature 518 (7540) (2015) 529-533. https://doi.org/10.1038/nature14236
- T. Schaul, J. Quan, I. Antonoglou, D. Silver, Prioritized Experience Replay, arXiv preprint arXiv:1511.05952 (2015).
- D.W. Corne, J.D. Knowles, M.J. Oates, The pareto envelope-based selection algorithm for multiobjective optimization, in: International Conference on Parallel Problem Solving from Nature, Springer, 2000, pp. 839-848.
- J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of ICNN'95-International Conference on Neural Networks vol. 4, IEEE, 1995, pp. 1942-1948.
- H.-G. Beyer, H.-P. Schwefel, Evolution strategiesea comprehensive introduction, Nat. Comput. 1 (1) (2002) 3-52. https://doi.org/10.1023/A:1015059928466
- S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by simulated annealing, Science 220 (4598) (1983) 671-680. https://doi.org/10.1126/science.220.4598.671
- R. Li, M.T. Emmerich, J. Eggermont, T. Back, M. Schutz, J. Dijkstra, J.H. Reiber, Mixed integer evolution strategies for parameter optimization, Evol. Comput. 21 (1) (2013) 29-64. https://doi.org/10.1162/EVCO_a_00059
- M. Clerc, J. Kennedy, The particle swarm-explosion, stability, and convergence in a multidimensional complex space, IEEE Trans. Evol. Comput. 6 (1) (2002) 58-73. https://doi.org/10.1109/4235.985692
- Y. Shi, R. Eberhart, A modified particle swarm optimizer, in: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), IEEE, 1998, pp. 69-73.
- M.S. Arumugam, M. Rao, On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems, Discrete Dynam Nat. Soc. 2006 (2006), 079295.
- J. Kennedy, R.C. Eberhart, A discrete binary version of the particle swarm algorithm, in: 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation vol. 5, IEEE, 1997, pp. 4104-4108.
- L.V. Santana-Quintero, A.A. Montano, C.A.C. Coello, A review of techniques for handling expensive functions in evolutionary multi-objective optimization, in: Computational Intelligence in Expensive Optimization Problems, Springer, 2010, pp. 29-59.
- Z. Zhou, Y.S. Ong, P.B. Nair, Hierarchical surrogate-assisted evolutionary optimization framework, in: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No. 04TH8753) vol. 2, IEEE, 2004, pp. 1586-1593.
- M.A. El-Beltagy, A.J. Keane, Evolutionary optimization for computationally expensive problems using Gaussian processes, in: Proc. Int. Conf. On Artificial Intelligence vol. 1, Citeseer, 2001, pp. 708-714.
- L. Willmes, T. Back, Y. Jin, B. Sendhoff, Comparing neural networks and kriging for fitness approximation in evolutionary optimization, in: The 2003 Congress on Evolutionary Computation vol. 1, IEEE, 2003, pp. 663-670.
- N.H. Awad, M.Z. Ali, P.N. Suganthan, J.J. Liang, B.Y. Qu, Problem Definitions and Evaluation Criteria for the Cec 2017 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization, Nanyang Technological University, Singapore, 2017. Tech. rep.
- M. Edenius, K. Ekberg, B. H. Forssen, D. Knott, Casmo-4, a Fuel Assembly Burnup Program, User's Manual, Studsvik-SOA-9501, Studsvik of America, Inc.
- M.I. Radaideh, B. Forget, K. Shirvan, Large-scale design optimisation of boiling water reactor bundles with neuroevolution, Ann. Nucl. Energy 160 (2021), 108355. https://doi.org/10.1016/j.anucene.2021.108355
- S. Challita, F. Zalila, C. Gourdin, P. Merle, A precise model for google cloud platform, in: 2018 IEEE International Conference on Cloud Engineering (IC2E), IEEE, 2018, pp. 177-183.
- J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal Policy Optimization Algorithms, 2017 arXiv preprint arXiv:1707.06347.
- A. Hill, A. Raffin, M. Ernestus, A. Gleave, R. Traore, P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, et al., Stable Baselines, GitHub Repository.
- M.I. Radaideh, K. Du, P. Seurin, D. Seyler, X. Gu, H. Wang, K. Shirvan, NEORL: NeuroEvolution Optimization with Reinforcement Learning, arXiv preprint, 2021. arXiv:2112.07057.