| 1. Qu B Y, Zhu Y S, Jiao Y C, and et al. A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems. Swarm and Evolutionary Computation, 2018, 38: 1-11. 2. Xing H, Ji Y, Bai L, and et al. An adaptive evolution based quantum inspired evolutionary algorithm for QoS multicasting in IP/DWDM networks. Computer Communications, 2009, 32(6): 1086-1094. 3. Qu Z, Fu J, Liu X, Li C. Network coding resources optimization with transmission delay constraint in multicast networks. High Technology Letters, 2017, 23(1): 30-37. 4. Hou Y, Wu N, Zhou M, Li Z. Pareto-Optimization for Scheduling of Crude Oil Operations in Refinery via Genetic Algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(3): 296-307. 5. Zitzler E, and Lothar T. Multi-objective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation, 1999, 3(4): 517-530. 6. Cho J, Wang Y, Chen I, Chan K. A Survey on Modeling and Optimizing Multi-Objective Systems. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1867-1901. 7. Han K H, and Kim J H. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE transactions on evolutionary computation, 2002, 6(6): 580-593. 8. Ada C, Wu P, Chu F, and Zhou M C. Improved quantum-inspired evolutionary algorithm for large-size lane reservation. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(12): 1535-1548. 9. Gupta S, Stuti M, Tamanna G, and et al. Parallel quantum-inspired evolutionary algorithms for community detection in social networks. Applied Soft Computing, 2017, 61: 331-353. 10. Konar D, Siddhartha B, Kalpana S, and et al. An improved Hybrid Quantum-Inspired Genetic Algorithm (HQIGA) for scheduling of real-time task in multiprocessor system. Applied Soft Computing, 2017, 53: 296-307. 11. Qu Z, Liu X, Zhang X, and et al. Hamming-distance-based adaptive quantum-inspired evolutionary algorithm for network coding resources optimization. The Journal of China Universities of Posts and Telecommunications, 2015, 22(3): 92-99. 12. Sheng X, He Y, Chang L, and et al. An Improved Quantum-Inspired Evolutionary Algorithm for Knapsack Problems. The 3rd International Conference on Cloud Computing and Security (ICCCS 2017), 16-18 Jun, 2017, Nanjing, China. Springer, Cham, 2017: 694-708. 13. Li J, Li W, Huang Y. A New Quantum Rotation Angle of Quantum-Inspired Evolutionary Algorithm for TSP. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 21-24 July, 2017, Guangzhou, China. IEEE, 2017: 369-374. 14. Xiong H, Wu Z, Fan H, Li G, Jiang G. Quantum rotation gate in quantum-inspired evolutionary algorithm: A review, analysis and comparison study. Swarm and Evolutionary Computation, 2018, 42: 43-57. 15. Darrell W. A genetic algorithm tutorial. Statistics and computing, 1994, 4(2): 65-85. 16. Han K H, Park K H, Lee C H, and et al. Parallel quantum-inspired genetic algorithm for combinatorial optimization problem. Evolutionary Computation, 2001. Proceedings of the 2001 Congress on. IEEE, 2001, 2: 1422-1429. 17. Wang Y, Feng X Y, Huang Y X, et al. A novel quantum swarm evolutionary algorithm and its applications. Neurocomputing, 2007, 70(4-6): 633-640. 18. Wang Y P, Li Y H. A Novel Quantum Genetic Algorithm for TSP. Chinese journal of computers, 2007(05):5748-5755. (in chinese) |
