Referred Journal Papers      

      1. Wang, R., Purshouse, R. C., Fleming, P. J., Preference-inspired co-evolutionary algorithms for many objective optimisation, IEEE Transactions on Evolutionary Computation., 17 (4), 474-494, 2013. SCI 一区 IF = 5.82 (ESI引用率前1%，同时是2015年3、4、5、6月份ESI Computer Science领域Hot paper, 即引用率进入该领域0.1%).

2. Wang, R., Zhang, Q. F., Zhang, T., Decomposition based algorithms using Pareto adaptive scalarizing methods IEEE Transactions on Evolutionary Computation, IEEE, 2015, To appear SCI 一区 IF = 5.82

3. Wang, R., Purshouse, R. C., Fleming, P. J., Preference-inspired co-evolutionary algorithms using weights for many objective optimisation , European Journal of Operational Research, 243(2), 423-441, 2015. SCI 二区 IF = 2.36 (ESI引用率前10%)

4. Wang, R., Fleming, P. J., Purshouse, R. C., General framework for localised multi-objective evolutionary algorithms, Information sciences, 258(2), 29-53, 2014. SCI 二区 IF = 4.04 (ESI引用率前10%)

5. Wang, R., Purshouse, R. C., Giagkiozis, I., Fleming, P. J., The iPICEA-g a new hybrid evolutionary multi-criteria decision making approach using the brushing technique, European Journal of Operational Research, 243(2), 442-453, 2015. SCI 二区 IF = 2.36

6. Wang, R., Maszatul M. Mansor, Purshouse, R. C., Fleming, P. J., An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms, International Journal of Systems Science , 2015, In Press, DOI: 10.1016/j.ijss.2015.01.008. SCI 三区 IF = 2.10

7. Shi, Z.C., Wang, R., Zhang, T., Multi-objective optimal design of hybrid renewable energy systems using preference-inspired coevolutionary approach. Solar Energy, Volume 118, August 2015, Pages 96-106. SCI 二区 IF = 3. 469

8. Lei, H., Wang, R., Laporte, G., Solving a multi-objective dynamic stochastic districting and routing problem with a co-evolutionary algorithm, Computers & Operations Research, Elsevier, 2015, Doi:10.1016/j.cor.2015.09.002. SCI 二区 IF = 1.86

9. Zhang, T., Wang, R., Liu, Y. J., Guo, B., An enhanced preference-inspired co-evolutionary algorithm using orthogonal design and epsilon dominance archiving strategy, Engineering Optimization, 2014 In Press. DOI: 10.1080/0305215X.2015.1012078. SCI 三区 IF = 1.07

10. Zhang, T., Andrews, J., Wang, R., Optimal Scheduling of Track Maintenance on a Railway Network. Quality and Reliability Engineering international, 29(2): 285-297, 2013. SCI 四区 IF = 1.19

11. Wu, G., Mallipeddi, R., Suganthan, P., Wang, R., Chen, H., Differential Evolution with Multi-Population Based Ensemble of Mutation Strategies. Information Sciences, Elsevier, 2015, Doi:10.1016/j.ins.2015.09.009. SCI 二区 IF = 4.04

12. Zhang Y., Zhang T., Wang, R., Liu, Y. J., Guo B., Optimal operation of a smart residential microgrid based on model predictive control by considering uncertainties and storage impacts，Solar Energy 122 (2015) 1052–1065，DOI:10.1016/j.solener.2015.10.027, Elsevier. SCI 二区 IF = 3. 469

• Referred Conference Papers      

       

        1. Wang, R., Purshouse, R. C., Fleming, P. J., Local preference-inspired co-evolutionary algorithms, in: GECCO 2012: Proceedings of the Genetic and Evolutionary Computation Conference, ACM, Philadelphia, USA, 2012, pp. 513-520. .(EI：20123315330809)

  
  2. Wang, R., Purshouse, R. C., Fleming, P. J., Preference-inspired co-evolutionary algorithm using adaptively generated goal vectors, Evolutionary Computation (CEC), 2013 IEEE Congress on. IEEE, Cancun, Mexico, 2013: 916- 923. (EI：20123315330809)

  
  3. Wang, R., Purshouse, R. C., Fleming, P. J., On Finding Well-Spread Pareto Optimal Solutions by Preference-inspired Co-evolutionary Algorithm, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO 2013. ACM, Amsterdam, The Netherlands, 2013: 695-702. (EI：20133616687419)

  
  4. Wang, R., Purshouse, R. C., Fleming, P. J., Preference-inspired co-evolutionary algorithms using weight vectors for many-objective optimisation, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO 2013, ACM, Amsterdam, The Netherlands, 2013, pp. 101-102. （EI: 20133516658192）

  
  5. Wang. R., Zhang T, and Guo B, An enhanced MOEA/D using uniform directions and a pre-organization procedure, Evolutionary Computation (CEC), 2013 IEEE Congress on. IEEE, 2013: 2390-2397. (EI： 20133416645346)

  
  6. Wang, R., Purshouse, R. C., Fleming, P. J., whatever works best for you-a new method for a priori and progressive multi-objective optimisation, in: Evolutionary Multi-Criterion Optimization, Springer, 2013, pp. 337-351. （EI： 20131416166484）

  
  7. Wang, R., Zhang, Q.F., Zhang, T., Pareto adaptive scalarising function for decomposition based algorithms in: Evolutionary Multi-Criterion Optimization, Springer, Lecture Notes in Computer Science Volume 9018, 2015, pp 248-262. （ISTP: 000361702100017 EI: 20151300677865）

  
  8. Purshouse, R. C., Deb, K., Mansor, M. M., Mostaghim, S., Wang, R., A Review of Hybrid Evolutionary Multiple Criteria Decision Making Methods , In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 1147-1154). IEEE, Beijing, China. (ISTP: 000356684601072 EI: 20144600182916)

  
  9. Shi Z.C., Wang R., Zhang T. PICEA-g using an enhanced fitness assignment method, 2014 IEEE symposium on Computational Intelligence in Multi-Criteria Decision-Making (MCDM), Orlando, FL, USA, IEEE, pp. 72-77. (EI: 20150700522992)

  
  10. Wang, R., Zhang F.X., Zhang, T., Multi-objective optimal design of hybrid renewable energy systems using evolutionary algorithms. The 2015 11th International Conference on Natural Computation (ICNC'15), Zhang JiaJie, China, 2015, August. (EI会议 待检索)

  
  11. Shi ZC, Wang R., Zhang T, Zhang Y. Optimal design of hybrid renewable energy systems using multi-objective evolutionary algorithm. Ⅲ. European Conference on Renewable Energy Systems, Turkey, 2015. (ISBN: 978-605-86911-3-1). (EI会议 待检索)

  
  12. Zhang Y, Zhang T, Guo B, Wang R., Shi ZC. MPC based energy storage control strategy for smart distribution system under high renewable energy penetration[C]. Ⅲ. European Conference on Renewable Energy Systems, Turkey, 2015. (ISBN: 978-605-86911-3-1). (EI会议 待检索)

  
  13. Zhang, X. Y., Wang, R., Liao, T., Zhang, T., Zha, Y. Short-term forecasting of wind power generation Based on the Similar Day and Elman Neural Network. 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa. 2015. (EI会议 待检索)

  
  14. Zhang, Y., Wang, R., Zhang, T., Liao, T., Liu Y., Guo, B., Stochastic model predictive control based economic dispatch for hybrid energy system including wind and energy storage devices. 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa. 2015. (EI会议 待检索)

  
  15. Wang, R., Zhang, T., Multi-objective optimal design of hybrid renewable energy systems using MOEA/D, international Conference on Renewable Energy Research and Applications, IEEE, USA,. 2014, pp. 161-167.

  
  16. 明梦君，王锐，张涛，基于方差值调节差分进化算法的可再生能源规划研究，2015年湖南省系统工程与管理学会学术会议。一等优秀论文