During the past two years, we have carried out intensive research on dynamic constrained optimization through evolutionary computation. The main work can be summarized as follows:
1) We have designed a suite of benchmark test functions for dynamic constrained optimization and proposed a novel algorithm to deal with dynamic constrained optimization problems.
2) We have studied how to enhance the search ability of evolutionary algorithms by a two-phase optimization mechanism and by the adaptive tuning of coordinate systems.
3) We have conducted a comparative study of constraint-handling techniques in evolutionary algorithms and provided some theoretic analyses on the advantages and disadvantages of differential constraint-handling techniques in different scenarios.
4) We have proposed new scalarizing functions in decomposition-based multiobjective evolutionary algorithms to handle multiple conflicting objective functions.
5) We have carried out real-world applications in automotive lightweight design and wind farm layout design.
Overall, we have published six papers in top International Journals and two papers in International Conferences. In addition, we submitted one high-quality journal paper. The following are the publications during the past two years:
1) Y. Wang, D.-Q. Yin, S. Yang, and G. Sun. Global and local surrogate-assisted differential evolution for expensive constrained optimization problems with inequality constraints. IEEE Transactions on Cybernetics, in press, 2018. DOI: 10.1109/TCYB.2018.2809430 (2017 ISI Impact Factor: 8.803)
2) Z.-Z. Liu, Y. Wang, S. Yang, and K. Tang. An adaptive framework to tune the coordinate systems in nature-inspired optimization algorithms. IEEE Transactions on Cybernetics, in press, 2018. DOI: 10.1109/TCYB.2018.2802912 (2017 ISI Impact Factor: 8.803)
3) Y. Wang, H. Liu, H. Long, Z. Zhang, and S. Yang. Differential evolution with a new encoding mechanism for optimizing wind farm layout. IEEE Transactions on Industrial Informatics, vol. 14, no. 3, pp. 1040-1054, 2018. (2017 ISI Impact Factor: 5.43)
4) S. Jiang, S. Yang, Y. Wang, and X. Liu. Scalarizing functions in decomposition-based multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, vol. 22, no. 2, pp. 296-313, 2018. (2017 ISI Impact Factor: 8.124)
5) W. Gong, Y. Wang, Z. Cai, and S. Yang. A weighted biobjective transformation technique for locating multiple optimal solutions of nonlinear equation systems. IEEE Transactions on Evolutionary Computation, vol. 21, no. 5, pp. 697-713, 2017. (2017 ISI Impact Factor: 8.124)
6) Y. Wang, B. Xu, G. Sun, and S. Yang. A two-phase differential evolution for uniform designs in constrained experimental domains. IEEE Transactions on Evolutionary Computation, vol. 21, no. 5, pp. 665-680, 2017. (2017 ISI Impact Factor: 8.124)
7) J.-P. Li, Y. Wang, S. Yang, and Z. Cai. A comparative study of constraint-handling techniques in evolutionary constrained multiobjective optimization, 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, pp. 4175-4182.
8) Z.-Z. Liu, Y. Wang, S. Yang, and Z. Cai. Differential evolution with a two-stage optimization mechanism for numerical optimization, 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016, pp. 3170-3177.
9) Y. Wang, J. Yu, S. Yang, and S. Jiang. Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons, IEEE Transactions on Systems, Man, and Cybernetics: Systems, submitted, 2018. (2017 ISI Impact Factor: 5.131)
