研究领域

群智能优化、运筹学及其应用 （1）群智能优化算法模型研究与分析，包括遗传算法、粒子群算法、差分进化、模拟退火算法和免疫优化算法等； （2）非线性优化、约束和多目标优化的模型分析与算法求解； （3）群智能优化与数学模型和数学知识结合的混合算法研究； （4）运筹优化在通讯网络和社会计算中的应用；

近期论文

[1]赵新超, P.Y. Huang, T.T. Liu and X.M. Li, A Hybrid Clonal Selection Algorithm for Quality of Service-aware Web Service Selection Problem, International Journal of Innovative Computing, Information and Control, 8(12):8527-8544, 2012.
[2] 赵新超, B.Q. Song, P.Y. Huang et al., An Improved Discrete Immune Optimization Algorithm based on PSO for QoS-driven Web Service Composition, Applied Soft Computing, 12(8):2208-2216, 2012.
[3] Q.B. Sun, W.B. Wang, F.C. Yang, 赵新超, Towards service pool construction approach based on extended QoS model for pervasive environment, Information, 15(11B):5071-5086, 2012.
[4] 赵新超, Simulated annealing algorithm with adaptive neighborhood, Applied Soft Computing, 11 (2): 1827–1836, 2011.
[5] 赵新超, G.L. Liu, H.Q. Liu, G.S. Zhao, S.Z. Niu, A New Clonal Selection Immune Algorithm with Perturbation Guiding Search and Non-uniform Hypermutation,International Journal of Computational Intelligence Systems, 4(s):1-17, 2010.
[6] 赵新超, A perturbed particle swarm algorithm for numerical optimization,Applied Soft Computing,10(1):119-124, 2010.
[7] 王文彬，孙其博，赵新超，杨放春，基于非均衡变异离散粒子群算法的QoS全局最优Web服务选择方法，电子学报，38(12):2774-2779，2010.
[8] 赵新超, Advances on Protein Folding Simulations based on the lattice HP models with Natural Computing, Applied Soft Computing, 8(2):1029–1040, 2008.
[9] 赵新超, Gao X.S.,Affinity genetic algorithm, Journal of Heuristics, 13(2):133–150, 2007.
[10] 赵新超, Gao X.-S.,Hu Z.-C., Evolutionary programming based on non-uniform mutation, Applied Mathematics and Computation, 192(1): 1-11, 2007.
[11] 赵新超, Hao J.L., Exploration/exploitation tradeoff with cell-shift and heuristic crossover for evolutionary algorithms,Journal of Systems Science and Complexity,20(1): 66-74,2007.