职称：副教授（博士）
邮箱：jiangqiaoyong@xaut.edu.cn
主讲《人工智能导论》、《计算智能》、《Python程序设计》等课程。
1.“中国高校计算机大赛－团体程序设计天梯赛”（全国二等奖2项，省一等奖1项，省二等奖2项）
2.“中国大学生计算机设计大赛西北赛区”（省二等奖1项）
1.“在线学习适应度地形特征的高效差分演化算法研究及应用”（国家级）
2.“面向多模态多目标优化的多任务概率密度比建模与协同演化机理研究”（国家级）
3.“基于统计分析理论的有毒云团运动预测研究”（国家级）
4.“基于基因表达和头脑风暴的高维多目标进化算法研究”（省部级）
5.“国网陕西电科院综合功率预测系统建设-综合功率预测系统软硬件开发”（校企合作）
1.“基于大规模数据的复杂网络图聚类关键技术及应用”（陕西省计算机学会科技进步二等奖）
2.“复杂网络环境中移动对象位置服务与优化理论及应用”（陕西省教育厅科技进步二等奖）
主要从事演化优化与知识发现的算法设计与应用等方面的研究，着重在多目标演化优化、演化算法的自动设计、轨道交通的调度与优化、联邦学习与优化等方向展开研究。发表的相关论文如下：
1.Self-adaptive resource allocation based on reinforcement learning for multi-concept multi-objective optimization[J]. Appl. Soft Comput.
193:114851(2026).
2.排斥机制驱动的不平衡多模态多目标进化算法[J]. 控制与决策,
DOI:10.13195/j.kzyjc.2025.1177(2026).
3.An evolutionary multitasking algorithm driven by two-stage
adaptive transfer learning models[C]. NTCI 2025(2026).
4.A particle swarm optimizer with scoring-based multi-neighborhood
selection mechanism for multimodal multi-objective problems[J].
Clust. Comput. 28(7): 435 (2025).
5.Multitasking optimization algorithm based on a multitransfer
strategy[J]. Memetic Comput. 17(2): 17 (2025).
6.An adaptive transfer strategy guided by reference vectors for many-objective optimization problems[J]. J. Supercomput. 81(1): 80
(2025).
7. Enhancing Knowledge Transfer in the EMTO with Manifold Learning
and Reinforcement Learning[C]. CEC 2025: 1-8.
7.MOEA/D with customized replacement neighborhood and dynamic resource allocation for solving 3L-SDHVRP[J]. Swarm Evol. Comput.
85:101463 (2024).
9.Multiple search operators selection by adaptive probability
allocation for fast convergent multitask optimization[J]. J. Supercomput. 80(11): 16046-16092 (2024).
10.Two Stages multi-operator hybrid constraint handling strategy for
CMTOPs[C]. ICIC (1) 2024: 57-69.
11.Multipopulation-based multi-tasking evolutionary algorithm[J].
Appl. Intell. 53(4): 4624-4647 (2023).
12.A regularity model-based multi-objective estimation of distribution memetic algorithm with auto-controllable population diversity[J].
Memetic Comput. 15(1): 45-70 (2023).
13.Improved adaptive coding learning for artificial bee colony
algorithms[J]. Appl. Intell. 52(7): 7271-7319 (2022).
14.A self-adaptive single-objective multitasking optimization
algorithm[C]. BIC-TA 2022: 117-130.
15.Differential evolution algorithm with multi-population cooperation and multi-strategy integration[J]. Neurocomputing, 421: 285-302
(2021).
16. An improved sine-cosine algorithm with dynamic selection
pressure[J]. J. Comput. Sci. 55: 101477 (2021).
17. Analysis of multitasking evolutionary algorithms under the order of solution variables[J]. Complex. 4609489:18 (2020).
18. An adaptive encoding learning for artificial bee colony algorithms
[J]. J. Comput. Sci. 30: 11-27 (2019).
19. Dynamic reference vectors and biased crossover use for inverse model based evolutionary multi-objective optimization with irregular
Pareto fronts[J]. Appl. Intell. 48(9): 3116-3142 (2018).
20.A novel cuckoo search algorithm with multiple update rules[J].
Appl. Intell. 48(11): 4192-4211 (2018).
21.ARAe-SOM+BCO: An enhanced artificial raindrop algorithm using self-organizing map and binomial crossover operator[J].
Neurocomputing, 275: 2716-2739 (2018).
22.An efficient multi-objective artificial raindrop algorithm and its application to dynamic optimization problems in chemical processes[J].
Appl. Soft Comput. 58: 354-377 (2017).
23.Multi-objective differential evolution with dynamic covariance matrix learning for multi-objective optimization problems with variable
linkages[J]. Knowl. Based Syst. 121: 111-128 (2017).
24.The performance comparison of a new version of artificial raindrop algorithm on global numerical optimization[J]. Neurocomputing, 179:
1-25 (2016).
25. MOEA/D-ARA+SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and
simulated binary crossover[J]. Knowl. Based Syst. 107: 197-218 (2016).
26. Parameter identification of chaotic systems using artificial raindrop algorithm[J]. J. Comput. Sci. 8: 20-31 (2015).
