Indian Patent Filed: A. Agarwal, K. Krishnan, D. Kumar , D. K. Saxena, A Wireless Edge Computing based Adaptive Traffic Control System with Real-time Vehicle Tracking and Cloud Integration, Application No. 202311079549, dated 23/11/2023
International Patent Filed: D. Aggarwal, D.K. Saxena, T. Bäck, M. Emmerich, Crew Optimization, Netherlands Patent Application N2025010, Feb. 2020
[1] Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning; A. Agarwal, D. Sahu, R. Mohata, K. Jeengar, A. Nautiyal and D. K. Saxena; Expert Systems with Applications, 2024 (https://doi.org/10.1016/j.eswa.2024.124416)
[2] Unified Innovized Progress Operator for Performance Enhancement in Evolutionary Multi- and Many- objective Optimization; S. Mittal, D. K. Saxena*, K. Deb; E. Goodman, IEEE Transactions on Evolutionary Computation, 2023 (https://ieeexplore.ieee.org/document/10269130)
[3] Real-world airline crew pairing optimization: customized genetic algorithm versus column generation method; D. Aggarwal, D. K. Saxena*, T. Bäck, M. Emmerich; Evolutionary Multi-Criterion Optimization; Vol 13970, 2023, 518-531
[4] Investigating Innovized Progress Operators with Different Machine Learning Methods; D. Bhasin, S. Swami, S. Sharma, S. Sah, D. K. Saxena*, K. Deb; Evolutionary Multi-Criterion Optimization, 2023, Vol 13970, 134-146
[5] Enhanced Innovized Progress Operator for Evolutionary Multi-and Many-objective Optimization, S. Mittal, D. K. Saxena*, K. Deb and E. D. Goodman, IEEE Transactions on Evolutionary Computation, 2022, doi: 10.1109/TEVC.2021.3131952.
[6] A Localized High-Fidelity-Dominance based Many-Objective Evolutionary Algorithm, D. K. Saxena*, S. Mittal, S. Kapoor, and K. Deb, IEEE Transactions on Evolutionary Computation, 2022 (https://ieeexplore.ieee.org/abstract/document/9814856)
[7] A Learning-based Innovized Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization, S. Mittal, D.K. Saxena*, K. Deb, and E.D. Goodman; ACM Transactions on Evolutionary Learning and Optimization, 2022, Volume 2, Issue 1, 1-29 (https://doi.org/10.1145/3474059)
[8] Online summarization of dynamic graphs using subjective interestingness for sequential data, S. Kapoor, D. K. Saxena, and M. van Leeuwen, Data Mining and Knowledge Discovery, 35, 88–126, 2021, https://doi.org/10.1007/s10618-020-00714-8
[9] Embedding a Repair Operator in Evolutionary Single and Multi-objective Algorithms - An Exploitation-Exploration Perspective; K. Deb, S. Mittal, D. K. Saxena* and E. D. Goodman; Evolutionary Multi-Criterion Optimization, 89-101, 2021
[10] Discovering Subjectively Interesting Multigraph Patterns, S. Kapoor, D.K. Saxena and M. van Leeuwen;Machine Learning, Vol 109, 2020: https://doi.org/
[11] A new replica placement strategy based on multi-objective optimisation for HDFS; Y. Li, M. Tian, Y. Wang, Q. Zhang, D. K. Saxena, and L. Jiao; International Journal of Bio-Inspired Computation, 16(1), 2020, 13-22
[12] On Timing the Nadir-Point Estimation and/or Termination of Reference-Based Multi- and Many-objective Evolutionary Algorithms; D. K. Saxena* and Sarang Kapoor; Evolutionary Multi-Criterion Optimization, 191-202, 2019.
[13] Timing the Decision Support for Real-World Many-Objective Optimization Problems; J. A Duro, D. K. Saxena*; Evolutionary Multi-Criterion Optimization, 191-205, 2017.
[14] Entropy based Termination Criterion for Multiobjective Evolutionary Optimisation; D. K. Saxena*, Arnab Sinha, J. A. Duro and Q. Zhang; IEEE Transactions on Evolutionary Computation, 20 (4), 485-498, 2016 Code
[15] Machine learning based decision support for many-objective optimization problems; J.A.Duro, D. K.Saxena*, K.Deb and Q.Zhang; Neurocomputing, Volume 146, Pages 30–47. http://www.sciencedirect.com/science/article/pii/S0925231214008753
[16] Objective Reduction in Many-objective Optimization: Linear and Nonlinear Algorithms; D. K.Saxena*, J.A.Duro, A.Tiwari, K.Deb and Q.Zhang; IEEE Transactions on Evolutionary Computation, 2013, 17(1), 77-99. Code
[17] An Evolutionary Multi-objective Framework for Business Process Optimization; K.Vergidis, D.K.Saxena* and A.Tiwari; Applied Soft Computing, 2012, 2638-2653.
[18] Identifying the Redundant and Ranking the Critical Constraints in Practical Optimization Problems; D.K.Saxena*, A.Rubino, J.A.Duro and A.Tiwari; Engineering Optimization, 2012, 1-23.
[19] Using Objective Reduction and Interactive Procedure to Handle Many-objective optimization Problems; A.Sinha, D.K.Saxena*, K.Deb and A.Tiwari, Applied Soft Computing, 2013, 3(1), 415-427.
[20] Framework for Many-objective Test Problems with both Simple and Complicated Pareto-set Shapes; D.K.Saxena*, Q.Zhang, J.A.Duro and A.Tiwari; Evolutionary Multi-Criterion optimization, 2011, 197-211.
[21] On Handling a Large Number of Objectives A Posteriori and During Optimization; D.Brockhoff, D.K.Saxena*, K.Deb and E.Zitzler; Multi-objective Problem Solving from Nature, 2008, 4, 377-403.
[22] Non-linear Dimensionality Reduction Procedures for certain Large-dimensional Multi-objective Optimization Problems: Employing Correntropy and a Novel Maximum Variance Unfolding; D.K.Saxena* and K.Deb; Evolutionary Multi-Criterion Optimization, 2007, 772-787.
Refereed Conference Papers
[1] A Generic and Computationally Efficient Automated Innovization Method for Power-Law Design Rules; K. Garg, A. Mukherjee, S. Mittal, D. K. Saxena and K. Deb; Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. ACM, New York, NY, USA: https://doi.org/10.1145/
[2] Learning based Multi-objective Optimization Through ANN-Assisted Online Innovization; S. Mittal, D. K. Saxena and K. Deb; In Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. ACM, New York, NY, USA: https://doi.org/10.1145/
[3] A Unified Automated Innovization Framework Using Threshold-based Clustering; S. Mittal, D. K. Saxena and K. Deb; Proceedings of Congress on Evolutionary Computation (CEC-2020), Piscataway, NJ: IEEE Press.
[4] Innovative Design of Hydraulic Actuation System for Operator Fatigue Reduction and Its Optimization; S. Mittal, D. Aggarwal and D. K. Saxena; Advances in Multidisciplinary Analysis and Optimization, Lecture Notes in Mechanical Engineering, Springer, 2020.
[5] On large-scale airline crew pairing generation; D. Aggarwal, D. K. Saxena, M. Emmerich, and S. Paulose; In IEEE Symposium Series on Computational Intelligence (SSCI), November 2018, 593-600.
[6] Interdependence and Integration among Components of the Airline Scheduling Process, D. Aggarwal, D. K. Saxena, and M. Emmerich; in 21st World Conference of the Air Transport Research Society (ATRS), Antwerp, Belgium, July 2017. [PDF available at http://www.optimization-online.org/DB HTML/2020/05/7774.html]
[7] Service Information in the Provision of Support Service Solutions: A State-of-the-art Review; S. Kundu, A. McKay, R. Cuthbert, D. McFarlane, D. K. Saxena, A. Tiwari and P. Johnson; CIRP Industrial Product-Service Systems; Cranfield, U.K, 2009, ISBN: 978-0-9557436-5-8, 100-106.
[8] Constrained many-objective optimization: A way forward; D. K. Saxena, T. Ray, K. Deb and A. Tiwari; IEEE Congress on Evolutionary Computation, Trondheim, Norway, 2009, ISBN:978-1-4244-2958-5, 545-552.
[9] Dimensionality Reduction of Objectives and Constraints in multi-objective optimization problems: A system design perspective; D. K. Saxena and K. Deb; IEEE Congress on Evolutionary Computation, Hongkong, 2008, ISBN:978-1-4244-1822-0, 3204-3211.
[10] Trading on infeasibility by exploiting constraint’s criticality through multi-objectivization: A system design perspective; D. K. Saxena and K. Deb; IEEE Congress on Evolutionary Computation, Singapore, 2007, ISBN:978-1-4244-1339-3, 919-926.
[11] Searching for Pareto-optimal Solutions through Dimensionality Reduction for Certain Large-dimensional Multi-Objective Optimization Problems; K. Deb and D.K.Saxena; IEEE Congress on Evolutionary Computation, Vancouvar, Canada, 2006, IEEE: 0-7803-9487-9, 3353-3360.
Deliverables to "British Aerospace Systems & Engineering and Physical Sciences Research Council, UK"
for the project: "S4T : Support Service Solutions: Strategy and Transition"
| Sr No |
Deliverable | Year | Pages | Co-authors | |
| No. | Affiliation | ||||
| 1 | Current state of service information | 2008 | 31 | 5 | University of - Leeds, Cranfield, & Cambridge, UK. |
| 2 | Service information requirements | 2009 | 125 | 6 | University of - Cranfield, Cambridge, & Leeds, UK. |
| 3 | Blueprint for future service information | 2009 | 55 | 5 | University of - Leeds, Cranfield, & Cambridge, UK. |
| 4 |
Industrial case studies |
2009 | 38 | 5 | University of - Cranfield, Cambridge, & Leeds, UK. |
| 5 | A roadmap for the transition to future service information solutions | 009 | 11 | 10 | University of - Cambridge, Leeds, Cranfield, & BAES, UK. |
Technical Reports
[2020]
[1] Aggarwal, D., Saxena, D.K., Bäck, T., Emmerich, M. (March, 2020). AirCROP: Airline Crew Pairing Optimizer for Complex Flight Networks Involving Multiple Crew Bases & Billion-Plus Variables. EADAL Report Number 2020001. [pdf] NEW
[2] Aggarwal, D., Saxena, D.K., Bäck, T., Emmerich, M. (March, 2020). On Initializing Airline Crew Pairing Optimization for Large-scale Complex Flight Networks. EADAL Report Number 2020002. [pdf] NEW
[2019]
[1] Aggarwal, D., Saxena, D.K., Bäck, T., Emmerich, M. (July, 2019). Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method. EADAL Report Number 2019001. [pdf]