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Dhish Kumar Saxena Associate Professor dhishfme[at] Website
Areas of Interest
  • Optimization, Evolutionary Multi-objective Optimization, Multi-Criteria Decision Making, Big Data Analytics
Professional Background
April 2016PresentAssociate ProfessorDepartment of Mechanical & Industrial Engineering, IIT Roorkee
Nov. 2012April 2016Assistant ProfessorDepartment of Mechanical & Industrial Engineering, IIT Roorkee
April 2012August2012Liverhume Research FellowDepartment of Computer Science, Bath University, UK
May 2008April 2012Academic FellowManufacturing Department, Cranfield University, UK
Honors and Awards
MCDM Doctoral Award Finalist (one of the top 3 Ph.Ds internationally during 5 years period (2007-11)Cranfield University2011
Educational Details
PhDEvolutionary Many-objective OptimizationIIT Kanpur2008
Administrative Background
2016-Associate EditorElsevier's Swarm and Evolutionary Computation
Aug. 2014May, 2017Chief Warden KIHIIT Roorkee
Jan 2015May 2017Member: Guest House Advisory CommitteeIIT Roorkee
July 2015May 2017Member: Department Purchase Committee, under DOSW set-upIIT Roorkee
July 2015May 2017Co-ordinator Tinkering LabIIT Roorkee
Sponsored Research Projects
TopicFunding AgencyYear
INNOVIZATION: Discovery of Innovative Knowledge through Optimization and Machine LearningMHRD, MSU (USA), GE (105.64 Lakhs)2019
A Systems Approach towards Data Mining and Prediction in Airlines operations [PI]DeiTY-NWO-GE (483,000 Euro)2015
Decomposition Based Multiobjective Evolutionary Computation [Overseas-CI]NSF, China (800,000 RMB)2015
Multi-objective Optimization of Composite Aircraft Wing.Airbus, UK2009
Tinkering Laboratory [Co-ordinator]MHRD (5.1 crore INR: 770,000 USD)2015
Many-objective Optimization: A way forwardHewlett Packard, UK2008
  • IEEE, Member
  • Elsevier: Swarm and Evolutionary Computation Journal, Associate Editor
Projects and Thesis Supervised
Title of ProjectNames of Students
Evolutionary Multi-objective Optimisation from a System Design Perspective.Alessandro Rubino
Optimisation of Composite Aircraft WingBenjamin Bruner
Many-objective optimization: A way forward.Wu Qin
Weighted Diversity Measure to Improve Convergence in a Class of Many-objective Optimization ProblemsHimansu Sekhar Dash
Feature based Optimal Sensor Position for Fault Diagnosis in Rolling Element BearingPraveen Nagesh
Application of Axiomatic Design Principles for Weight Optimization of Automotive ChassisIshwar Keshav Yanganti
PHDs Supervised
TopicScholar NameStatus of PHDRegistration Year
INNOVIZATION: Discovery of Innovative Knowledge through Optimization and Machine LearningSukrit MittalO2018
Airline Scheduling under UncertaintyDivyam AgarwalO2015
Feature construction by means of pattern mining on Big Data for Airline OperationsSarang KapoorO2015
Machine Learning based Decision Support for a Class of Many-objective Optimization ProblemsJoao A DuroA2009
Participation in short term courses
Couse NameSponsored ByDate
Designed & Conducted: Multidisciplinary Optimization: From Theory to PracticeCranfield University & EnginSoft, UK2010&2011
National International Collaboration
Optimisation of Composite Aircraft Wing.Airbus, UKPG
Many-objective Optimization: A Way ForwardHewlett Packard, UKPG
Refereed Journal Papers

[1] 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.

[2] Timing the Decision Support for Real-World Many-Objective Optimization Problems; J. A Duro, D. K. Saxena; Evolutionary Multi-Criterion Optimization, 191-205, 2017.

[3] 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

[4] 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.

[5] 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, 2012, 99, 1-23. Code

[6] An Evolutionary Multi-objective Framework for Business Process Optimization; K.Vergidis, D.K.Saxena and A.Tiwari; Applied Soft Computing, 2012, 2638-2653.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] 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] 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.

[2] 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.

[3] 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.

[4] 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,  ISBN:978-1-4244-1339-3, 919-926.

[5] 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"

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 43 6 University of - Cranfield,  Cambridge, & Leeds, UK.
3 Blueprint for future service information 2009 37 5 University of - Leeds, Cranfield,  & Cambridge, UK.

 Industrial case studies

2009 30 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.
  • Dhish obtained his Ph.D in Evolutionary Many-objective Optimization (2008), under the supervision of Shanti Swaroop Bhatnagar Awardee Prof. Kalyanmoy Deb, IIT Kanpur. In MCDM Conference, Finland, 2011, Dhish's Ph.d was adjudged as one of the three most impactful Ph.Ds in the world, during 2007-11, in the area of Evolutionary Multi-objective Optimization and Multi-criterion Decision Making. Dhish brings on board his work-experience in the United Kingdom, for almost half-a-decade, where he worked with universities like Cranfield and Bath, in collaboration with companies like British Aerospace Systems, Hewlett Packard, and Airbus. The focus of his research has been two fold. At a fundamental level, his research has focused on facilitating a better understanding of highly constrained practical optimization problems, characterized by high degree of non-linearity and several (many) conflicting objectives. In that, machine learning techniques have been integrated with evolutionary algorithms to rank the objectives and also the constraints by order of their importance, to facilitate a decision support for a given problem. At the applied level, his research focus has been on demonstrating the utility of the self-developed tools and techniques on a wide range of real-world: engineering design, business-process, and multi-disciplinary optimization & multi-criterion decision making problems.