MDO Lab - Research Resources

 

Codes


These are the codes for various algorithms developed by MDO group. The codes are free to download and use for non-commercial purposes. In case you do not have access to any of the papers linked below, please do not hesitate to contact us for a copy.

Multifidelity Optimization


Evolutionary Bilevel Optimization


Multi-concept Optimization


Multimodal Multiobjective Optimization


Genetic Programming

Evolutionary Multi/many-objective Optimization

Surrogate-assisted Optimization
  • A generalized surrogate-assisted evolutionary algorithm for expensive multi-objective optimization (GSAEA) (Generalized version of SASSEA; Can be run in generational or steady-state mode by setting number of infills). Codes | Forthcoming (CEC 2023)
  • A steady-state algorithm for solving expensive multi-objective optimization problems with non-parallelizable evaluations(SASSEA). Codes | Paper - IEEE TEVC 2022
  • Partial evaluation strategies for expensive evolutionary constrained optimization (SParEA). Codes | Paper - IEEE TEVC 2021
  • Adjusting normalization bounds to improve hypervolume based search for expensive multi-objective optimization Code | Paper - CAIS 2023
  • Investigating normalization bounds for hypervolume-based infill criterion for expensive multiobjective optimization Code | Paper - EMO 2021
  • Multiple Surrogate Assisted Decomposition Based Evolutionary Algorithm for Expensive Multi/Many-Objective Optimization (HSMEA). Code | Data | Paper - IEEE TEVC 2019
  • Bridging Kriging Believer and Expected Improvement Using Bump Hunting for Expensive Black-box Optimization Code | Paper - GECCO 2021
  • Multiple Spatially distributed Surrogate Assited Multi-objective Optimizer (SAMO). Code | Paper - ASME JMD 2016
  • SAMO with Improved Selection (SAMO-IS). Code | Paper - IEEE CEC 2016
  • Hybrid Surrogate based Algorithm for CEC 2014 competition benchmarks(HSBA). Code | Paper - IEEE CEC 2014

Constrained Optimization

Large-scale(variable) Optimization


Resources for current and potential higher degree research students with MDO group


These are the general guidelines, information and resources for students working within, or planning to work with the MDO group. HDR resources.