Dear Prof., we have worked on triple objective engineering design problems in the past. I am including some of our works, hopefully this helps you:

Ganesan, T., Vasant, P. and Elamvazuthi, I., 2013. Hybrid neuro-swarm optimization approach for design of distributed generation power systems. Neural Computing and Applications, 23(1), pp.105-117.

Ganesan, T., Vasant, P. and Elamvazuthi, I., 2013. Normal-boundary intersection based parametric multi-objective optimization of green sand mould system. Journal of Manufacturing Systems, 32(1), pp.197-205.

Ganesan, T., Elamvazuthi, I. and Vasant, P., 2015. Multiobjective design optimization of a nano-CMOS voltage-controlled oscillator using game theoretic-differential evolution. Applied Soft Computing, 32, pp.293-299.

T Ganesan, I Elamvazuthi, 2016,A Multiobjective Approach for Resilience-based Plant Design Optimization, Quality Engineering.

Thank you.

For the solution of design tasks with multiple objectives the Tchbyshev Program   developed by Ralph Steuer  widely applied many investigators could be used . To appropriately formulate a design problem in terms of a optimization model with multiple objectives it is required to use an appropriate analysis and  synthesis engineering systems methodology. In the work Analysis and Synthesis of Engineering Systems, that you can download from my Researchgate page, a methodology developed by me is briefly described and used in the solution of many engineering design tasks, including those  with 3 and  much more ebjectives.

Greetings,

Jose Arzola

NSGA II introduced the crowding distance as the density estimator for multi-obj problems. Its first introduction was implemented for bi-obj problems, but many researches have been done in recent years to improve it, I just searched "crowding distance for triple objective" in google and I headed to this paper which has an interesting modified crowding distance. Furthermore, by a deeper search, more robust and citable results can be found surely (the link is attached).

But in my point of view, I recommend you to try other kinds of diversity estimators too and for the beginning, I suggest the MUGA paper which uses epsilon elimination considering both design variable and objective spaces. It has been used in many disciplines for example in vehicle suspension optimization design.

Jamali, Ali, Kazem Atashkari, and Nader Nariman-zadeh. Multi-objective Uniform-diversity Genetic Algorithm (MUGA). INTECH Open Access Publisher, 2008.

Jamali, A., M. Salehpour, and N. Nariman-zadeh. "Robust Pareto active suspension design for vehicle vibration model with probabilistic uncertain parameters." Multibody System Dynamics 30, no. 3 (2013): 265-285.

Regards.

https://www.lncc.br/pdf_consultar.php?idt_arquivo=7819&mostrar=1&teste=1

Thanks for the reply