Opossum: how does the multi-objective optimization work?

Hi to everyone,
I wanted to do a multi-objective optimization on Grasshopper, so I started using Opossum. It seems to work well, but I wonder how it works.
You can choose between five different algorithms (RBFMOpt, MACO (Ant Colony), MOEA/D, NSGA-II and NSPSO (Particle Swarm)), but what are the differences?
When I set different objectives (goals), does the algorithm try to optimize all of them the same way or does he have a privileged one (for example the number one)?
What do the parameters in the results window mean?
is there some sort of manual/documentation to consult in order to understand better how this plug-in work?

Thank you very much. Matteo.


Idk the Opossum. But for multi-objective algorithm, you could search the research paper on google (NSPSO-Non Sorting Particle Swarm Optimization for instance). Basically, if you have one objective, i-th iteration, you could compare the fitness ( objective value ) of each particle and store the best one and then i+1-th iteration, you could use the best one to update other particle to find the optimal value. Howerver, if you have two or more objective, you cannot compare which one is better, so they use non-sorting algorithm ( pareto approach ) to list all particle which cannot be compared. The algorithm is quite complex, so I suggest you should search the paper to know more.