The single value in your attached graph represents the combination of two objectives. This position of the solution indicates the quality w.r.t objective 1 along the x-axis while th quality w.r.t objective 2 along the y-axis.

dear Umair Ayub ,

My question was why I'm getting only one value for objective 1 and 2 instead of multiple values usually given by such method.

Regards.

This behavior may be a bug in the evolution process that leads to obtain the Pareto set containing multiple identical candidate solutions.

Dear Łukasz Wieczorek ,

Thank you for the quick replay, what can be the solution in this case ?

Regards.

Dear Mahdi Abdeddaim,

there could be many reasons. I suggest the optimization process should be debugged generation-by-generation in order to observe the tendency of the further populations. For this purpose the population size may be limited to a few individuals.

You should take a closer look at:

- the initial population - please check that those candidate solutions are diverse, i.e., arbitrary generated,

- the selection operation - please check whether it produces pairs of different candidate solutions,

- the crossover operation - please check whether children are different from the parents,

- the mutation probability - I do not know the optimization problem in detail, however, sometimes the GA-based optimization process finds a local (or even global) minimum, hence all the further candidate solutions (in the following populations) are the same - in this case higher value of the mutation probability stimulates the further optimization process.

Eventually, if the aspects listed above are correct for the first generations, you can simply print all the individuals of populations in the whole optimization process and check (a) whether the quantity of individuals in populations are always the same - as the size of the initial population and (b) if there is no local/global minimum that results in the consecutive populations contain the same identical elements.