But , in my case, optimality is very large even though the objective fval is converge (please check img.)
Your plots are uninterpretable to many in the forum (and definitely to me), because they are not labelled in English. A few thoughts I can offer, however:
(1) It may be that fval is converging, but it is converging to something non-optimal. That could explain why the optimality measure is large, and is especially likely if fmincon is telling you that it cannot find even a feasible point.
(2) It is important to keep in mind that the optimality measure is not a normalized quantity and therefore you often can't be sure what to consider "very large" or "very small". For example, the point x0=1e-6 is quite close to the minima of both these functions: f(x)=x.^2 and g(x)=1e20*x^2. However, the gradients and the value of g(x0) are quite a bit larger than for f(x0).
Is it ok if I rerun to the last value even if exitflag didn't get 1?
I wouldn't have a lot of faith in it. It would be better to pursue improved initial guess strategies. If it is very hard to devise a good initial guess, it often means that the problem is infeasible and there is just no point trying to solve it.
If I don't get an exitflag of 1 through sqp and rerun it doesn't work, is this a good approach to get the result using a nonderivative method instead of the sqp algorithm?
Questions like that are not answerable without seeing the objective function and constraints.
