The multi-objective optimization problem in my case is non-linear as it consists of three objective function of which two are nonlinear function and the third is a linear function. Lets say objective 1 is as given below:
Objective 1: Minimize $f_1(X_1,X_2)=230.54X_1^2+305X_2^2+554.56X_1X_2+121.59X_1+112.9X_2$
Constraint 1: $ 0 \le X_1 \le 1.5$
Constraint 2: $ 0 \le X_2 \le 1$
Constraint 3: $ X_1 + X_2 \le 30$
Here, $X_1$ and $X_2$ are decision variable and are real numbers.
To determine convexity of the above function, the eigenvalues of its Hessian matrix is examined.
For this case, I get both positive and negative eigenvalue. The eigenvalues are 1306.5 and -235.4. Hence, it is a non-convex optimization problem. For this case, how can classify this problem as NP-Hard and use NSGA-II to solve the problem?