Difference-genetic co-evolutionary algorithm for nonlinear mixed integer programming problems

In this paper, the difference genetic co-evolutionary algorithm (D-GCE) is proposed for the mixed integer programming problems. First, the mixed integer programming problem with constrains converted to unconstrained bi-objective optimization problems. Secondly, selection mechanism combines the Pareto dominance and superiority of feasible solution methods to choose the excellent individual as the next generation. Final, differential evolution algorithm and genetic algorithm handle the continuous part and discrete part, respectively. Numerical experiments on 24 test functions have shown that the new approach is efficient. The comparison results among the D-GCE and other evolutionary algorithms indicate that the proposed D-GCE algorithm is competitive with and in some cases superior to, other existing algorithms in terms of the quality, efficiency, convergence rate, and robustness of the final solution.