Self-adaptive differential evolution hyper-heuristic with applications in process design

The paper presents a differential evolution (DE)-based hyper-heuristic algorithm suitable for the optimization of mixed-integer non-linear programming (MINLP) problems. The hyper-heuristic framework includes self-adaptive parameters, an ϵ-constrained method for handling constraints, and 18 DE variants as low-level heuristics. Using the proposed approach, we solved a set of classical test problems on process synthesis and design and compared the results with those of several state-of-the-art evolutionary algorithms. To verify the consistency of the proposed approach, the above-mentioned comparison was made with respect to the percentage of convergences to the global optimum (NRC) and the average number of objective function evaluations (NFE) over several trials. Thus, we found that the proposed methodology significantly improves performance in terms of NRC and NFE.