A Differential Evolution Algorithm with Q-Learning for Solving Engineering Design Problems
Damla Kızılay, M. Fatih Tasgetiren, Hande Öztop, Levent Kandiller, Ponnuthurai Nagaratnam Suganthan · 2020
In this paper, a differential evolution algorithm with Q-Learning (DE-QL) for solving engineering Design Problems (EDPs) is presented. As well known, the performance of a DE algorithm depends on the mutation strategy and its control parameters, namely, crossover and mutation rates. For this reason, the proposed DE-QL generates the trial population by using the QL method in such a way that the QL guides the selection of the mutation strategy amongst four distinct strategies as well as crossover and mutation rates from the Q table. The DE-QL algorithm is well equipped with the epsilon constraint handling method to balance the search between feasible regions and infeasible regions during the evolutionary process. Furthermore, a new mutation operator, namely DE/Best to current/l, is proposed in the DE-QL algorithm. In this paper, 57 EDPs provided in “Problem Definitions and Evaluation Criteria for the CEC 2020 Competition and Special Session on A Test-suite of Non-Convex Constrained optimization Problems from the Real-World and Some Baseline Results” are tested by the DE-QL. We provide our results in Appendixes and will be evaluated with other competitors in the competition.