Improved self-adaptive constrained differential evolution algorithm

In order to solve constrained optimization problems, a novel differential evolution algorithm is proposed. Firstly, the algorithm uses two mutation strategies to balance local search ability and global search ability. Secondly, the new population is selected based on the feasibility-based rule, and poor individuals in the new population are replaced by the substitution strategy. Then, a mutation mechanism is used to mutate the worst individual to maintain the diversity of the population. Finally, the adaptive parameter control mechanism is introduced to enhance the robustness and adaptability of the algorithm. In order to verify the effectiveness of this algorithm, 10 benchmark constraint optimization problems and 10 engineering constraint optimization problems are tested. Experimental results show that the algorithm has high precision, fast convergence speed, and strong robustness.