An Improved Differential Evolution for Constrained Optimization Problems
Liechao Zhang, Lin Shang
Available Online February 2018.
- https://doi.org/10.2991/csece-18.2018.89How to use a DOI?
- differential evolution; orthogonal design; simple diversity rules; hybrid adaptive-crossover-mutation; function optimization
- A fast and robust differential evolution based on orthogonal design (ODE) is proposed, and then it is used to solve constrained optimization problems. The ODE combines the conventional DE (CDE), which is simple and efficient, with the orthogonal design, which can exploit the optimum offspring. The ODE has some features. 1) It uses a robust crossover based on orthogonal design and an optimal offspring is generated with the constrained statistical optimal method. 2) To decrease the number of the orthogonal design and make the algorithm converge faster, decision variable fraction strategy is applied here. 3) It uses simple diversity rules to handle the constraints and maintain the diversity of the population; 4) A multi-parent hybrid adaptive-crossover-mutation operator based on the non-convex theory is proposed, which can enhance the non-convex search ability. 5) The ODE simplifies the scaling factor F of the CDE, which can reduce the parameters of the algorithm and make it easy to use for engineers. We execute the proposed algorithm to solve 13 benchmark functions with linear or/and nonlinear constraints. Through comparison with some state-of-the-art evolutionary algorithms, the experimental results demonstrate that the performance of the ODE outperforms other evolutionary algorithms in terms of the quality of the final solution and the stability; and its computational cost (measured by the average number of fitness function evaluations) is lower than the cost required by the other techniques compared.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Liechao Zhang AU - Lin Shang PY - 2018/02 DA - 2018/02 TI - An Improved Differential Evolution for Constrained Optimization Problems PB - Atlantis Press SP - 417 EP - 422 SN - 2352-538X UR - https://doi.org/10.2991/csece-18.2018.89 DO - https://doi.org/10.2991/csece-18.2018.89 ID - Zhang2018/02 ER -