Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2019.1656097
An efficient hybrid differential evolutionary algorithm for zbilevel optimisation problems
Xing Bao
; College of Business Administration, Zhejiang University of Finance and Economics, Hangzhou, China
Titing Cui
; Applied and Computational Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden
Zhongliang Zheng
; College of Economic and Management, China Agricultural University, Beijing, China;
Haiyun Liu
; Department of Economics, School of Liberal Arts, Tulane University, New Orleans, LA, USA
Sažetak
Bilevel problems are widely used to describe the decision problems with hierarchical upper–lower-level structures in many economic fields. The bilevel optimisation problem (BLOP) is intrinsically NP-hard when its objectives and constraints are complex and the decision variables are large in scale at both levels. An efficient hybrid differential evolutionary algorithm for BLOP (HDEAB) is proposed where the optimal lower level value function mapping method, the differential evolutionary algorithm, k-near- est neighbours (KNN) and a nested local search are hybridised to improve the computational accuracy and efficiency. To show the performance of the HDEAB, numerical studies were conducted on SMD (Sinha, Maro and Deb) instances and an application example of optimising a venture capital staged-financing contract. The results demonstrate that the HDEAB outperforms the BLEAQ (bile- vel evolutionary algorithm based on quadratic approximations) greatly in solving the BLOPs with different scales
Ključne riječi
Bilevel optimisation problem; differential evolutionary algorithm; KNN; nested local search
Hrčak ID:
229625
URI
Datum izdavanja:
22.1.2019.
Posjeta: 1.054 *