TY - JOUR
T1 - Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework
AU - Rahnamay Naeini, Matin
AU - Yang, Tiantian
AU - Sadegh, Mojtaba
AU - AghaKouchak, Amir
AU - Hsu, Kuo lin
AU - Sorooshian, Soroosh
AU - Duan, Qingyun
AU - Lei, Xiaohui
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/6
Y1 - 2018/6
N2 - Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA.
AB - Simplicity and flexibility of meta-heuristic optimization algorithms have attracted lots of attention in the field of optimization. Different optimization methods, however, hold algorithm-specific strengths and limitations, and selecting the best-performing algorithm for a specific problem is a tedious task. We introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme. SC-SAHEL explores performance of different EAs, such as the capability to escape local attractions, speed, convergence, etc., during population evolution as each individual EA suits differently to various response surfaces. The SC-SAHEL algorithm is benchmarked over 29 conceptual test functions, and a real-world hydropower reservoir model case study. Results show that the hybrid SC-SAHEL algorithm is rigorous and effective in finding global optimum for a majority of test cases, and that it is computationally efficient in comparison to algorithms with individual EA.
KW - Evolutionary Algorithm (EA)
KW - Hybrid optimization
KW - Hydropower
KW - Reservoir operation
KW - Shuffled Complex Evolution (SCE)
UR - http://www.scopus.com/inward/record.url?scp=85044752014&partnerID=8YFLogxK
U2 - 10.1016/j.envsoft.2018.03.019
DO - 10.1016/j.envsoft.2018.03.019
M3 - Article
AN - SCOPUS:85044752014
SN - 1364-8152
VL - 104
SP - 215
EP - 235
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
ER -