عنوان

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

شماره

LA6b9364md

عنوان اصلي

Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework

نام عام مواد

[Article]

نام نخستين پديدآور

Rahnamay Naeini, M; Yang, T; Sadegh, M; AghaKouchak, A; Hsu, KL; Sorooshian, S; Duan, Q; Lei, X

متن يادداشت

© 2018 Elsevier Ltd 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.

تاريخ نشر

2018

عنوان

UC Irvine

نام الکترونيکي

نوع ماده

[Article]

کد کاربرگه

275578

سطح دسترسي

a

تكميل شده

Y