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

Matin Rahnamay Naeini, Tiantian Yang, Mojtaba Sadegh, Amir AghaKouchak, Kuo lin Hsu, Soroosh Sorooshian, Qingyun Duan, Xiaohui Lei

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)215-235
Number of pages21
JournalEnvironmental Modelling and Software
Volume104
DOIs
StatePublished - Jun 2018

Keywords

  • Evolutionary Algorithm (EA)
  • Hybrid optimization
  • Hydropower
  • Reservoir operation
  • Shuffled Complex Evolution (SCE)

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