Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL) optimization framework. (June 2018)

• Record Type: Journal Article
• Authors: Rahnamay Naeini, Matin; Yang, Tiantian; Sadegh, Mojtaba; AghaKouchak, Amir; Hsu, Kuo-lin; Sorooshian, Soroosh; Duan, Qingyun; Lei, Xiaohui
• Is Part Of: Environmental modelling & software. Volume 104(2018)
• Page Start: 215
• Page End: 235
• Publication Date: 2018-06
• Subjects: Shuffled Complex Evolution (SCE) -- Hybrid optimization -- Evolutionary Algorithm (EA) -- Reservoir operation -- Hydropower; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.03.019
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: A new self-adaptive hybrid evolution algorithm, entitled SC-SAHEL is proposed. The newly developed framework is based on the Shuffled Complex Evolution algorithm. The SC-SAHEL algorithm utilizes multiple Evolutionary Algorithms (EAs) as the search cores. SC-SAHEL selects the best performing EA during the evolution process. The SC-SAHEL algorithm also reveals the performance of EAs at each optimization phase.