A novel and effective optimization algorithm for global optimization and its engineering applications: Turbulent Flow of Water-based Optimization (TFWO). (June 2020) - Ghasemi, Mojtaba; Davoudkhani, Iraj Faraji; Akbari, Ebrahim; Rahimnejad, Abolfazl; Ghavidel, Sahand; Li, Li
Abstract: In this study we present a new and effective grouping optimization algorithm (namely, the Turbulent Flow of Water-based Optimization (TFWO)), inspired from a nature search phenomenon, i.e. whirlpools created in turbulent flow of water, for global real-world optimization problems. In the proposed algorithm, the problem of selecting control parameters is eliminated, the convergence power is increased and the algorithm have a fixed structure. The proposed algorithm is used to find the global solutions of real-parameter benchmark functions with different dimensions. Besides, in order to further investigate the effectiveness of TFWO, it was used to solve various types of nonlinear Economic Load Dispatch (ELD) optimization problems in power systems and Reliability–RedundancyAllocation Optimization (RRAO) for the overspeed protection system of a gas turbine, as two real-world engineering optimization problems. The results of TFWO are compared with other algorithms, which provide evidence for efficient performance with superior solution quality of the proposed TFWO algorithm in solving a great range of real-parameter benchmark and real-world engineering problems. Also, the results prove the competitive performance and robustness of TFWO algorithm compared to other state-of-the-art optimization algorithms in this study. The source codes of the TFWO algorithm are publicly available at https://github.com/ebrahimakbary/TFWO . Highlights: A new variant of DE algorithm is proposed based on turbulent flow of water (TFWO). TFWO is tested on real-parameter benchmark functions with different dimensions. Also, TFWO is tested on Economic Load Dispatch as a real-world optimization problem. A Reliability–Redundancy Allocation Optimization was also solved by TFWO. The results prove superiority and robustness of TFWO compared to other algorithms.