A hybrid multi-population metaheuristic applied to load-sharing optimization of gas compressor stations. (January 2022)

• Record Type: Journal Article
• Authors: Rodrigues, Leonardo R.
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Metaheuristic -- Multi-population -- Hybrid algorithms -- Symbiotic Organisms Search -- Crow Search Algorithm -- Gas compressor station; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107632
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, a hybrid population-based, nature-inspired metaheuristic algorithm is developed and applied to solve the load-sharing optimization problem, which is a common problem that appears in many industrial applications. The proposed algorithm is a hybrid multi-population metaheuristic that combines the diversification capability of the Crow Search Algorithm (CSA) and the intensification capability of the Symbiotic Organisms Search (SOS). It uses two subpopulations. One subpopulation evolves according to the CSA algorithm and is responsible for finding promising regions in the search space. The second subpopulation evolves according to the SOS algorithm and is responsible for performing a refined search in the promising regions to find the best solution for the problem. The performance of the proposed algorithm is compared with the performance of five competing algorithms, including the stand-alone versions of CSA and SOS. The results obtained show that the proposed CSA-SOS outperformed all the competing algorithms in all experiments. Graphical abstract: Highlights: A hybrid metaheuristic combining the best features of CSA and SOS is developed. Two populations evolve to implement the exploration and exploitation capabilities. CSA is used to explore the search space and to identify promising regions. SOS intensifies the search in the promising regions identified by CSA.