A two-stage cooperative scatter search algorithm with multi-population hierarchical learning mechanism. (1st October 2022)

• Record Type: Journal Article
• Authors: Zhao, Fuqing; Zhou, Gang; Wang, Ling; Xu, Tianpeng; Zhu, Ningning; Jonrinaldi,
• Is Part Of: Expert systems with applications. Volume 203(2022)
• Publication Date: 2022-10-01
• Subjects: Scatter search -- Multi-population -- Hierarchical learning mechanism -- Interactive learning strategy -- Pattern search; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.117444
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel hierarchical learning scatter search algorithm (TCSSMH) is proposed. Multi-swarm hierarchical learning mechanism is introduced into SS to update Refset. A cooperative interactive learning mechanism is introduced to update individuals. A superior performance of the proposed approaches is proved in the experiments. Abstract: Scatter search (SS) is a population-based metaheuristic algorithm, which has been proved high efficiency and effective optimizer for complex continuous real value problems. A two-stage cooperative SS guided with the multi-population hierarchical learning mechanism (TCSSMH) to overcome the slow convergence speed of the original SS is proposed. Three strategies are applied to the original SS. Firstly, TCSSMH adopts an adaptive two-way selection search strategy based on the elite reference set ( RefSet ), which is elite-oriented and ensures the quality of the population. Secondly, the multi-group hierarchical learning mechanism is embedded in the updating process of the RefSet, and the population of the candidates is divided into three levels including excellent candidates, medium candidates, and inferior candidates according to the fitness value of the function. These three subpopulations cooperate to balance the exploration and exploitation ability of the algorithm in the process of evolution. Finally, each subpopulation adopts an interactive learning strategy to increase the diversity of the population and avoid premature convergence of solutions. The optimum of each subpopulation with high accuracy is obtained by the pattern search (PS) optimization. The stronger search ability and higher search efficiency of these additional proposed strategies are verified by extensive experiments. The TCSSMH algorithm is tested on the CEC2017 benchmark test suite and practical engineering problems. The experimental results show that the TCSSMH algorithm is superior to other state-of-the-art algorithms in global search ability and convergence on the benchmark problems.