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- W4381325895 abstract "Abstract The Whale Optimization Algorithm(WOA), a population-based meta-heuristic technique,which mimics the hunting behavior of humpback whales and has been successfully applied to solve many real-world problems. The standard WOA algorithm suffers from uneven population distribution and premature convergence. In this paper, a multiple strategy whale optimization algorithm (MSWOA) is proposed. Firstly, the population is initialized using the sobol sequence, local search around each individual is performed to improve the quality of the initial solution. Secondly, global search is implemented by introducing levy flight and improved pa factor. Thirdly, an adaptive weight strategy is proposed to improve the global exploration and local development capabilities of the algorithm. Finally, the genetic variation operator is introduced to enhance the ability of escaping from local optimum. To verify the performance of the proposed technique, MSWOA is compared with six state-of-the-art meta-heuristics on 23 benchmark functions and three engineering optimization problems. The experimental results show that MSWOA has better performance on most of benchmark functions and is competitive in solving engineering design problems." @default.
- W4381325895 created "2023-06-21" @default.
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- W4381325895 date "2023-06-21" @default.
- W4381325895 modified "2023-09-25" @default.
- W4381325895 title "Multi-Strategy Whale Optimization Algorithm for global optimization and engineering applications" @default.
- W4381325895 doi "https://doi.org/10.21203/rs.3.rs-3071467/v1" @default.
- W4381325895 hasPublicationYear "2023" @default.
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