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- W4285259697 abstract "Jellyfish search optimizer (JSO) and Sine–cosine algorithm (SCA) are two metaheuristic methods having different search operators. The paper proposes a hybrid metaheuristic that combines the features of both the algorithms and develops a hybrid algorithm that outperforms both of its parents. Specifically, it first uses opposition-based learning in population initialization and then introduces a modified position update operator of SCA in the original structure of JSO. The proposed metaheuristic has been tested on a set of benchmark functions and to find the minimum weight design of the transmission tower. The proposed algorithm outperforms JSO and SCA for benchmark functions as well as the transmission tower problem." @default.
- W4285259697 created "2022-07-14" @default.
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- W4285259697 date "2022-01-01" @default.
- W4285259697 modified "2023-10-17" @default.
- W4285259697 title "A Hybrid Metaheuristic for Transmission Tower Design Optimization" @default.
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- W4285259697 doi "https://doi.org/10.1007/978-981-19-0707-4_77" @default.
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