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- W2898096256 abstract "Spider Monkey Optimisation (SMO) is a new addition within the arena of nature-inspired algorithms. It is a recent Swarm Intelligence (SI) based algorithm, that models the food foraging behavior of a group of spider monkeys that mimic the Fission-Fusion Social System (FFSS) behavior. The SMO has been proven to be competitory and it balances the capabilities; exploitation and exploration efficiently. This article presents a significant variant of SMO, namely Spider Monkey Optimization with Enhanced Learning (SMOEL). In the proposed strategy, to increase the exploitation capability of SMO, an enhanced learning mechanism is introduced in the local leader stage that is based on the fitness of the solution. Reliability and accuracy of the intended algorithm are tested over 14 benchmarks functions and the comparison showed against various state of art algorithms available in the literature. The obtained outcomes prove the superiority of the intended algorithm." @default.
- W2898096256 created "2018-11-02" @default.
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- W2898096256 date "2018-01-01" @default.
- W2898096256 modified "2023-09-27" @default.
- W2898096256 title "Spider Monkey Optimization Algorithm with Enhanced Learning" @default.
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- W2898096256 doi "https://doi.org/10.1007/978-981-13-1813-9_39" @default.
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