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- W2743138742 abstract "Aiming at the shortage that Kinetic-molecular theory optimization algorithm (KMTOA) is more likely to show premature convergence and the accuracy of searching for the optimum needs to be improved, a Kinetic-molecular theory optimization algorithm (KCKMTOA) based on Kent chaotic mapping was proposed. The algorithm utilizes the characteristics of chaotic algorithm, such as ergodicity, non-periodicity, randomness etc. When algorithm falls into the local optimum, Kent chaotic mapping is used to perform chaotic search around the local optimum to replace partial particles of original particle swarm in order to jump out from the local optimum to find the global optimum. As the algorithm is carried out, the radius of chaotic search decreases linearly, so as to ensure the precision of searching and speed of the algorithm. 20 classical test functions are taken into consideration in simulation experiments. After making a comprehensive comparison for the performance of searching for the optimum of DE, GA, QPSO, KMTOA and KCKMTOA in 20 classical test functions, the results of experiments show that this algorithm has obvious advantages in precision, speed and robustness optimization etc." @default.
- W2743138742 created "2017-08-17" @default.
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- W2743138742 date "2017-01-01" @default.
- W2743138742 modified "2023-10-12" @default.
- W2743138742 title "Kinetic-molecular theory optimization algorithm based on Kent chaotic mapping" @default.
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- W2743138742 doi "https://doi.org/10.1063/1.4992952" @default.
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