2012
DOI: 10.4028/www.scientific.net/amm.155-156.92
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Improved Shuffled Frog-Leaping Algorithm and its Application

Abstract: This paper proposes an improved shuffled frog-leaping algorithm, the algorithm improves the subpopulation frog individual optimization way which is not just the worst individual optimization. "Guide optimal" probability and "guide suboptimal" probability are put forward. The experiments results of multiple problems of the TSPLIB show that the algorithm is feasible and effective.

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Cited by 2 publications
(4 citation statements)
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“…In the paper, the SFLA is used to optimize the parameters of the ICM model to obtain the combined the optimal parameters f and g. SFLA imitates the process of the frog population distribution in the food searching, mainly includes two parts: local search and global information exchange (Zhang et al, 2018). The algorithm first performs a local search, and then uses the information sharing among subgroups to search for a global search.…”
Section: Icm Parameter Adaptive Designmentioning
confidence: 99%
See 2 more Smart Citations
“…In the paper, the SFLA is used to optimize the parameters of the ICM model to obtain the combined the optimal parameters f and g. SFLA imitates the process of the frog population distribution in the food searching, mainly includes two parts: local search and global information exchange (Zhang et al, 2018). The algorithm first performs a local search, and then uses the information sharing among subgroups to search for a global search.…”
Section: Icm Parameter Adaptive Designmentioning
confidence: 99%
“…The method is to divide a frog population into several subgroups, and each frog only belongs to a specific subgroup (Zhang et al, 2018). Firstly, the fitness rules is set up to get the frog Xb with the best fitness, and the frog Xw with the worst fitness in the subgroup, and the best frog Xm among all frogs in the whole population.…”
Section: Icm Parameter Adaptive Designmentioning
confidence: 99%
See 1 more Smart Citation
“…In agricultural information collection facilities, the domestic agricultural production environmental monitoring, fertilizer and water testing, heavy metal detection sensor equipment, data collection terminals in kind, function, sensitivity, stability (Zhang et al, 2012;Gg et al, 2012). Cost has not yet reached large-scale application of the Internet of Things requirements need to be further towards miniaturization, precision, sensitivity development; crop performance information perception sensor body also heavily dependent on imports, the cost is high.…”
Section: Introductionmentioning
confidence: 99%