TY - GEN
T1 - A fuzzy crow search algorithm for solving data clustering problem
AU - Wu, Ze Xue
AU - Huang, Ko Wei
AU - Yang, Chu Sing
N1 - Funding Information:
Acknowledgement. This research was supported by the Ministry of Science and Technology of Taiwan, under grants MOST 108-2221-E-006-111-.
Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - The crow is one of the most intelligent bird and infamous for observing other birds so that they can steal their food. The crow search algorithm (CSA), a nature-based optimizer, is inspired by the social behavior of crows. Scholars have applied the CSA to obtain efficient solutions to certain function and combinatorial optimization problems. Another popular and powerful method with several real-world applications (e.g., energy, finance, marketing, and medical imaging) is fuzzy clustering. The fuzzy c-means (FCM) algorithm is a critical fuzzy clustering approach given its efficiency and implementation easily. However, the FCM algorithm can be easily trapped in the local optima. To solve this data clustering problem, this study proposes a hybrid fuzzy clustering algorithm combines the CSA and a fireworks algorithm. The algorithm performance is evaluated using eight well-known UCI benchmarks. The experimental analysis concludes that adding the fireworks algorithm improved the CSA’s performance and offered better solutions than those by other meta-heuristic algorithms.
AB - The crow is one of the most intelligent bird and infamous for observing other birds so that they can steal their food. The crow search algorithm (CSA), a nature-based optimizer, is inspired by the social behavior of crows. Scholars have applied the CSA to obtain efficient solutions to certain function and combinatorial optimization problems. Another popular and powerful method with several real-world applications (e.g., energy, finance, marketing, and medical imaging) is fuzzy clustering. The fuzzy c-means (FCM) algorithm is a critical fuzzy clustering approach given its efficiency and implementation easily. However, the FCM algorithm can be easily trapped in the local optima. To solve this data clustering problem, this study proposes a hybrid fuzzy clustering algorithm combines the CSA and a fireworks algorithm. The algorithm performance is evaluated using eight well-known UCI benchmarks. The experimental analysis concludes that adding the fireworks algorithm improved the CSA’s performance and offered better solutions than those by other meta-heuristic algorithms.
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U2 - 10.1007/978-3-030-55789-8_67
DO - 10.1007/978-3-030-55789-8_67
M3 - Conference contribution
AN - SCOPUS:85091312258
SN - 9783030557881
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 782
EP - 791
BT - Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
A2 - Fujita, Hamido
A2 - Sasaki, Jun
A2 - Fournier-Viger, Philippe
A2 - Ali, Moonis
PB - Springer Science and Business Media Deutschland GmbH
T2 - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
Y2 - 22 September 2020 through 25 September 2020
ER -