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- W4360604453 startingPage "119954" @default.
- W4360604453 abstract "This paper proposes an improved variant of the harmony search (HS) algorithm called HSGS for solving clustering problems. The HSGS generates two new harmonies at each iteration. The first is produced by the harmony improvisation procedure of the canonical HS and promotes exploration ability. The second is generated by one of two new phases added to HS to enhance the exploitation capability with different ratios. Each phase employs the mathematical properties of a famous pattern found in nature, called golden section, to search around a specific harmony. In addition, these phases utilize different information of data points that need to be clustered. In each cycle of the HSGS, a self-adaptive selection mechanism selects a phase to properly adjust the exploitation ratio during the search process. A harmony with a better objective value between the two new harmonies competes with the current worst harmony to update the harmony memory. The performance of HSGS for clustering several well-known benchmark data sets, gene expression data sets, and noisy versions of gene expression data sets is compared with two modified variants of HSGS, seven HS-based clustering algorithms, k-means++, and six evolutionary clustering algorithms. In general, the results indicate that HSGS is superior to all considered algorithms in all data sets." @default.
- W4360604453 created "2023-03-24" @default.
- W4360604453 creator A5009261595 @default.
- W4360604453 creator A5027824156 @default.
- W4360604453 creator A5077151661 @default.
- W4360604453 date "2023-08-01" @default.
- W4360604453 modified "2023-09-23" @default.
- W4360604453 title "HSGS: A hybrid of harmony search algorithm and golden section for data clustering" @default.
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