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- W1575043707 abstract "The clustering problem is among the most important optimization problems. Given that it is an NP-hard problem, it can be efficiently solved using meta-heuristic algorithms such as the gravitation search algorithm (GSA). GSA is a new swarm- based algorithm particularly suitable for solving NP-hard combinatorial optimization problems. This paper solves the clustering problem with a newly proposed memetic GSA (MGSA) algorithm. MGSA is coupled with the pattern reduction operator and the multi-start operator. The proposed MGSA algorithm was verified on six UCI benchmarks and images segmentation. Based on a performance comparison amongst MGSA, the original GSA, and two state-of-the-art meta-heuristic algorithms (Firefly algorithm and the Artificial bee colony algorithm), we observe that the proposed algorithm can significantly reduce computation time without compromising much on the quality of the solution." @default.
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- W1575043707 date "2015-05-01" @default.
- W1575043707 modified "2023-09-26" @default.
- W1575043707 title "A memetic gravitation search algorithm for solving clustering problems" @default.
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- W1575043707 doi "https://doi.org/10.1109/cec.2015.7256966" @default.
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