Multi-level quantum inspired metaheuristics for automatic clustering of hyperspectral images

Dutta, Tulika; Bhattacharyya, Siddhartha; Panigrahi, Bijaya Ketan; Zelinka, Ivan; Mršić, Leo

doi:10.1007/s42484-023-00110-7

Cited by 3 publications

(1 citation statement)

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“…Fengcai et al 42 developed an improved Bloch quantum artificial bee colony algorithm (IBQABC) where QC theory and the related ideas of the Bloch sphere are utilized and applied to segment the gray-level images using a multilevel thresholding technique. In 43 , six multilevel quantum-inspired (i.e., QC) metaheuristics were developed, and they are Qubit GA, Qutrit GA, Qubit Multi-exemplar PSO, Qutrit Multi-exemplar PSO, Qubit Artificial Humming Bird Algorithm (AHBA), and Qutrit AHBA. These algorithms were applied to the automatic clustering of hyperspectral images.…”

Section: Related Workmentioning

confidence: 99%

QSHO: Quantum Spotted Hyena Optimizer for Global Optimization

Si,

Miranda,

Nandi

et al. 2024

Preprint

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Spotted Hyena Optimizer (SHO) is a population-based metaheuristic algorithm inspired by the spotted hyenas’ social behavior, and it is developed for solving global optimization problems. SHO has shown superior performance over its competitive metaheuristic algorithms in solving benchmark function optimization problems and engineering design problems. However, it suffers premature convergence in local optima due to its lack of exploration while solving multi-modal optimization problems. This article proposes an improved SHO, namely quantum SHO (QSHO), inspired by quantum computing. The QSHO implements a quantum computing mechanism to promote its exploration ability. The novel method is tested on well-known IEEE CEC2013 and IEEE CEC2017 benchmark suits with 30 and 50 dimensions and four real-world engineering optimization problems. The results of QSHO are compared with that of Classical SHO, improved SHO (ISHO), Modified SHO (MSHO), Oppositional SHO with mutation operator (OBL-MO-SHO), SHO with space transformation search (STS-SHO), Quantum Salp Swarm Algorithm (QSSA), and Chimp Optimization Algorithm (ChOA). The results are analyzed using the Wilcoxon Signed Rank Test (WSRT) and Friedman Test. The empirical results show that QSHO statistically outperforms other compared algorithms for both benchmark problem suits with both 30 and 50 dimensions. In addition, we have assessed the QSHO in four real-world engineering optimization problems and the QSHO statistically outperforms the competitive algorithms.

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Section: Related Workmentioning

confidence: 99%