Fuzzy Clustering with Obstructed Distance Based on Quantum-Behaved Particle Swarm Optimization

Ping, Lu; An-xin, Zhao

doi:10.1109/gcis.2010.57

Cited by 4 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Combined with fuzzy theory, a particle swarm optimization approach is improved for image clustering in [167]. [168] introduces detour distance into QPSO and applies the particles escaping principle to avoid the phenomenon that the updated cluster center particles sink into the area of the obstacles. To address the problem of predefining clusters number in PSO, [169] updates the amount of cluster centroids in the process of iteration dynamically, preventing overcongregating particles near boundaries of solution space and making the algorithm to search for optimum in different dimensions.…”

Section: ) Algorithm Descriptionmentioning

confidence: 99%

Quantum Optimization and Quantum Learning: A Survey

Tian

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Quantum mechanism, which has received widespread attention, is in continuous evolution rapidly. The powerful computing power and high parallel ability of quantum mechanism equip the quantum field with broad application scenarios and brand-new vitality. Inspired by nature, intelligent algorithm has always been one of the research hotspots. It is a frontier interdisciplinary subject with a perfect integration of biology, mathematics and other disciplines. Naturally, the idea of combining quantum mechanism with intelligent algorithms will inject new vitality into artificial intelligence system. This paper lists major breakthroughs in the development of quantum domain firstly, then summarizes the existing quantum algorithms from two aspects: quantum optimization and quantum learning. After that, related concepts, main contents and research progresses of quantum optimization and quantum learning are introduced respectively. At last, experiments are conducted to prove that quantum intelligent algorithms have strong competitiveness compared with traditional intelligent algorithms and possess great potential by simulating quantum computing. INDEX TERMS Quantum optimization, quantum learning, quantum evolutionary algorithm (QEA), quantum particle swarm algorithm (QPSO), quantum immune clonal algorithm (QICA), quantum neural network (QNN), quantum clustering (QC).

show abstract

Section: ) Algorithm Descriptionmentioning

confidence: 99%