@article{MAKHILLJEAS201914717607,
    title = {Image Feature Extraction using Quantum-PSO and Chaotic Map},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {7},
    pages = {2352-2359},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.2352.2359},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.2352.2359},
    author = {Seba},
    keywords = {Particle swarm optimization,quantum particle swarm optimization,chaotic number generator,logistic map,simplification,efficiency},
    abstract = {One of the population-based heuristic global search algorithms is the Particle Swarm Optimization
(PSO) algorithm that is motivated through patterns of social behavior of organisms which live and interact
within large groups. The PSO is depended on researches on swarms such as fish schooling and bird flocking
Quantum Particle Swarm (Q-PSO) algorithm based on quantum individual, the theory of quantum used the
change the adapting mode of the particles. In this study, Q-PSO algorithm was used in order to enhance the
speed of search and the convergence precision and guarantee the effectiveness and simplification. It is simpler
and more powerful than the algorithms available. The simulation and its application in the feature extraction
prove its high efficiency.}
    }