원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.9 No.10
2016.10
pp.1-10
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Centripetal Accelerated Particle Swarm Optimization (CAPSO) is a recent and well embraced, interest stimulating topic in swarm intelligence (SI). The original CAPSO method does not have parameters to tune or adjust, so two new parameters are introduced to catapult the efficiency and boost the overall performance. For further enhancement of the algorithm’s efficiency, the principle of quantum-behaved particles is also added. In evaluating the capability of the Improved Centripetal Accelerated Particle Swarm Optimization (ICAPSO) algorithm, we tested it on medical image database, in the aspect of Relevance Feedback of a Content-Based Image Retrieval (CBIR) system, clearly, ICAPSO outperformed others.
목차
Abstract
1. Introduction
2. Improved Centripetal–Accelerated Particles Swarm Optimization(ICAPSO)
2.1. Adding Two Factors to CAPSO
2.2. Quantum Influence on Centripetal–Accelerated Particles
2.3. The ICAPSO Algorithm
3. ICAPSO Application for Relevance Feedback (RF) In Medical Image Retrieval
4. Experiments
5. Conclusion
References
1. Introduction
2. Improved Centripetal–Accelerated Particles Swarm Optimization(ICAPSO)
2.1. Adding Two Factors to CAPSO
2.2. Quantum Influence on Centripetal–Accelerated Particles
2.3. The ICAPSO Algorithm
3. ICAPSO Application for Relevance Feedback (RF) In Medical Image Retrieval
4. Experiments
5. Conclusion
References
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