文章基本信息

标题：MULTI-OBJECTIVE ARTIFICIAL BEE COLONY (MOABC) ALGORITHM TO IMPROVE CONTENT-BASED IMAGE RETRIEVAL PERFORMANCE
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作者：ANIL KUMAR MISHRA ; Dr.MADHABANANDA DAS ; DR.T.C.PANDA 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：59
期号：3
出版社：Journal of Theoretical and Applied
摘要：Multi-objective optimization has been a difficult area and focus for research in fields of image processing. This paper presents anoptimization algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems in CBIR. We have introduce to multi-object ABC algorithms is based on the intelligent scavenging behaviour for content base images. It uses less control parameters, and it can be efficiently used for solving for multi object optimization problems. In the current work, MOABC for discrete variables hasbeen developed and implemented successfully for the multi-objective design optimization of composites. The proposed algorithm is corroborated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.Finally the performance is evaluated incomparison with other nature inspired techniques which includes Multi-objective Particle Swarm Optimization (MOPSO) and Multi-objective Genetic Algorithm (MOGA). The performance of MOABC is better as par with thatof MOPSO,MOGA and ABC for all the loading conﬁgurations.
关键词：Multi-objective optimization; Structural optimization; Artiﬁcial Bee Colony (ABC); Feature Extraction.