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Modified Optimization by Artificial Bee Colony ( MOABC ) for improvement in content-based image retrieval performance

Artificial Bee Colony(ABC) algorithm based optimization techniques are quite popular and focus for research in fields of Swarm Intelligence. This paper presents an optimization algorithm based on artificial bee colony (ABC) to deal with optimization problems in Content Based Image Retrieval (CBIR) System. We have introduced a modified ABC algorithm which 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 has been developed and implemented successfully for the multi variable design optimization of composites. The proposed algorithm is applied to Image retrieval section of CBIR system for minimizing the feature vectors and to find the image similar to the queried image. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The performance of MOABC is better as par with that of ABC, PSO and GA for all the loading configurations.