MULTI-OBJECTIVE ARTIFICIAL BEE COLONY (MOABC) ALGORITHM TO IMPROVE CONTENT-BASED IMAGE RETRIEVAL PERFORMANCE
Anil Kumar Mishra, Madhabananda Das · 2014
Multi-objective optimization has been a difficult a rea and focus for research in fields of image proce ssing. This paper presents anoptimization algorithm based on artificial bee colony (ABC) to deal with multiobjective optimization problems in CBIR. We have introduce to multi-object ABC algorithms is based on the intelligent scavenging behaviour for content ba se images. It uses less control parameters, and it can be efficiently used for solving for multi object optim ization 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, a nd simulation results show that the proposed approach is highly competitive and can be considered a viabl e alternative to solve multi-objective optimization p roblems.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.