Image processing with krill herd algorithm

The holding and transfering of the compressed image is becoming increasingly more difficult and because of this a new solution technique with a more compressed rate and image quality has been developed. The use of metaheuristic algorithms as evident throughout various literature has become more popular as a way to solve these various problems. This research will aim to analyse the effects of the metaheuristic algorithms on image compression and in accordance with this aim, Krill Herd Algorithm with Genetic Operators, Ant Colony Optimisation with Genetic Operator, Particle Swarm Optimisation and Pool Based Genetic Algorithm (PBGA) are all applied on this image. Krill Herd Algorithm and Ant Colony Algorithm are applied to the standard image and will be applied in order to unite with the genetic operators to escape from the local minimum. As a result, the mean square error and compression rate will be better. In addition to the analysis, PSO and PBGA will be applied to this problem. When the solution performances of the algorithms are compared with the same compression rate the best result is seen with PBGA. PSO, ACO with GO, ACO with GO, KHA and ACO all follow PBGA respectively.