A Distributed Clone Selection Algorithm with Its Application in Image Enhancement

A distributed clone selection algorithm was proposed and applied for image enhancement in the paper. The task of the distributed clone algorithm was to adapt the parameters of the image enhancement transforming function, as to enhance the contrast and detail in the image according to an objective fitness criterion. In order to improve the performance of the clone selection algorithm, the antibodies were separated into different groups for evolution to keep the balance of local and global search efficiency as well as to avoid the premature convergence. Gaussian distribution, chaotic distribution, and uniform distribution were utilized in each antibody population group for mutation. Extended experimentation with image enhancement applications indicated that the proposed distributed clone selection algorithm achieved a great performance in optimization process.