This paper presents a novel segmentation algorithm based on optimizing histogram multi-level thresholding of images
by employing a variation of particle swarm optimization (PSO) Algorithm which improves the accuracy and the speed of
segmentation based on the conventional PSO algorithm. Entropy has been chosen as the criteria for segmentation based
on the multi-level thresholding. Entropy is input parameter of a fitness function for finding the best segmentation level.
We have to find the optimum thresholding level based on the entropy of different image segments. A new optimization
algorithm that called Hybrid cooperative- comprehensive learning PSO (HCOCLPSO), is used for optimization in this
paper. This algorithm overcomes on common problems of basic variants of PSO, which are curse of dimensionality and
tendency of premature convergence or in other word, getting stuck in local optima. This segmentation technique has
been compared with conventional segmentation based on PSO and genetic algorithm (GA). We presented our
segmentation results to experts. Our subjective measurements by experts show that we can achieve about 80 percents
accuracy which is a better result when compared with conventional PSO and genetic algorithm. In terms of seed we can
achieve much higher performance than two other schemes.