Improved Free Search And Its Application On Image Segmentation

Free Search (FS) is a new swarm intelligent algorithm put forward few years ago. There is wider research space for algorithm itself. So proposing efficient improvement of FS is still a hot topic among the researchers now. A novel improved free search algorithm is proposed in this paper, which is based disaster policy. In this algorithm, the individual will make a judge of the disaster, the ones who match the condition will be initialized starting point of the next research cycle. According to the natural, the new algorithm has more powerful global searching ability. The simulation results showed that, compared with other improved FS algorithms(adaptation search algorithm and the FS based cross operator) proposed before, it is improved virtually on convergence precision and speed by using the FS algorithm based disaster policy to optimize 3 typical benchmarks.Finding a suitable combination of thresholds is the key to threshold image segmentation based on maximum entropy. The entropy is the function of the threshold, it couldn't reach precision high enough by other optimization algorithm. In this paper, Free Search was proposed to improve the method to serve the problem. By the experiment of the Lena and Pepper image, The simulation results showed that, the improved method can select optimal threshold, which can achieve more perfect segmentation effect. It is effective to use the improved algorithm in the image segmentation, whose application field is widened.