Purpose The evolution of crowd intelligence is a mainly concerns issue in the field of crowd science. It is a kind of group behavior that is superior to the individual’s ability to complete tasks through the cooperation of many agents. In this study, the evolution of crowd intelligence is studied through the clustering method and the particle swarm optimization (PSO) algorithm. Design/methodology/approach This study proposes a crowd evolution method based on intelligence level clustering. Based on clustering, this method uses the agents’ intelligence level as the metric to cluster agents. Then, the agents evolve within the cluster on the basis of the PSO algorithm. Findings Two main simulation experiments are designed for the proposed method. First, agents are classified based on their intelligence level. Then, when evolving the agents, two different evolution centers are set. Besides, this paper uses different numbers of clusters to conduct experiments. Practical implications The experimental results show that the proposed method can effectively improve the crowd intelligence level and the cooperation ability between agents. Originality/value This paper proposes a crowd evolution method based on intelligence level clustering, which is based on the clustering method and the PSO algorithm to analyze the evolution.
Abstract. In the foreign fibers cleaning process, pseudo-foreign fibers are often mistaken for foreign fibers, this result not only seriously affects the detecting precision of foreign fibers cleaning machine, but also doubles the time of cleaning up lint. As for false identification problem of pseudo-foreign fibers in cotton, this paper proposes a new approach for fast segmentation of pseudo-foreign fibers in cotton on the basis of improved genetic algorithm. This improved genetic algorithm reduced the searching range for calculating optimal threshold from 0~255 to 100~220. The calculating speed in this stage was improved more than twice in average. The fitness amendments formula is also proposed to improve genetic algorithm disadvantage, at the same time, this solved issues of "premature", and converging to global optimal solution difficultly in the traditional algorithm. The results show that the algorithm has high speed, accuracy, anti-interference and so on.Keywords: Cotton, Pseudo-foreign fibers, Genetic algorithm, Image segmentation,Threshold. IntroductionSorting out foreign fibers in cotton is an urgent solved problem [1]. Foreign fibers have affected the quality of cotton products seriously [2]. Automated visual inspection (AVI) system is a main tool at present for real time foreign fibers detection in lint. The theory of this system is color differences of image processing technology [3][4]. The concept of color difference is based on that impurities of lint cotton and the cotton background have different colors. Hence, the impurities which has similar colors and shapes with foreign fibers were thought as foreign fibers to be removed, which is called as "pseudo-foreign fibers in cotton", also referred to be "pseudo-foreign fibers".* Corresponding author. Image Segmentation of Pseudo-foreign Fibers in Cotton 539Pseudo-foreign fibers in cotton have various kinds, such as cottonseed, grass blade, boll shell, cotton leaf, cotton stick, weed, stringy cotton, stringy cotton, colored cotton and oiled cotton, etc. [5]. These pseudo-foreign fibers are very similar to the foreign fibers in color, size and shape. However, compared with foreign fibers, pseudo-foreign fibers harm hardly to the cotton into yarn, dyeing, bleaching and other aspects. On the new standard GB1103-2007 of cotton, these pseudo-foreign fibers in cotton are ruled to follow the normal process of impurities to remove, and then determine level according to standards cotton classification [6].Meanwhile, the traditional methods of image segmentation, such as Otsu method, and the watershed method, are tested to process the pseudo-foreign fibers' image. The histogram information generated by these methods has only one peak, and the peak threshold is more concentrated, and segmentation effect is not satisfactory [7].Due to false identification by cleaning machine of foreign fibers, pseudo-foreign fibers affected seriously the detection accuracy of the foreign fibers cleaning machine, also affected the efficiency of the foreign fibers cleaning machin...
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