Research On Intervention Cultural Algorithm And Its Applications

Cultural algorithm (CA) is an evolutionary computation model originated from culture evolutionary process. Its core is to explicitly acquire problem-solving knowledge from the evolving population and in return to apply the knowledge to guide the search. Different from traditional evolution algorithms, CA is a dual inheritance system that models two levels of evolution:the population space and the belief space, and CA can provide an explicit mechanism for acquisition, storage and integration of problem solving experience. As a result, this evolutionary speed surpasses the speed of the single biological genetic evolution. The scholars and the researchers have been paying more and more attention to this particular feature, although CA's theoretical research is still in the initial stage.Based on the discussion of CA's theory, this paper improves CA's framework and algorithms, and applies the framework into the image segmentation. The main research work and the creativities in the dissertation are as follows.Firstly, as far as the question easily-trapped in local optimum in the unconstrained optimization is concerned, the intervention cultural algorithm framework is introduced, and furthermore, apply this framework to unconstrained optimization, in order to propose two types of intervention cultural algorithms-intervention cultural algorithm with bi-communication protocol (ICAEP-bcp) and the intervention cultural particle swarm optimization (ICA-PSO).ICAEP-bcp is based on the basic CA and the evolutionary programming is used as the population model. The research applies the situational knowledge in the belief space to judge whether the algorithm is trapped in local optimum, and uses the intervention operation to keep the variety in the population space. ICA-PSO takes the particle swarm optimization as the population model, using the global optimum to judge whether the algorithm is trapped in the local optimum, through r/K selection strategies to keep the variety of the population. Compared with the basic CA, here the proposed ICAEP-bcp can fit the unconstrained optimization functions more, while ICA-PSO can fit high-dimensional complicated function's optimization more.Secondly, according to the questions of the constrained optimization, to modify the intervention CA's framework, two intervention cultural algorithms are proposed. One is based on the hierarchical architecture model (IHAM),the other is based on the dynamic model (ICAEP-DM).IHAM takes evolutionary programming as the population model, and takes tree regional structure as the belief space model. The basic characteristics of the belief cell are used to judge whether the algorithm is trapped in local optimum, through the intervention mechanism to operate the belief cell, in order to jump out of the local optimum. ICAEP-DM takes the evolutionary programming as the population model, and takes the knowledge structure in dynamic model as the belief space model. Applying the historical knowledge to judge whether the algorithm is trapped in local optimum, the knowledge concerning the belief space is operated to help the algorithm to jump out of the local optimum. Compared with the CA based on hierarchical architecture model, IHAM owns priorities. Compared with the improved evolutionary programming, the proposed ICAEP-DM has high success ratio and better stability. Compared with other competitive constraint optimization algorithm, ICAEP-DM has higher accuracy and faster convergence.Finally, the idea of ICA-PSO is introduced into the image segmentation. Combining the Otsu's method and maximum entropy method respectively, the multi-threshold image segmentation methods based on ICA-PSO are proposed for double thresholding and three thresholding. Combing the two-dimensional Otsu's method, the two-dimensional segmentation method based on ICA-PSO is proposed. Compared with other methods, the proposed methods in this paper give better performance and provide higher convergence speed.