The Research And Application Of Swarm Intelligence Optimizaiton Algorithm Under Memetic Computing Framework

Swarm intelligence is a kind of heuristic optimization algorithm,which simulates the foraging behavior of species in nature(such as animals or insects) etc.) by the interaction between individuals, to achieve the optimal solution of s the target. In real life, many practical problems are optimization problems, such as image segmentation, genetic clustering problem, social network clustering problem, classification and prediction of medical diagnosis problems, the economic dispatch problem, nurse scheduling problem, vehicle routing problem etc. and swarm intelligence algorithms show good performance in solving these problems, and has become a research hotspot in recent years. However, there are many problems to be solved, such as the speed of algorithm optimization, the accuracy of optimization etc.. Moreover, it has been proved theoretically that only one swarm intelligence algorithm can not solve all the problem.Memetic is a field of intelligent computation inspired by the concept of natural evolution principle of Darwin and the cultural evolution principle of Dawkins. Meme can be understood as a’ culture gene’, different from the gene, the culture gene has the characteristics of the inheritance and propagation. Memetic uses the complex structures, such as the simple combination of individual and meme to solve the problem. Memetic computing combines with the mechanism of the global search and local search,which makes the better search efficiency in some areas than some traditional intelligent algorithm, it is faster in several orders of magnitude. It can be applied to the field of a wide range of optimization problems and abtains satisfactory results. Memetic computing provides a framework or a concept, in this framework, using different search strategies, peoples can establish new swarm intelligence algorithm.In this regard, the focus of this paper is to study the thought and theory of the swarm intelligence optimization algorithm, under the framework of the memetic calculation. Studys the mechanism and mathematical model of tumor growth, inspired by this,under the memetic computing framework, put forward a new swarm intelligence algorithm, named the’ invasion tumor growth algorithm’, and apply it to the clustering problem and the support vector machine optimization problems.This paper mainly focuses on the improvement of swarm intelligence algorithm under the framework of memetic computing, and the theory and methods of tumor growth, and completed the following main contents and innovation points:1) In the framework of memetic computing, because the traditional quantum behaved particle swarm optimization algorithm only considers the local search ability, reduces the accuracy of optimization, especially, for the large-scale optimization problems, the performance was significantly decreased. In this regard, we first improves the traditional local search strategy, using the weighted method, strengthens the ability of local search. On this basis, in order to strengthen the global search ability of the algorithm, research the rules of memetic algorithms, assuming that, there is a ‘bird kingdom’ in the the particles(birds), and has established the mathematical model with memory. Memory reflects the meme inherited characteristics. While the memory rules can increase the diversity of the algorithm, so the algorithm is not easy to fall into local optimal. Finally, we proposed the memtic quantum particle algorithm with memory(SMQPSO). The experimental results show that quantum behaved particle swarm optimization algorithm under the framework of memetic computing has a good performance to solve the large-scale optimization problems.2) Shuffled frog leaping algorithm(SFLA) is a typical swarm intelligence optimization algorithm under the framework of memetic computing. The local search is adopted like the simplified search rules of particle swarm, while meme shuffled strategy strengthens its global search ability. But the simplified search rules of the particle swarm has the slow convergence speed and the precision is not better. In addition, in the search process, the frog jump out of bounds often happen. In this regard, the local search strategy is introduced by the focus attracting factor in this paper, in order to improve the exploring ability of the frog; using the spatial zoom method to solve border problems, put forward the spatial zoom shuffled frog leaping algorithm(sg SFLA) with the attracting factor. Experimental results show that the improved algorithm can only solve the frog cross-border problems, but also improve the performance of the algorithm.3) In the framework of memetic computing, traditional teaching and learning optimization algorithm select the better local search strategy, while ignoring the global search ability. Only consider the characteristics of inheritance without considering the characteristics of transmission of meme. Specifically, the algorithm only considers one class, and in the interactive learning process of the students, the guiding role of teachers is not considered, so reduce the precision of the algorithm to solve multimodal optimization problems. In this regard, use the memtic shuffle strategy, put forward a new teaching and learning optimization algorithm with many classes(CTLBO). Experimental results show that under the new framework, the algorithnm increases the diversity, to solve multimodal optimization problems, it has a good performance.4) Based on the deeply rearch of the quantum particle swarm algorithm, shuffled frog leaping algorithm and teaching and leaning optimization algorithnm, grasp the basic principle of the intelligent algorithm under the memetic computing framework. Through the deep research on the growth mechanism and mathematical model of the invasion of tumor growth, inspired by the invasive behavior of the human brain, putford the invasive tumor growth optimization algorithm(ITGO).In the framework of memetic computing, the cells of tumor can be divided into invasive cells, proliferative cells, resting cells and dying cells, the exchange mechanism in different subgroups guarantee the global search ability of the algorithm. The local search ability presents the different cell subgroup’s own search rules, which can guarantee the speed of convergence of the algorithm. In order to simulate tumor growth mechanism, we assume that the intrusion of proliferating cells grow according with Levy distribution; resting cell grows by the leading of proliferative cell and the interaction between resting cells c; and dying cell growth is leaded by the proliferative cell and resting cells,or they may be killed. Finally, the experimental results show that the performance of the invasive tumor growth optimization algorithm.5) Using the invasive tumor growth(ITGO) optimization algorithm to solve the clustering problem and support vector machine optimization problem. The clustering problem is often viewed as a continuous multimode optimization problem, theory has proved that it is the NP problem. Encodes the cluster centers, the goal is to achieve the near distance within class and the farthest distance in different classes. The experimental results show that the algorithm proposed in this paper reduces the clustering error rate. Support vector machine(SVM) is a machine learning algorithm, because of its good performance and widely used. But its performance is influenced by the parameters, the parameter optimization problem has become the key problem of support vector machine. In this regard, we use the invasive tumor growth optimization algorithm to solve this problem. The experimental results show that the proposed algorithm improves the accuracy of classification.All of the above work is around the research of swarm intelligence algorithm under the memetic computing framework and the theory and method of invasive tumor growth, not only relates to the memtic theory, physics, statistics method and the theory of invasive tumor growth but also in theory and practice of data mining and machine learning, reflects the depth and systematic for the research of the intelligence algorithm. This research is helpful to further study of other methods of swarm intelligence algorithm under memetic computing framework, and this algorithm can be extended to more areas, to solve the more complicated optimization problem in reality.