Swarm Intelligence Optimization Algorithm And Its Applied Research In The Ppi Network

As the development of bioinformatics, it is found that proteins don't work separately. They usually interact with others and gather as an entirety to function. All the interactions in a life together is called protein protein interaction network (PPI network). The research of PPI network could help know the biological metabolism and circulatory system, deeply analyze disease, and enlighten the meaning of life. This would lead to a new stage of our knowledge of living life. Swarm intelligent algorithms are based on bionics. They have been profoundly studied, and matured applications of swarm intelligence are widely seen in solving TSP problem, data mining, image process, function optimization etc. The existing algorithms are being improved and the new algorithms are being proposed. As a result, swarm intelligent algorithms are one of the headlines of computational intelligence.This paper firstly introduced the main concepts of swarm intelligence and gave some descriptions of several typical algorithms. In addition, it gave the advantages and disadvantages of these algorithms and pointed out where to be improved. This paper also did researches on bioinformatics and gave a brief introduction on PPI network. In the mean time, as main computation analyzing methods, clustering algorithms were described.According to the scale free and small world characteristics of PPI network, this paper analyzed the insufficiencies of the functional flow algorithm which performs comparatively well. The thought of recognizing and dealing with bridging nodes were proposed to improve the performance of the algorithm. At the basis of this, swarm intelligence was introduced into the functional flow algorithm to automatically optimize some parameter which is manually set by experience. This would help raise the stability of the results and the improved algorithm was named as IQ-Flow algorithm. MIPS PPI data was used to test the IQ-Flow algorithm, the simulation results showed that the precision and stability were raised.In swarm intelligence, this paper studied artificial bee colony (ABC) algorithm. On synthesizing kinds of improve strategies of other swarm intelligent algorithms, weight factor, contracted factor and random disturbance were introduced to improve the ABC algorithm. The improved algorithm was called IABC algorithm. The simulation results showed that IABC algorithm performs better than the original ABC and other participated swarm intelligent algorithms both in speed and precision. IABC was also used to automatically search the threshold in order to overcome the subjectivities and the precision was raised. In addition, an ACO algorithm based on joint strength (Joint Strength Based Ant Colony Optimization Algorithm, JSACO) was proposes. According to the characters of PPI data, joint strength was introduced to modify the pickup/drop rules in order to reduce the time consuming and raise the precision of the algorithm. In the simulation of this paper, MIPS PPI data were used to test the JSACO algorithm. Also the simulation result was compared with other PPI clustering methods. The simulation results show that the JSACO algorithm costs less in time consume and performs better in precision.Finally, this paper gave a conclusion and made a prospect of the upcoming work.