关键词: 粒子群优化算法, 过程挖掘, 早熟收敛

Abstract: To realize process mining and to overcome the disadvantages that Standard Particle Swarm Optimization (SPSO) was easily getting into pre-maturity and local optimum, mutation operation based particle swarm process mining was proposed. In evolutionary process of SPSO, all particles followed the optimal particle to search, which led population diversity to appear premature convergence after decreased rapidly. Under genetic algorithm inspiration, the algorithm was flung off restraint of falling in local extreme point by carrying out mutation operation for particle in population. Thus algorithm's ability to extricate escape from the local optimum was improved. Experimental results showed that the proposed method got good effect in accuracy and speed of solution.

Key words: particle swarm optimization algorithm, process mining, premature convergence, mutation