Dynamic service selection based on chaotic mutation multi-objective particle swarm algorithm

Focused on the problem of the QoS global optimal dynamic service selection, this paper established a multi-objective service composition optimization model with QoS restriction. Then, it analyzed some disadvantages of the traditional multi-objective particle swarm algorithm, such as less diversity of solutions and falling into local extremum easily. At last, it proposed a method of chaotic mutation multi-objective particle swarm algorithm. The algorithm used the information entropy theory to maintain the diversity of non-dominated solution. In the latter part of the implementation of the algorithm, this paper introduced chaotic disturbance mechanism to improve the diversity of the population and the ability of global optimization algorithm, avoid falling into local extremum, and do rapid convergence to get the optimal non-dominated solutions. The experiment showed that this algorithm has good astringency, homogeneous solution, and better synthesized ability at dealing with the problem of service selection.