Multi-objective particle swarm optimization algorithm for cross-training programming
多目标粒子群算法在交叉培训规划中的应用

In order to improve the practicability of a cross-training programming, the factor of human learning behavior is considered. A multi-objective optimization model is presented on the basis of task redundancy policy, in which the objective functions describe the labor satisfaction and the learning efficiency. A cross-training programming based on multi-objective particle swarm optimization algorithm (MOPSO) is proposed. The MOPSO solves for the solutions of the proposed multi-objective optimization model and designs algorithm policies for different problem environments. Several flexible cell assemblies in different scales are presented for modeling the environment in a series of numerical experiments. Results in each environment are analyzed in the aspects of diversity, distribution and convergence index. The analyzed results show that the method presented in this paper can solve cross-training programming problems effectively.