Research on improved artificial bee colony algorithm based on large-scale multi-objective group combination optimization

Abstract

Large power system contains many generating units, and many factors need to be considered during operation. The unit combination optimization is a nonlinear large-scale optimization problem with multi-objective and multi-constraints, and the existing methods have many shortcomings. Artificial bee colony algorithm has good performance in solving nonlinear optimization problems, but it has some disadvantages such as low optimization efficiency and local extremum. To solve these problems, an improved artificial bee colony algorithm is proposed. The algorithm introduces variable field of vision, adjusts the initial solution and selection strategy, and combines with mutation operation in genetic algorithm. A multi - objective optimization model considering economy and environmental protection is constructed. In order to solve the problem that the calculation time is too long due to the expansion of the unit scale, a phased optimization method is adopted, and the improved algorithm is applied to the start-stop scheduling stage. After determining the start-stop state of the unit, the mixed integer programming method is used for load distribution. For unit contains up to 1000 units of large power grid optimization examples are simulated experiment, the experimental results show that the improved optimization algorithm convergence and global search ability is improved, greatly reduces the computation time of the mass unit combination, multiple objective conditions have also made the ideal results, verify the effectiveness of the proposed method.