Large-scale optimization arises in a variety of scientific and engineering applications. In this paper, a particle swarm optimization (PSO) approach with dynamic neighborhood that is based on kernel fuzzy clustering and variable trust region methods (called FT-DNPSO) is proposed for large-scale optimization. The cooperative coevolution incorporated with a kernel fuzzy C-means clustering strategy is introduced to divide high-dimensional problems in to subproblems, and explore their search spaces. Furthermore, the independent variable ranges change adaptably by using the variable trust region learning method, which expedites the convergence process and explores in the effective space. In addition, the dynamic neighborhood topology assists the PSO algorithm in cooperating with neighbor particles and avoids the problem of premature convergence. Simulation results substantiate the effectiveness of the proposed algorithm to solve large-scale optimization problems with many well-known benchmark functions.
- Cooperative coevolution (CC)
- dynamic neighborhood topology
- kernel fuzzy clustering
- large scale optimization
- particle swarm optimization (PSO)
- trust region