摘要：In the process of the optimization iteration in high-dimensional space, standard particle swarm optimization algorithm falls into local optimum easily and its convergence speed is also very slow. In order to solve these problems, a self-adaptive particle swarm optimization based on the directed-weighted complex networks is proposed by introducing the complex networks. In our algorithm, we use the small-scope network with directed links to initialize the particle swarm topological structure and introduce the evolution mechanism of directed dynamic networks, which will make the particle topological structure is evolved in scale-free networks when the in-degree is complied with the power-law distribution. Therefore, our proposed algorithm will improve the learning diversity among these particles, and avoid these particles fall into local optimum easily. The simulation experiment demonstrates that the proposed algorithm will improve the premature convergence problem and enhance the algorithm convergence speed.
