An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

Abstract EN

The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process.

To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed.

Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position.

At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution.

In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided.

The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem.

Compared with other algorithms, the convergence rate is faster.