No abstract

No abstract

The strengths and weaknesses of correlation algorithm, simulated annealing algorithm, and particle swarm optimization algorithm are studied in this paper. A hybrid optimization algorithm is proposed by drawing upon the three algorithms, and the specific application processes are given. To extract the current fundamental signal, the correlation algorithm is used. To identify the motor dynamic parameter, the filtered stator current signal is simulated using simulated annealing particle swarm algorithm. The simulated annealing particle swarm optimization algorithm effectively incorporates the global optimization ability of simulated annealing algorithm with the fast convergence of particle swarm optimization by comparing the identification results of asynchronous motor with constant torque load and step load.

No abstract

In recent years, community detection has become a hot research topic in complex networks. Many of the proposed algorithms are for detecting community based on the modularity Q. However, there is a resolution limit problem in modularity optimization methods. In order to detect the community structure more effectively, a memetic particle swarm optimization algorithm (MPSOA) is proposed to optimize the modularity density by introducing particle swarm optimization-based global search operator and tabu local search operator, which is useful to keep a balance between diversity and convergence. For comparison purposes, two state-of-the-art algorithms, namely, meme-net and fast modularity, are carried on the synthetic networks and other four real-world network problems. The obtained experiment results show that the proposed MPSOA is an efficient heuristic approach for the community detection problems.