Zhang Dingxue

A Modified Particle Swarm Optimization with an Adaptive Acceleration Coefficients

An adaptive particle swarm optimization (PSO) was proposed based on a version of PSO without the velocity of the previous iteration. To overcome premature searching for the velocity of a particle at zero, reinitialize the velocity of the particle with a random velocity. Simultaneity, to enhance exploration in the early stage and exploitation during the latter, we introduce exponential time-varying acceleration coefficients. The simulation results show that the algorithm has better probability of finding global optimum and mean best value than others algorithm.

A Modified Particle Swarm Optimization Algorithm with Dynamic Adaptive

To overcome premature searching by standard particle swarm optimization (PSO) algorithm for the large lost in population diversity, the measure of population diversity and its calculation are given, and an adaptive PSO with dynamically changing inertia weight is proposed. Simulation results show that the adaptive PSO not only effectively alleviates the problem of premature convergence, but also has fast convergence speed for balancing the trade-off between exploration and exploitation.

Stability and Neimark?Sacker bifurcation analysis of a food-limited population model with a time delay

In this paper, a kind of discrete delay food-limited model obtained by the Euler method is investigated, where the discrete delay τ is regarded as a parameter. By analyzing the associated characteristic equation, the linear stability of this model is studied. It is shown that Neimark—Sacker bifurcation occurs when τ crosses certain critical values. The explicit formulae which determine the stability, direction, and other properties of bifurcating periodic solution are derived by means of the theory of center manifold and normal form. Finally, numerical simulations are performed to verify the analytical results.

A Modified Particle Swarm Optimization and Simulation

To overcome premature searching by standard particle swarm optimization (PSO) algorithm, a new modified PSO with information of the closest particle is proposed. In the algorithm, the particle is updated not only by the best previous position and the best position among all the particles in the swarm, but also by the best previous position of the closest particle. To balance the trade-off between exploration and exploitation and convergence to the global optimum solution, a linearly varying acceleration coefficient over the generations was introduced. The simulation results show that the algorithm has better probability of finding global optimum and mean best value than others algorithm, especially for multimodal function.

Analysis and dynamical changing inertia weight strategy of particle swarm optimization

Convergence of particle velocity and effect on optimization performance were analyzed in particle swarm optimization, and a new algorithm with dynamical changing inertia weight was proposed. The information defined as the average absolute value of velocity of all particles was used in the algorithm, which can avoid premature convergence for the velocity is closed to 0 in the early search part. The simulation results show that the algorithm has better probability of finding global optimum and mean best value than other algorithms for maintaining the population diversity.