An integrated particle swarm optimization (IPSO) was proposed, where the simulated annealing optimization algorithm was combined with PSO to speed up the local search, and mutation operation was embedded to avoid the common defect of premature convergence.

With the introduction of punishment function and the appropriate modification of objective function,a sintering blending optimization algorithm is proposed,which takes full advantage of the global search ability of particle swarm optimization(PSO) algorithm and the local search ability of conjugate gradient algorithm with constraints.

In order to solve the problems of low-precision and slow control of the traditional algorithms in the pattern recognition and control of flatness,the neural network trained by hybrid algorithms of particle swarm optimization(PSO) and back propagation(BP)is introduced.

HPSO introduces evolutionary algorithm into particle swarm optimization(PSO),thus can get higher precision and faster convergence spe.

Multi-objective model of tolerance design and its solution with particle swarm optimization algorithm;

Application of immune particle swarm optimization to job-shop scheduling problem;

Bayer material balance computation based on improved particle swarm optimization algorithm;

Transmission Network Optimization Planning Based on Hybrid PSO;

PSO-Based Optimization of Signal Timing and Simulation for Isolated Intersection;

An optimal phase-factor selection algorithm for PTS based on PSO in OFDM;

Particle swarm optimization (PSO) and filter preprocessing based on hamming window is used to search the registration parameters.

The particle swarm optimization (PSO) algorithm is used to obtain the optimal solution to the minimization problem (i.

Due to the disadvantages of the canonical Particle Swarm Optimization (PSO), a new adaptive particle swarm optimization algorithm based on Sigmoid inertia weight was proposed.