A Modified Particle Swarm Optimization Algorithm and Application

Application of two-order particle swarm optimization algorithm in image segmentation

“…calculating the fitness I(xf) of the particle updating Pi' P g end for end for Yuhui Shi and Eberhart put forward the improved particle swarm optimization algorithm with inertia factor in 1998 [4][5][6], and updated the velocity iteration formula to formula (3).…”

“…The original particle swarm optimization is presented by James Kennedy and Russell Eberhart [1][2][3][4][5][6][7]. In the one-order particle swarm optimization algorithm, the velocity of particle is the function of differential term of historical optimal position and current position.…”

“…The shortcoming of inertia factor decreasing could be overcome by using the random inertia function [4,5,9]. The random inertia function is shown as formula (6) …”

“…Otherwise, its optimal fitness of individual remains unchanged. (6) The optimal fitness of all is compared and the best individual fitness is chosen to update the optimal fitness of the particle swarm. Otherwise, if the optimal fitness of the particle swarm is better than the current best fitness of individual, the optimal fitness of the particle swarm remains unchanged.…”

“…Particle swarm optimization is a kind of the optimization algorithms. James Kennedy and Russell Eberhart presented the original particle swarm optimization in 1995 [1][2][3][4][5][6][7]. This is a bionic algorithm, which simulates the process of bird swarm feeding to search for the global optimization.…”