If some nonglobal best position of the particle becomes the target which is the convergence of the particle swarm, and it is located near the global optimal solution, then the probability of finding the global optimal solution will be increased greatly, so that the global search capability of QPSO
algorithm is improved evidently [49, 50].
Static QPSO with synergetic melting prototype .
Dynamic QPSO where [alpha] changes according to (29) and randomly initialized.
Dynamic QPSO as (29), initialized with CVT and a synergetic melt prototype.
implementabilit s still and memory and y missing artificial pattern features recognition Table 2: Classification Accuracies for different QPSOs.
Although QPSO has been successfully applied in conventional single-objective optimization problems due to its global convergence and easy control, it is rarely used in solving multiobjective optimization problems [20,21].
The rest of the paper is organized as follows: After a brief introduction of the background of PSO and QPSO in Section 2, a novel ring model for position update is proposed in Section 3 and a new version of multiobjective quantum-behaved particle swarm optimization algorithm (MOQPSOr) is presented by integrating the new position-update strategy into it accordingly.
Compared with PSO, the most significant advantage of QPSO is that its global convergence can be theoretically guaranteed .
Unlike PSO, each individual particle in QPSO moves in the search space with a [delta] potential on each dimension, of whose center is point [p.
In QPSO, the distribution scope of each particle is set elaborately to relate to its relative position in the whole swarm:
Therefore, the position of the particle in QPSO is updated according to the following iteration equation: