An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Random Weight

Abstract

The standard particle swarm optimization (PSO) algorithm converges very fast, while it is very easy to fall into the local extreme point. According to waiting effect among particles with mean-optimal position(MP), the quantum-behaved particle swarm optimization (QPSO) algorithm can prevent the particle prematurely from falling into local extreme point, but its convergence speed is slow and convergence precision is still low. In order to further improve the precision of QPSO algorithm, the evaluation method of $delta$ trap characteristic length $L(t)$ of wave function for describing the particle's state is modified. In QPSO, a random weight to each particle in swarm is introduced, and according to the order of the each particle best position's fitting value, there are three evaluation programs for $L(t)$, which are random-weight mean-optimal position(RMP), reverse-order random-weight mean-optimal position(RRMP) and same-order random-weight mean-optimal position(SRMP). Through the test of several typical functions, its result shows that the convergence accuracy of QPSO with RMP and RRMP is better than those of QPSO with MP, so the evaluation of $L(t)$ with RMP and RRMP is feasible and effective

An application of PSO in a two-warehouse inventory model for deteriorating item under permissible delay in payment with different inventory policies

“…In this QPSO, particles' state equations are structured by wave function and the state of each particle is described by the local attracter p and the characteristic length L of d-trap which is determined by the mean-optimal position (MP). As MP enhances the cooperation between particles and particles' waiting with each other, QPSO can prevent particles trapping into local minima [41]. However the speed and accuracy of convergence are also slow.…”

“…In this connection, one may refer to the existing improved versions of QPSO, like, Weighted QPSO i.e., WQPSO [42]. AQPSO [43], RQPSO, RRQPSO, SRQPSO [41]. Gaussian QPSO i.e., GQPSO [44].…”

Optimization design of a gating system for sand casting aluminium A356 using a Taguchi method and multi-objective culture-based QPSO algorithm

“…[20][21][22] Above all, the PSO in evolutionary algorithms was used to effectively search for the input variables to obtain the optimal response objectives. 23,24 The PSO has high computation efficiency and algorithm simplicity. However, it is easily trapped into local optimum when dealing with multi-modal optimization problems.…”

Solving Lattice Protein Folding Problems by Discrete Particle Swarm Optimization

“…Recently, the researchers in [36] proposed an updated setbased PSO (S-PSO) for solving discrete optimization problems. The main difference between the newly proposed S-PSO and the previous ones [37,38,39] is that the new one incorporated possibilities to velocity set elements for each particle. This update helped to guide the movements of particles towards the optima.…”