An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Chaos Theory Exerting to Local Optimal Position

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

“…In order to compare the feasibility and performance of RQPSO, RRQPSO and SRQPSO with those of QPSO in this section, there are four nonlinear benchmark testing functions that are commonly used in [5], [6], [15]. These functions, the admissible range of the variable and the optimum are summarized in following.…”

“…An improved PSO(IPSO) is presented in [5], in the IPSO process, the population is divided into several subgroups, the entire population is shuffled at periodic stages in the evolution, and then points are reassigned to subgroups to ensure information sharing. Pan [6] proposed an improved particle swarm optimization algorithm based on the optimal and sub-optimal position(OSP-PSO). OSP-PSO enlarges the search space and enhances global search ability, and adopts mutation operator to keep the swarm's diversity.…”

Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization

“…Moreover, it explores the optimum solution at a low computational effort. Pan [34] introduced the chaos theory into QPSO; and the proposed method uses a logistic map to generate a set of chaotic offsets and produces multiple positions around every local optimal position of the particle, and thus, convergence accuracy is better than that in typical QPSO. Sun [35] proposed modified QPSO, which substitutes the global best position (gbest) by a personal best position (pbest) of a randomly selected particle, thus exhibiting stronger global search capability than QPSO and PSO.…”