Keywords: PSO QPSO Mean best position Weight parameter WQPSO a b s t r a c t Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with weighted mean best position according t...

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and...

This paper presents a novel image segmentation algorithm, which uses a biologically inspired paradigm known as swarm intelligence to segment images. A more efficient MRF based clustering algorithm that incorporated the Markov Random Field (MRF) theory and the Quantum Particle Swarm Optimization (QPSO) algorithm is proposed. the QPSO algorithm is ised to optimize the energy function which is a c...

Quantum-behaved particle swarm optimization (QPSO) is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO) and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED) problems with valve-p...

This paper aims to present a robust algorithm developed that minimize the number of sensor nodes in WSN using three quantum-behaved swarm optimization techniques based on Lorentz (QPSO-LR), Rosen–Morse (QPSO-RM), and Coulomb-like Square Root (QPSO-CS) potential fields. The allocate minimum wireless sensors forested areas without losing connectivity an environment with high penetration vegetatio...

This paper proposes a novel variant of quantum-behaved particle swarm optimization (QPSO) algorithm with the local attractor point subject to a Gaussian probability distribution (GAQPSO). The local attractor point in QPSO plays an important in that determining the convergence behavior of an individual particle. As such, the mean value and standard deviation of the proposed Gaussian probability ...

One of the popular methods for optimizing combinational problems such as portfolio selection problem is swarmbased methods. In this paper, we have proposed an approach based on Quantum-Behaved Particle Swarm Optimization (QPSO) for the portfolio selection problem. The particle swarm optimization (PSO) is a well-known population-based swarm intelligence algorithm. QPSO is also proposed by combin...

The on-orbit servicing mission assignment is very important to improve the cooperative work ratio of the on-orbit servicing spacecraft. One mission of on-orbit servicing can be fulfilled in many ways,and proper mission assignment must be guaranteed. Based on the characteristics and technical specifications of the mission planning issues, a multi-objective quantum-behaved particle swarm optimiza...

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and...

Quantum Particle Swarm Optimization (QPSO) is a global convergence guaranteed search method which introduces the Quantum theory into the basic Particle Swarm Optimization (PSO). QPSO performs better than normal PSO on several benchmark problems. However, QPSO’s quantum bit(Qubit) is still in Hilbert space’s unit circle with only one variable, so the quantum properties have been undermined to a ...