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 ...

Aiming to reduce the drawbacks of traditional VIRE approach, such as inaccurate boundary positioning and poor results of virtual tags using linear interpolation, we propose a new algorithm based on Radial Basis Functions (RBF) interpolation method and Quantum-behaved Particle Swarm Optimization (QPSO) in this paper. In order to simulate the actual loss of RSSI better, the proposed approach uses...

The phenomenon of network resource-constrained often appears in wireless sensor network (WSN) because of nodes with energy-limited, so it becomes a research topic to study on high-performance routing protocol in wireless sensor network. In accordance with the characteristic of quantum particle swarm optimization (QPSO), a novel clustering routing protocol with QPSO algorithm is proposed based o...

In this paper we investigate the use of quantum particle swarm optimization (QPSO) principles to resolve the satisfiability problem. We describe QPSOSAT, a new iterative approach for solving the well known Maximum Satisfiability problem (MAX-SAT). This latter has been shown to be NP-hard if the number of variables per clause is greater than 3. The basic idea is to harness the optimization capab...

Variation elliptical piston skirt has better mechanical and thermodynamic properties and it is widely applied in internal combustion engine in recent years. Because of its complex form, its geometrical precision evaluation is a difficult problem. In this paper, quasi-particle swarm optimization QPSO is proposed to calculate the minimum zone error and ellipticity of crosssection linear profile, ...

An improved quantum-behaved particle swarm optimization with elitist breeding (EB-QPSO) for unconstrained optimization is presented and empirically studied in this paper. In EB-QPSO, the novel elitist breeding strategy acts on the elitists of the swarm to escape from the likely local optima and guide the swarm to perform more efficient search. During the iterative optimization process of EB-QPS...

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) with MOEA/D framework order to make QPSO be able solve MOPs effectively, advantage of being fully used. We also employ a diversity controlling mechanism avoid premature convergenc...