This paper presents a new mutation operator called the Sobol Mutation (SOM) operator for enhancing the performance of Quantum Particle Swarm Optimization (QPSO) algorithm. The SOM operator unlike most of its contemporary mutation operators do not use the random probability distribution for perturbing the swarm population, but uses a quasi random Sobol sequence to find new solution vectors in th...

Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms traditional PSOs in search ability as well as having fewer parameters to control. In this paper, in order to depict the thinking model of people accurately that the decision-making is always influenced by the important part factors which we called elitist, so elitist mea...

Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. Although it has been shown to perform well in finding optimal solutions for many optimization problems, there has so far been little theoretical analysis on its convergence and performance. This p...

In this paper, we propose an improved quantum-behaved particle swarm optimization (QPSO), introducing chaos theory into QPSO and exerting logistic map to every particle position X(t) at a certain probability. In this improved QPSO, the logistic map is used to generate a set of chaotic offsets and produce multiple positions around X(t). According to their fitness, the particle's position is upda...

Groundwater level prediction in a water basin plays a significant role in the management of groundwater resources. Aground water level forecasting system is developed in this study using Support vector Machines (SVM). Further Quantum behaved Particle Swarm Optimization (QPSO) function is employed in this study to determine the SVM parameters. Later, the proposed SVM-QPSO model is used in determ...

Quantum-behaved particle swarm optimization (QPSO) is employed to deal with multiprocessor scheduling problem (MSP), which speeds the convergence and has few parameters to control. We combine the QPSO search technique with list scheduling to improve the solution quality in short time. At the same time, we produce the solution based on the problem-space heuristic. Several benchmark instances are...

A novel cultural quantum-behaved particle swarm optimization algorithm (CQPSO) is proposed to improve the performance of the quantum-behaved PSO (QPSO). The cultural framework is embedded in the QPSO, and the knowledge stored in the belief space can guide the evolution of the QPSO. 15 high-dimensional and multi-modal functions are employed to investigate the proposed algorithm. Numerical simula...

Wei Fang, Jun Sun and Wenbo Xu Centre of Intelligent and High Performance Computing, School of Information Technology, Jiangnan University No 1800, Lihu Avenue, Wuxi, China, 214122 {wxfangwei, sunjun_wx, xwb_sytu}@hotmail.com Adaptive infinite impulse response (IIR) filters have shown their worth in a wide range of practical applications. Because the error surface of IIR filters is multimodal i...

This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in the Basic Particle Swarm Optimization (BPSO) algorithm. The comparisons of numerical results show that QPSO outperforms BPSO algorithm in all the twelve cases ta...

In order to improve and accelerate the speed of image integration, an optimal and intelligent method for multi-focus image fusion is presented in this paper. Based on particle swarm optimization and quantum theory, quantum particle swarm optimization (QPSO) intelligent search strategy is introduced in salience analysis of a contrast visual masking system, combined with the segmentation techniqu...