An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position

Xi, Maolong; Sun, Jun; Xu, Wenbo

doi:10.1016/j.amc.2008.05.135

Cited by 205 publications

(107 citation statements)

References 3 publications

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“…To accomplish this, the effect values (ai) for each hi are calculated using the QPSO equations defined by Eq. (4)-(6) [28][29]. Finally, the gi values are obtained by Eq.…”

Section: Fuzzy Integral Stagementioning

confidence: 99%

“…As is well known, fuzzy set operations are extensively used for information aggregation [27]. The fuzzy integral that is an aggregation function using fuzzy measures is expressed as a computational scheme to integrate all of the values from the individual subsets [28]. The Choquet fuzzy integral, one of many fusion operators, can be computed as follows [27]: The attribute values (hi) for the Choquet fuzzy integral are the fuzzy values obtained by inferencing for each input configuration.…”

Section: Fuzzy Integral Stagementioning

confidence: 99%

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An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Karaköse¹

2018

IJCIS

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People working in the fields of robotics, animation, computer graphics and computer vision, and biomechanics, find it difficult to conduct human motion simulations, such as lifting, walking and running. This is because it is difficult to predict all of the motion strategies in a variety of situations. The human lifting motion is hard work; at the same time, lifting is the most demanded robotic motion. In this paper, a fuzzy integral based nonstationary fuzzy inference system is proposed to control a five-segment human model for the human lifting motion with human-inthe-loop simulation. This system was designed to reduce the computational complexity of nonstationary fuzzy systems. Hence, this paper contributes to the literature in two ways: as an improved nonstationary fuzzy system and as a human lifting motion simulation framework. A fuzzy integral algorithm between the fuzzification and defuzzification stages has been used to aggregate the inference outputs of the nonstationary fuzzy controller. The fuzzy integral algorithm uses fuzzy values obtained during the fuzzification stage as the attribute values and the fuzzy values obtained by a one-loop quantum particle swarm optimization algorithm as the importance values. The computational complexity in the nonstationary fuzzy systems and type-2 fuzzy systems can be reduced between 25 to 60 percent with the improved nonstationary fuzzy system. An experimental application of the human lifting motion was carried out to demonstrate the effectiveness of the proposed approach. The results illustrated that the proposed algorithm can achieve increased simplicity, improved effectiveness, good robustness, and a higher precision of computation.

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“…To accomplish this, the effect values (ai) for each hi are calculated using the QPSO equations defined by Eq. (4)-(6) [28][29]. Finally, the gi values are obtained by Eq.…”

Section: Fuzzy Integral Stagementioning

confidence: 99%

Section: Fuzzy Integral Stagementioning

confidence: 99%

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Karaköse¹

2018

IJCIS

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show abstract

“…From the quantum mechanics perspective, QPSO considers the particle possess quantum behavior and cannot determine the exact values of position vector and velocity vector simultaneously according to uncertainty principle [25,26]. Hence, there is no velocity vector in the particle of QPSO, and particle state is associated with an appropriate time-dependent Schrödinger equation and can be characterized by wave function ψ instead of position and velocity [27,28], where 2 ψ is the probability density function of its position. Let M particles in d dimensional space with k maximum generations, the position vector of ith particle at kth generation can be expressed as…”

Section: Quantum-behaved Particle Swarm Optimizationmentioning

confidence: 99%

“…In QPSO, the global optimal solution in the whole searching space can be guaranteed theoretically. Moreover, simulation results of numerous complex benchmark functions showed that QPSO has better global searching ability than the basic PSO [26,27]. Hence, QPSO are widely used to solve the complex optimization problems which includes hydrothermal scheduling and economic dispatch problem [28,29].…”

Section: Introductionmentioning

confidence: 99%

Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

Cheng

Niu

Feng

et al. 2015

Water

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Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast OPEN ACCESSWater 2015, 7 4233 accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.

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“…where mbest is the average optimal position of all the particles [24], and it can be computed by Equation (7). ,1 ,2 ,…”

Section: Quantum-behaved Particle Swarm Optimizationmentioning

confidence: 99%

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

2015

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Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the number of iterations is limited. A novel way of computing the local attractor for QPSO is proposed to improve QPSO's performance in global searching, and this novel QPSO is denoted as EQPSO during which we can guarantee the particles are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iteration. We also discuss this way of computing the local attractor in mathematics. The results of test functions are compared between EQPSO and other optimization techniques (including six different PSO and seven different optimization algorithms), and the results found by the EQPSO are better than other considered methods.

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An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position

Cited by 205 publications

References 3 publications

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

An Improved Nonstationary Fuzzy System Approach versus Type-2 Fuzzy System for the Lifting Motion Control with Human-in-the-Loop Simulation

Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

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