Study on Improved Particle Swarm Optimization Algorithm and Its Application

Chen, Ruqing

doi:10.1109/icnc.2009.175

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…For the algorithm of sub-population cooperation in particle swarm optimization, Chen et al 41 proposed a scheme in which adjacent sub-populations sequentially transferred the optimal solutions to cooperate and update the speed according to the weighted average of the optimal solutions of adjacent populations, which improved the global search ability of the algorithm to some extent. However, its cooperation mode is only limited to adjacent subpopulations, and the information on each subpopulation is not fully utilized.…”

Section: Robot Joint Error Identification Methods Based On Improved Psomentioning

confidence: 99%

Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling

Shen

Liu

et al. 2023

Quality & Reliability Eng

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To tackle the low identification accuracy of robot positioning, this paper proposes a positioning failure error identification method for industrial robots, combining the Kriging surrogate model and the improved particle swarm optimization algorithm. First, based on the Denavit-Hartenberg and the small displacement screw methods, the kinematic model of a six-degree-of-freedom industrial robot with joint errors is constructed. Then, the Kriging surrogate model of the kinematic error is constructed, which is trained by generated training samples. Finally, the joint error identification of industrial robots is solved by an improved particle swarm optimization algorithm based on exponential inertia weight and sub-population cooperation under the multi-attitude positioning condition. The simulation results show that the proposed optimization algorithm can significantly improve the convergence accuracy and accurately identify the actual errors at each joint of the industrial robot.

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Section: Robot Joint Error Identification Methods Based On Improved Psomentioning

confidence: 99%

Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling

Shen

Liu

et al. 2023

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…(1) it requires many preset parameters, making it challenging to find the optimal parameters; (2) the change in particle positions lacks randomness, making it easy to fall into the trap of local optimization [76]. A good solution is to adopt the Quantum Particle Swarm Optimization (QPSO) algorithm, which increases particle position's randomness by eliminating particle movement's direction attribute to remove the correlation between location updates and previous motion of particles [77]. QPSO can also make integer particle positions suitable for addressing the integer optimization issue.…”

Section: Moqpsomentioning

confidence: 99%

A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

Wang,

Jiang,

2024

CMES

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The unmanned aerial vehicle (UAV) swarm plays an increasingly important role in the modern battlefield, and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm. Due to the high cost and long duration of operational tests, it is essential to plan the test in advance. To solve the problem of planning UAV swarm operational test, this study considers the multi-stage feature of a UAV swarm mission, composed of launch, flight and combat stages, and proposes a method to find test plans that can maximize mission reliability. Therefore, a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission. A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans. This study first constructs a mission reliability model for the UAV swarm in the combat stage. Then, the launch stage and flight stage are integrated to develop a complete PMS (Phased Mission Systems) reliability model. Finally, the Binary Decision Diagrams (BDD) and Multi Objective Quantum Particle Swarm Optimization (MOQPSO) methods are proposed to solve the model. The optimal plans considering both reliability and cost are obtained. The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

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“…It is important that the ethylene yield is estimated on-line in order to control the product quality and product yield. Thus, a soft sensor should be developed to solve this problem [8].…”

Section: A Application Background Descriptionmentioning

confidence: 99%

“…Twenty independent experiments are carried out for each method and the averaged results are list out in Table II. Root mean squared error (RMSE) and maximal absolute error (MAXE), which are defined as formula (7) and formula (8), are used to evaluate the generalization performance of the three algorithms. Where i y and i y ∧ represent the real value and estimated output value, l is the sample number of testing set.…”

Section: B Soft Sensor Modeling and Analysismentioning

confidence: 99%

Soft sensor modeling based on modified PSO

Chen

2012

2012 8th International Conference on Natural Computation

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Standard particle swarm optimization (PSO) has the drawback of trapping into local minima easily when used for the optimization of high-dimension complex functions with a lot of local minima. In order to deal with the problem an improved PSO algorithm with crossover operator is developed. Better particles are selected in this algorithm, thus can avoid premature convergence to local optimum as well as accelerate the convergence speed. Four high-dimension complex benchmark functions are introduced to test this method. Simulation analysis shows that improved PSO algorithm has better capabilities in convergence accuracy and speed as well as its global search performance by comparison with normal PSO algorithms. Finally the improved PSO based neural network (NN) soft sensor model for ethylene yield is developed, results of the application in industrial process control show that this model has high prediction precision and good generalization ability, it can satisfy the need of spot measurement.

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Study on Improved Particle Swarm Optimization Algorithm and Its Application

Cited by 4 publications

References 4 publications

Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling

Positioning failure error identification of industrial robots based on particle swarm optimization and Kriging surrogate modeling

A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

Soft sensor modeling based on modified PSO

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