Particle swarm optimization algorithm based on chaos cloud model

Zhang, Chaolong; Chun-ri, YU; Jiang, Shanhe; Liu, Quanjin; Wu, Wenjin; Li, Yanmei

doi:10.3724/sp.j.1087.2012.01951

Cited by 6 publications

(4 citation statements)

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“…Particle swarm can be prevented from falling into local optimal solution by replacing the global optimal value g p of the standard PSO with the sub-particle swarm optimal value sub g p _ . The modified PSO equation is as follows [8]:…”

Section: B Improvement In the Global Optimal Valuementioning

confidence: 99%

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

Zhao

Wang

et al. 2013

AMR

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Adaptive infrared image contrast enhancement is presented based on modified particle swarm optimization (PSO) and incomplete Beta Function. On the basis of traditional PSO, modified PSO integrates into the theory of Multi-Particle Swarm and evolution theory algorithm. By using separate search space optimal solution of multiple particles, the global search ability is improved. And in the iteration procedures, timely adjustment of acceleration coefficients is convenient for PSO to find the global optimal solution in the later iteration. Through infrared image simulation, experimental results show that the modified PSO is better than the standard PSO in computing speed and convergence.

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Section: B Improvement In the Global Optimal Valuementioning

confidence: 99%

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

Zhao

Wang

et al. 2013

AMR

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“…Characterized by the coexistence of the uncertainty and certainty as well as the stability during the course of knowledge representation, the cloud model has reflected the fundamental principle of the biological evolution in nature [19][20][21]. By virtue of its characteristics such as easy implementation of derivation process, simple rules, and strong robustness, it has been widely utilized in the field of intelligent control and subjective evaluation [22]. Normal cloud model is one of the most important among cloud models.…”

Section: Introductionmentioning

confidence: 99%

Path Tracking Control of Automatic Parking Cloud Model considering the Influence of Time Delay

Hua

Jiang

Cai

et al. 2017

Mathematical Problems in Engineering

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This paper establishes the kinematic model of the automatic parking system and analyzes the kinematic constraints of the vehicle. Furthermore, it solves the problem where the traditional automatic parking system model fails to take into account the time delay. Firstly, based on simulating calculation, the influence of time delay on the dynamic trajectory of a vehicle in the automatic parking system is analyzed under the transverse distance Dlateral between different target spaces. Secondly, on the basis of cloud model, this paper utilizes the tracking control of an intelligent path closer to human intelligent behavior to further study the Cloud Generator-based parking path tracking control method and construct a vehicle path tracking control model. Moreover, tracking and steering control effects of the model are verified through simulation analysis. Finally, the effectiveness and timeliness of automatic parking controller in the aspect of path tracking are tested through a real vehicle experiment.

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“…In recent years, the cloud model has been introduced and combined with a number of algorithms such as GA, PSO, and evolution algorithm [47][48][49] According to the principle of sociology, the more excellent individual often appears around the current optimal individual in subpopulations. In other words, the better frog is found with great probability in the vicinity of the current best frog.…”

Section: The Elite Evolution Strategy For Leader Frog Subpopulation Bmentioning

confidence: 99%

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

Yang

et al. 2018

Mathematical Problems in Engineering

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The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

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Particle swarm optimization algorithm based on chaos cloud model

Cited by 6 publications

References 0 publications

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

Infrared Image Contrast Enhancement Based on Modified Particle Swarm Optimization

Path Tracking Control of Automatic Parking Cloud Model considering the Influence of Time Delay

The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

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