scholarly journals Constrained Fuzzy Predictive Control Using Particle Swarm Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Oussama Ait Sahed ◽  
Kamel Kara ◽  
Mohamed Laid Hadjili
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Tracking Error ◽  
Particle Swarm ◽  
Search Space ◽  
Phase Behaviour ◽  
Computational Time ◽  
Distribution Law ◽  
Swarm Optimization ◽  
Practical Applications

A fuzzy predictive controller using particle swarm optimization (PSO) approach is proposed. The aim is to develop an efficient algorithm that is able to handle the relatively complex optimization problem with minimal computational time. This can be achieved using reduced population size and small number of iterations. In this algorithm, instead of using the uniform distribution as in the conventional PSO algorithm, the initial particles positions are distributed according to the normal distribution law, within the area around the best position. The radius limiting this area is adaptively changed according to the tracking error values. Moreover, the choice of the initial best position is based on prior knowledge about the search space landscape and the fact that in most practical applications the dynamic optimization problem changes are gradual. The efficiency of the proposed control algorithm is evaluated by considering the control of the model of a 4 × 4 Multi-Input Multi-Output industrial boiler. This model is characterized by being nonlinear with high interactions between its inputs and outputs, having a nonminimum phase behaviour, and containing instabilities and time delays. The obtained results are compared to those of the control algorithms based on the conventional PSO and the linear approach.

Method of Product Innovation Acquisition Based on Dynamic Response Particle Swarm Optimization

2012 ◽  
Vol 542-543 ◽  
pp. 3-7 ◽  
Author(s):  
Yan Chao Yin ◽  
Ya Yu Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Product Innovation ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Front End ◽  
Mining Algorithm ◽  
Innovative Thinking ◽  
Appearance Feature

In order to accurately locate the changing demands and objects of customers, a novel mining algorithm was presented to predict the Optimum Customer Demand (OCD) in the uncertain front end, which can be formulated as a dynamic nonlinear optimization problem. The improved Particle Swarm Optimization involves accurately detecting the changes all of the search space and reliably updating obsolete particle memories, which has been shown to be effective in locating the changing plentiful innovative thinking. Finally, the algorithm is applied into the prediction of customer demands in car’s appearance feature, which effectively guide the implementation of product innovation and greatly improve the front end performance.

scholarly journals Fuel-Efficient on-Orbit Service Vehicle Allocation Based on an Improved Discrete Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Wu Jian ◽  
Liu Qingguo ◽  
Liu Xinxue ◽  
Li Yaxiong
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Performance Index ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Computational Time ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

Given the limited fuel capacity of an on-orbit service vehicle (OSV), proper OSV allocation to satellites during each service mission is critical for economic fuel consumption. This allocation problem can be formulated as an optimization problem with many continuous and discrete design variables of wide domains. This problem can be effectively handled through the proposed approach that combines the tabu search with the discrete particle swarm optimization algorithm (DPSO-TS). First of all, Pontryagin’s minimum principle and genetic algorithm (GA) are exploited to find the most fuel-efficient transfer trajectory. This fuel efficiency maximization can then serve as the performance index of the OSV allocation optimization model problem. In particular, the maximization of the minimum residual fuel over individual OSVs is proposed as a performance index for OSV allocation optimization. The optimization problem is numerically solved through the proposed DPSO-TS algorithm. Finally, the simulation results demonstrate that the DPSO-TS algorithm has a higher accuracy compared to the DPSO, the DPSO-PDM and the DPSO-CSA algorithms in the premise that these four algorithms have the basically same computational time. The DPSO-TS algorithm can effectively solve the OSV allocation optimization problem.

scholarly journals Dynamic Neighborhood-Based Particle Swarm Optimization for Multimodal Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xu-Tao Zhang ◽  
Biao Xu ◽  
Wei Zhang ◽  
Jun Zhang ◽  
Xin-fang Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Multimodal Optimization ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Benchmark Instances ◽  
Multimodal Problems

Various black-box optimization problems in real world can be classified as multimodal optimization problems. Neighborhood information plays an important role in improving the performance of an evolutionary algorithm when dealing with such problems. In view of this, we propose a particle swarm optimization algorithm based on dynamic neighborhood to solve the multimodal optimization problem. In this paper, a dynamic ε-neighborhood selection mechanism is first defined to balance the exploration and exploitation of the algorithm. Then, based on the information provided by the neighborhoods, four different particle position updating strategies are designed to further support the algorithm’s exploration and exploitation of the search space. Finally, the proposed algorithm is compared with 7 state-of-the-art multimodal algorithms on 8 benchmark instances. The experimental results reveal that the proposed algorithm is superior to the compared ones and is an effective method to tackle multimodal optimization problems.

scholarly journals DIMENSIONED PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER OPTIMIZATION PROBLEM

2018 ◽  
Vol 6 (4) ◽  
pp. 281-290
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Search Space ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Local Optimal Solution

This paper present’s Dimensioned Particle Swarm Optimization (DPSO) algorithm for solving Reactive power optimization (RPO) problem.  Dimensioned extension is introduced to particles in the particle swarm optimization (PSO) model in order to overcome premature convergence in interactive optimization. In the performance of basic PSO often flattens out with a loss of diversity in the search space as resulting in local optimal solution.  Proposed algorithm has been tested in standard IEEE 57 test bus system and compared to other standard algorithms. Simulation results reveal about the best performance of the proposed algorithm in reducing the real power loss and voltage profiles are within the limits.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

scholarly journals Improved Quantum Particle Swarm Optimization for Mangroves Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Zhehuang Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Stable Convergence ◽  
Network Parameter ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Quantum Particle Swarm Optimization

Quantum particle swarm optimization (QPSO) is a population based optimization algorithm inspired by social behavior of bird flocking which combines the ideas of quantum computing. For many optimization problems, traditional QPSO algorithm can produce high-quality solution within a reasonable computation time and relatively stable convergence characteristics. But QPSO algorithm also showed some unsatisfactory issues in practical applications, such as premature convergence and poor ability in global optimization. To solve these problems, an improved quantum particle swarm optimization algorithm is proposed and implemented in this paper. There are three main works in this paper. Firstly, an improved QPSO algorithm is introduced which can enhance decision making ability of the model. Secondly, we introduce synergetic neural network model to mangroves classification for the first time which can better handle fuzzy matching of remote sensing image. Finally, the improved QPSO algorithm is used to realize the optimization of network parameter. The experiments on mangroves classification showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

scholarly journals Convergence Analysis of Particle Swarm Optimizer and Its Improved Algorithm Based on Velocity Differential Evolution

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hongtao Ye ◽  
Wenguang Luo ◽  
Zhenqiang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Velocity ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Relationship Of ◽  
The Relationship ◽  
Improved Algorithm

This paper presents an analysis of the relationship of particle velocity and convergence of the particle swarm optimization. Its premature convergence is due to the decrease of particle velocity in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. An improved algorithm which introduces a velocity differential evolution (DE) strategy for the hierarchical particle swarm optimization (H-PSO) is proposed to improve its performance. The DE is employed to regulate the particle velocity rather than the traditional particle position in case that the optimal result has not improved after several iterations. The benchmark functions will be illustrated to demonstrate the effectiveness of the proposed method.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

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