Particle Swarm Optimization (PSO) algorithm for wind farm optimal design

A real-time dispatch (RTD) model for wind power incorporated power system aimed at maximizing wind power utilization and minimizing fuel cost is proposed in this paper. To cope with the prematurity and local convergence of conventional particle swarm optimization (PSO) algorithm, a novel adaptive chaos quantum-behaved particle swarm optimization (ACQPSO) algorithm is put forward. The adaptive inertia weight and chaotic perturbation mechanism are employed to improve the particle’s search efficiency. Numerical simulation on a 10 unit system with a wind farm demonstrates that the proposed model can maximize wind power utilization while ensuring the safe and economic operation of the power system. The proposed ACQPSO algorithm is of good convergence quality and the computation speed can meet the requirement of RTD.

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The effect of acceleration coefficients in Particle Swarm Optimization algorithm with application to wind farm layout design

FME Transaction ◽

10.5937/fme2004922r ◽

2020 ◽

Vol 48 (4) ◽

pp. 922-930

Author(s):

Shafiqur Rehman ◽

Salman Khan ◽

Luai Alhems

Keyword(s):

Particle Swarm Optimization ◽

Design Problem ◽

Wind Farm ◽

Particle Swarm ◽

Pso Algorithm ◽

Layout Design ◽

Energy Output ◽

Swarm Optimization ◽

Wind Farm Layout ◽

The Impact

Wind energy has become a strong alternative to traditional sources of energy. One important decision for an efficient wind farm is the optimal layout design. This layout governs the placement of turbines in a wind farm. The inherent complexity involved in this process results in the wind farm layout design problem to be a complex optimization problem. Particle Swarm Optimization (PSO) algorithm has been effectively used in many studies to solve the wind farm layout design problem. However, the impact of an important set of PSO parameters, namely, the acceleration coefficients, has not received due attention. Considering the importance of these parameters, this paper presents a preliminary analysis of PSO acceleration coefficients using the conventional and a modified variant of PSO when applied to wind farm layout design. Empirical results show that the acceleration coefficients do have an impact on the quality of final layout, resulting in better overall energy output.

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Random Number Generator Based On Hybrid Algorithm Between Particle Swarm Optimization (PSO) Algorithm And 3D-Chaotic System And Its Application

10.34279/0923-008-003-005 ◽

2018 ◽

pp. 1

Author(s):

Rajaa Ahmed Ali

Keyword(s):

Particle Swarm Optimization ◽

Chaotic System ◽

Hybrid Algorithm ◽

Random Number ◽

Random Number Generator ◽

Particle Swarm ◽

Pso Algorithm ◽

Number Generator ◽

Swarm Optimization

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Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽

10.3390/s21082868 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2868

Author(s):

Gong Cheng ◽

Huangfu Wei

Keyword(s):

Particle Swarm Optimization ◽

Sensor Networks ◽

Particle Swarm ◽

Pso Algorithm ◽

The Internet ◽

Directional Sensor Networks ◽

Swarm Optimization ◽

Coverage Optimization ◽

Boundary Constraint ◽

Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

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Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201644 ◽

2021 ◽

pp. 1-17

Author(s):

J. Shobana ◽

M. Murali

Keyword(s):

Particle Swarm Optimization ◽

Sentiment Analysis ◽

Language Processing ◽

Short Term Memory ◽

Contextual Information ◽

Particle Swarm ◽

Pso Algorithm ◽

Swarm Optimization ◽

Adaptive Particle Swarm Optimization ◽

Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

Complex & Intelligent Systems ◽

10.1007/s40747-020-00252-2 ◽

2021 ◽

Author(s):

Na Geng ◽

Zhiting Chen ◽

Quang A. Nguyen ◽

Dunwei Gong

Keyword(s):

Particle Swarm Optimization ◽

Task Allocation ◽

Optimal Algorithm ◽

Particle Swarm ◽

Evolutionary Process ◽

Pso Algorithm ◽

Swarm Optimization ◽

Fitness Evaluation ◽

Modified Particle Swarm Optimization ◽

Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

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Multiple RFI Sources Location Method Combining Two-Dimensional ESPRIT DOA Estimation and Particle Swarm Optimization for Spaceborne SAR

Remote Sensing ◽

10.3390/rs13061207 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1207

Author(s):

Junfei Yu ◽

Jingwen Li ◽

Bing Sun ◽

Yuming Jiang ◽

Liying Xu

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Geographic Location ◽

Doa Estimation ◽

Pso Algorithm ◽

Two Dimensional ◽

Swarm Optimization ◽

Location Method ◽

Esprit Algorithm ◽

To Receive

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

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Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

Applied Sciences ◽

10.3390/app11020839 ◽

2021 ◽

Vol 11 (2) ◽

pp. 839

Author(s):

Shaofei Sun ◽

Hongxin Zhang ◽

Xiaotong Cui ◽

Liang Dong ◽

Muhammad Saad Khan ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Communication Systems ◽

Optimization Problems ◽

Classical Method ◽

Particle Swarm ◽

Pso Algorithm ◽

Electromagnetic Analysis ◽

Swarm Optimization ◽

Test Board

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm’s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case.

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