Minimizing sidelobe levels and facilitating null placements of nonlinear antenna arrays using an improved particle swarm optimization method

2013 ◽  
Vol 33 (1/2) ◽  
pp. 65-73 ◽  
Author(s):  
Huijun Deng ◽  
Xue Li ◽  
Libao Sun ◽  
Shiyou Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Antenna Array ◽  
Antenna Arrays ◽  
Particle Swarm ◽  
Optimization Method ◽  
Entropy Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Cross Entropy Method ◽  
Improved Pso

Purpose – The aim of this paper is to explore the potential of particle swarm optimization (PSO) methods for minimizing the sidelobe levels (SLL) and placing null at arbitrary angles of a nonlinear antenna array. Design/methodology/approach – An improved PSO algorithm is designed. Findings – The improved PSO method is an efficient and robust global optimizer for minimizing the SLL and placing null at arbitrary angles of a nonlinear antenna array. Originality/value – Some improvements, such as the design of some new formulae for both position and velocity updating, the introduction of an age variable, and the devise of an intensification searches using the cross entropy method, are proposed.

A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 304-311 ◽  
Author(s):  
Pengfei Jia ◽  
Fengchun Tian ◽  
Shu Fan ◽  
Qinghua He ◽  
Jingwei Feng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Wound Infection ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Sensor Array ◽  
Particle Swarm ◽  
Optimization Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type

Purpose – The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach – An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings – The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications – To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications – In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value – The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

Hypothesis testing-based adaptive PSO

2014 ◽  
Vol 12 (1) ◽  
pp. 89-101 ◽  
Author(s):  
Yanxia Sun ◽  
Karim Djouani ◽  
Barend Jacobus van Wyk ◽  
Zenghui Wang ◽  
Patrick Siarry
Keyword(s):  
Particle Swarm Optimization ◽  
Hypothesis Testing ◽  
Local Minimum ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Engineering Optimization ◽  
Swarm Optimization ◽  
Content Type ◽  
Adaptive Pso

Purpose – In this paper, a new method to improve the performance of particle swarm optimization is proposed. Design/methodology/approach – This paper introduces hypothesis testing to determine whether the particles trap into the local minimum or not, then special re-initialization was proposed, finally, some famous benchmarks and constrained engineering optimization problems were used to test the efficiency of the proposed method. In the revised manuscript, the content was revised and more information was added. Findings – The proposed method can be easily applied to PSO or its varieties. Simulation results show that the proposed method effectively enhances the searching quality. Originality/value – This paper proposes an adaptive particle swarm optimization method (APSO). A technique is applied to improve the global optimization performance based on the hypothesis testing. The proposed method uses hypothesis testing to determine whether the particles are trapped into local minimum or not. This research shows that the proposed method can effectively enhance the searching quality and stability of PSO.

Reentry guidance by accelerated fractional-order particle swarm optimization method

2020 ◽  
Vol 92 (8) ◽  
pp. 1281-1293
Author(s):  
Khurram Shahzad Sana ◽  
Weiduo Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Initial Phase ◽  
Particle Swarm ◽  
Optimization Method ◽  
Test Case ◽  
Swarm Optimization ◽  
Content Type ◽  
Flight Vehicles ◽  
Path Constraints

Purpose The aim of this study is to design a guidance method to generate a smoother and feasible gliding reentry trajectory, a highly constrained problem by formalizing the control variables profile. Design/methodology/approach A novel accelerated fractional-order particle swarm optimization (FAPSO) method is proposed for velocity updates to design the guidance method for gliding reentry flight vehicles with fixed final energy. Findings By using the common aero vehicle as a test case for the simulation purpose, it is found that during the initial phase of the longitudinal guidance, there are oscillations in the state parameters which cause to violate the path constraints. For the glide phase of the longitudinal guidance, the path constraints have higher values because of the increase in the atmosphere density. Research limitations/implications The violation in the path constraints may compromise the flight vehicle safety, whereas the enforcement assures the flight safety by flying it within the reentry corridor. Originality/value An oscillation suppression scheme is proposed by using the FAPSO method during the initial phase of the reentry flight, which smooths the trajectory and enforces the path constraints partially. To enforce the path constraints strictly in the glide phase, ultimately, another scheme by using the FAPSO method is proposed. The simulation results show that the proposed algorithm is efficient to achieve better convergence and accuracy for nominal as well as dispersed conditions.

Optimization of circular antenna arrays of isotropic radiators using simulated annealing

2009 ◽  
Vol 1 (5) ◽  
pp. 441-446 ◽  
Author(s):  
Munish Rattan ◽  
M.S. Patterh ◽  
B.S. Sohi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Antenna Arrays ◽  
Particle Swarm ◽  
Excitation Amplitude ◽  
Optimization Method ◽  
Sidelobe Level ◽  
Swarm Optimization ◽  
Circular Antenna Arrays

This paper presents the design optimization of circular antenna arrays of isotropic radiators using simulated annealing. The problem has been formulated to achieve a desired value of sidelobe level and a minimum possible value of beamwidth. This is accomplished by jointly optimizing the excitation amplitude and spacing between elements. Simulation examples have been given and comparison has been carried out with particle swarm optimization method.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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