scholarly journals Conformal Array Pattern Synthesis and Activated Elements Selection Strategy Based on PSOGSA Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Bin Sun ◽  
Chunheng Liu ◽  
Yang Liu ◽  
Xiaofang Wu ◽  
Yongzhen Li ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Selection Strategy ◽  
Pattern Synthesis ◽  
Conformal Array ◽  
Hybrid Particle Swarm Optimization ◽  
Element Pattern ◽  
Novel Strategy ◽  
Element Selection

The pattern synthesis and activated element selection for conformal array is investigated based on hybrid particle swarm optimization-gravitational search algorithm (PSOGSA) in this paper. With the introduction of PSOGSA algorithm which is a novel hybrid optimization technique, the element excitations are optimized to obtain the desired pattern for conformal array in the case of considering uncoupled and coupled element pattern. Numerical simulation and full-wave electromagnetic calculation verify the advantage and efficiency of our method. Then, a novel strategy of activated element selection based on PSOGSA algorithm is proposed for saving the energy consumption in conformal array.

Neural Network-based Model Predictive Control with CPSOGSA for SMBR Filtration

2017 ◽  
Vol 7 (3) ◽  
pp. 1538 ◽  
Author(s):  
Zakariah Yusuf ◽  
Norhaliza Abdul Wahab ◽  
Abdallah Abusam
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Membrane Fouling ◽  
Pid Controller ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Permeate Flux ◽  
Hybrid Particle Swarm Optimization

This paper presents the development of neural network based model predictive control (NNMPC) for controlling submerged membrane bioreactor (SMBR) filtration process.The main contribution of this paper is the integration of newly developed soft computing optimization technique name as cooperative hybrid particle swarm optimization and gravitational search algorithm (CPSOGSA) with the model predictive control. The CPSOGSA algorithm is used as a real time optimization (RTO) in updating the NNMPC cost function. The developed controller is utilized to control SMBR filtrations permeate flux in preventing flux decline from membrane fouling. The proposed NNMPC is comparedwith proportional integral derivative (PID) controller in term of the percentage overshoot, settling time and integral absolute error (IAE) criteria. The simulation result shows NNMPC perform better control compared with PID controller in term measured control performance of permeate flux.

Gravitational search algorithm for synthesis of selectively thinned concentric ring array antenna with minimum sidelobe level and with fixed and variable first null beamwidth

2014 ◽  
Vol 7 (6) ◽  
pp. 775-781 ◽  
Author(s):  
Anirban Chatterjee ◽  
Gautam Kumar Mahanti ◽  
Narendra Nath Pathak
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Synthesis Method ◽  
Computational Time ◽  
Sidelobe Level ◽  
Concentric Ring ◽  
Pattern Synthesis ◽  
First Case ◽  
Gravitational Search ◽  
First Null Beamwidth

Thinning a large concentric ring array by an evolutionary algorithm needs to handle a large amount of variables. The computational time to find out the optimum elements set increases with the increase of array size. Moreover, thinning significantly reduces the directivity of the array. In this paper, the authors propose a pattern synthesis method to reduce the peak sidelobe level (peak SLL) while keeping first null beamwidth (FNBW) of the array fixed by thinning the outermost rings of the array based on Gravitational Search Algorithm (GSA). Two different cases have been studied. In the first case only the outermost ring of the array is thinned and in the second case the two outermost rings are thinned. The FNBW of the optimized array is kept equal to or less than that of a fully populated, uniformly excited and 0.5 λ spaced concentric ring array of same number of elements and rings. The directivity of the optimized array for the above two cases are compared with an array optimized by thinning all the rings, while keeping the design criteria same as the above two cases. The optimized array by thinning the outermost rings gives higher directivity over the optimized array by thinning all the rings. Time required for computing the optimum elements state for the above two cases using GSA are shown lesser compared to the optimized array by thinning all the rings using the same algorithm. The peak SLL and the FNBW of the optimized array for the above two cases are also compared with the optimized array by thinning all the rings.

scholarly journals A Multi-Objective Optimization Dispatch Method for Microgrid Energy Management Considering the Power Loss of Converters

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2160 ◽  
Author(s):  
Xiaomin Wu ◽  
Weihua Cao ◽  
Dianhong Wang ◽  
Min Ding
Keyword(s):  
Power Loss ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Inertial Mass ◽  
Real Time Scheduling ◽  
Opposition Based Learning ◽  
Hybrid Particle Swarm Optimization ◽  
Time Scheduling ◽  
Benchmark Test Functions ◽  
Energy Conversion Devices

With the spreading and applying of microgrids, the economic and environment friendly microgrid operations are required eagerly. For the dispatch of practical microgrids, power loss from energy conversion devices should be considered to improve the efficiency. This paper presents a two-stage dispatch (TSD) model based on the day-ahead scheduling and the real-time scheduling to optimize dispatch of microgrids. The power loss cost of conversion devices is considered as one of the optimization objectives in order to reduce the total cost of microgrid operations and improve the utility efficiency of renewable energy. A hybrid particle swarm optimization and opposition-based learning gravitational search algorithm (PSO-OGSA) is proposed to solve the optimization problem considering various constraints. Some improvements of PSO-OGSA, such as the distribution optimization of initial populations, the improved inertial mass update rule, and the acceleration mechanism combining the memory and community of PSO, have been integrated into the proposed approach to obtain the best solution for the optimization dispatch problem. The simulation results for several benchmark test functions and an actual test microgrid are employed to show the effectiveness and validity of the proposed model and algorithm.

scholarly journals Performance Comparison of Particle Swarm Optimization and Gravitational Search Algorithm to the Designed of Controller for Nonlinear System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sahazati Md Rozali ◽  
Mohd Fua’ad Rahmat ◽  
Abdul Rashid Husain
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Tracking Error ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Backstepping Controller ◽  
Gravitational Search

This paper presents backstepping controller design for tracking purpose of nonlinear system. Since the performance of the designed controller depends on the value of control parameters, gravitational search algorithm (GSA) and particle swarm optimization (PSO) techniques are used to optimise these parameters in order to achieve a predefined system performance. The performance is evaluated based on the tracking error between reference input given to the system and the system output. Then, the efficacy of the backstepping controller is verified in simulation environment under various system setup including both the system subjected to external disturbance and without disturbance. The simulation results show that backstepping with particle swarm optimization technique performs better than the similar controller with gravitational search algorithm technique in terms of output response and tracking error.

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