Analysis of a folded reflect-array antenna using particle swarm optimization

2011 ◽  
Vol 3 (3) ◽  
pp. 267-272 ◽  
Author(s):  
Sabine Dieter ◽  
Christoph Fischer ◽  
Wolfgang Menzel
Keyword(s):  
Fitness Function ◽  
Good Choice ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Specific Element ◽  
Design And Optimization ◽  
Antenna Performance ◽  
Array Antennas ◽  
Field Radiation ◽  
Phase Truncation

In this paper, a method for design and optimization of folded reflect-array antennas is proposed based on particleswarm optimization (PSO). In addition to such a powerful optimization algorithm, two further requirements have to be fulfilled. The first one is a good and fast algorithm for the exact prognosis of the far-field radiation diagram, resulting from a specific element configuration on the reflector. Additionally, a good choice of the fitness function for the evaluation of the resulting radiation diagrams is necessary. In both, reflect-array-related aspects such as phase truncation, reflection losses, and cell discontinuities have to be considered. Antenna optimization based on this technique is presented in this paper at the example of two 77 GHz folded reflect-array antennas. The efficiency of this approach is demonstrated with these examples, and the results are verified by measurement, showing an excellent agreement with the specifications of the diagram masks. The implemented tool, including a realistic antenna diagram preview, allows the investigation of the design parameters’ influence on the antenna performance, such as illumination amplitude, high substrate losses, and phase truncation.

Design and optimization of bump (compression surface) for diverterless supersonic inlet

2021 ◽  
pp. 095441002110029
Author(s):  
Irsalan Arif ◽  
Hassan Iftikhar ◽  
Ali Javed
Keyword(s):  
Fitness Function ◽  
Supersonic Jet ◽  
Three Dimensional ◽  
Flow Characteristics ◽  
Least Square ◽  
Pressure Recovery ◽  
Design Parameters ◽  
Inlet System ◽  
Design And Optimization ◽  
Supersonic Inlet

In this article design and optimization scheme of a three-dimensional bump surface for a supersonic aircraft is presented. A baseline bump and inlet duct with forward cowl lip is initially modeled in accordance with an existing bump configuration on a supersonic jet aircraft. Various design parameters for bump surface of diverterless supersonic inlet systems are identified, and design space is established using sensitivity analysis to identify the uncertainty associated with each design parameter by the one-factor-at-a-time approach. Subsequently, the designed configurations are selected by performing a three-level design of experiments using the Box–Behnken method and the numerical simulations. Surrogate modeling is carried out by the least square regression method to identify the fitness function, and optimization is performed using genetic algorithm based on pressure recovery as the objective function. The resultant optimized bump configuration demonstrates significant improvement in pressure recovery and flow characteristics as compared to baseline configuration at both supersonic and subsonic flow conditions and at design and off-design conditions. The proposed design and optimization methodology can be applied for optimizing the bump surface design of any diverterless supersonic inlet system for maximizing the intake performance.

scholarly journals Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

Aerospace ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Shuangxi Liu ◽  
Fengping Huang ◽  
Binbin Yan ◽  
Tong Zhang ◽  
Ruifan Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Index System ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Evaluation Index System ◽  
Design Parameters ◽  
Swarm Optimization ◽  
Adaptive Pso

In an effort to maximize the combat effectiveness of multimissile groups, this paper proposes an adaptive simulated annealing–particle swarm optimization (SA-PSO) algorithm to enhance the design parameters of multimissile formations based on the concept of missile cooperative engagement. Firstly, considering actual battlefield circumstances, we establish an effectiveness evaluation index system for the cooperative engagement of missile formations based on the analytic hierarchy process (AHP). In doing so, we adopt a partial triangular fuzzy number method based on authoritative assessments by experts to ascertain the weight of each index. Then, considering given constraints on missile performance, by selecting the relative distances and angles of the leader and follower missiles as formation parameters, we design a fitness function corresponding to the established index system. Finally, we introduce an adaptive capability into the traditional particle swarm optimization (PSO) algorithm and propose an adaptive SA-PSO algorithm based on the simulated annealing (SA) algorithm to calculate the optimal formation parameters. A simulation example is presented for the scenario of optimizing the formation parameters of three missiles, and comparative experiments conducted with the traditional and adaptive PSO algorithms are reported. The simulation results indicate that the proposed adaptive SA-PSO algorithm converges faster than both the traditional and adaptive PSO algorithms and can quickly and effectively solve the multimissile formation optimization problem while ensuring that the optimized formation satisfies the given performance constraints.

scholarly journals Optimization of Fuzzy Logic Controller by Using Heuristic Algorithms

2019 ◽  
pp. 13-18
Author(s):  
Sinan Unsal ◽  
Ibrahim Aliskan
Keyword(s):  
Fuzzy Logic ◽  
Systematic Approach ◽  
Fuzzy Logic Controller ◽  
Heuristic Algorithms ◽  
Algorithm Design ◽  
Optimal Solution ◽  
Design Parameters ◽  
Fuzzy Logic Controllers ◽  
Swarm Optimization ◽  
Design And Optimization

There are many design parameters in the structure of fuzzy logic controllers. Conventional methods that don't have a systematic approach are often used in determining of these parameters. However, setting the controller parameters in this way leads to long experiments and this takes a lot of time. For this reason, design parameters of the fuzzy logic controller are usually determined by using heuristic algorithms. Because, heuristic algorithms can offer solutions that are very close to the optimal solution for the problems where exact solution cannot be obtained. In this study, output membership functions of a fuzzy logic controller are optimized using particle swarm optimization and genetic algorithm. Design and optimization stages are explained in detail and results are compared with each other.

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

scholarly journals Geometric Design-based Dimensional Synthesis of a Novel Metamorphic Multi-fingered Hand with Maximal Workspace

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Wei An ◽  
Jun Wei ◽  
Xiaoyu Lu ◽  
Jian S. Dai ◽  
Yanzeng Li
Keyword(s):  
Geometric Design ◽  
Practical Significance ◽  
Design Parameters ◽  
Control Algorithms ◽  
Discretization Method ◽  
Dimensional Synthesis ◽  
Symmetrical Structure ◽  
Design And Optimization ◽  
Total Length

AbstractCurrent research on robotic dexterous hands mainly focuses on designing new finger and palm structures, as well as developing smarter control algorithms. Although the dimensional synthesis of dexterous hands with traditional rigid palms has been carried out, research on the dimensional synthesis of dexterous hands with metamorphic palms remains insufficient. This study investigated the dimensional synthesis of a palm of a novel metamorphic multi-fingered hand, and explored the geometric design for maximizing the precision manipulation workspace. Different indexes were used to value the workspace of the metamorphic hand, and the best proportions between the five links of the palm to obtain the optimal workspace of the metamorphic hand were explored. Based on the fixed total length of the palm member, four nondimensional design parameters that determine the size of the palm were introduced; through the discretization method, the influence of the four design parameters on the workspace of the metamorphic hand with full-actuated fingers and under-actuated fingers was analyzed. Based on the analysis of the metamorphic multi-fingered hand, the symmetrical structure of the palm was designed, resulting in the largest workspace of the multi-fingered hand, and proved that the metamorphic palm has a massive upgrade for the workspace of underactuated fingers. This research contributed to the dimensional synthesis of metamorphic dexterous hands, with practical significance for the design and optimization of novel metamorphic hands.

scholarly journals Design and optimization of differential capacitive micro accelerometer for vibration measurement

2021 ◽  
Vol 30 (1) ◽  
pp. 19-27
Author(s):  
Kumar Gomathi ◽  
Arunachalam Balaji ◽  
Thangaraj Mrunalini
Keyword(s):  
Software Design ◽  
Proof Mass ◽  
Vibration Measurement ◽  
Design Parameters ◽  
Mechanical Sensitivity ◽  
Design And Optimization ◽  
Capacitive Accelerometer ◽  
Cross Axis ◽  
Micro Accelerometer

Abstract This paper deals with the design and optimization of a differential capacitive micro accelerometer for better displacement since other types of micro accelerometer lags in sensitivity and linearity. To overcome this problem, a capacitive area-changed technique is adopted to improve the sensitivity even in a wide acceleration range (0–100 g). The linearity is improved by designing a U-folded suspension. The movable mass of the accelerometer is designed with many fingers connected in parallel and suspended over the stationary electrodes. This arrangement gives the differential comb-type capacitive accelerometer. The area changed capacitive accelerometer is designed using Intellisuite 8.6 Software. Design parameters such as spring width and radius, length, and width of the proof mass are optimized using Minitab 17 software. Mechanical sensitivity of 0.3506 μm/g and Electrical sensitivity of 4.706 μF/g are achieved. The highest displacement of 7.899 μm is obtained with a cross-axis sensitivity of 0.47%.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Optimal Design and Control of a Slider-Crank Mechanism System

2012 ◽  
Vol 487 ◽  
pp. 608-612 ◽  
Author(s):  
Chih Cheng Kao
Keyword(s):  
Particle Swarm Optimization ◽  
System Identification ◽  
Optimal Design ◽  
Fitness Function ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Pm Synchronous Motor ◽  
And Control ◽  
Crank Mechanism

This paper mainly proposes an efficient modified particle swarm optimization (MPSO) method, to identify a slider-crank mechanism driven by a field-oriented PM synchronous motor. The parameters of many industrial machines are difficult to obtain if these machines cannot be taken apart. In system identification, we adopt the MPSO method to find parameters of the slider-crank mechanism. This new algorithm is added with “distance” term in the traditional PSO’s fitness function to avoid converging to a local optimum. Finally, the comparisons of numerical simulations and experimental results prove that the MPSO identification method for the slider-crank mechanism is feasible.

An Efficient Chaotic Salp Swarm Optimization Approach Based on Ensemble Algorithm for Solving Class Imbalance Problem

2021 ◽  
Author(s):  
Rekha G ◽  
Krishna Reddy V ◽  
chandrashekar jatoth ◽  
Ugo Fiore
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Class Imbalance ◽  
Optimization Approach ◽  
Class Imbalance Problem ◽  
Swarm Optimization ◽  
Adaboost Algorithm ◽  
Wide Range

Abstract Class imbalance problems have attracted the research community but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an Adaboost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization and Adaboost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of Adaboost.

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