Monopulse Cassegrain antenna design using evolutionary algorithms

Using evolutionary programming (EP), monopulse Cassegrain antennas with four feeds can be designed for the desired sum gain, side lobe level and minimum possible antenna size. A method is proposed to achieve the optimum monopulse difference pattern. cos^q(θ) type feeds are considered as the feed system and the final designs have been checked using real feeds. Proper cost functions are proposed to achieve the desired gain, side lobe level and optimum slope in the difference pattern, taking into account the feasibility of the feed system. The effects of the parameters involved in the optimization are investigated.     

基于进化规划求解最优通信生成树

本文分析了使用遗传算法求解最优通信生成树的缺陷,提出了基于进化规划求解最优通信生成树的新方法,并将这一方法扩展到有约束最优通信生成树问题。仿真结果验证了算法的有效性。     

Cognitive radio spectrum allocation using evolutionary algorithms

Cognitive radio has been regarded as a promising technology to improve spectrum utilization significantly. In this letter, spectrum allocation model is presented firstly, and then spectrum allocation methods based on genetic algorithm (GA), quantum genetic algorithm (QGA), and particle swarm optimization (PSO), are proposed. To decrease the search space we propose a mapping process between the channel assignment matrix and the chromosome of GA, QGA, and the position of the particle of PSO, respectively, based on the characteristics of the channel availability matrix and the interference constraints. Results show that our proposed methods greatly outperform the commonly used color sensitive graph coloring algorithm.     

Optimal design of a hyperstatic Stewart platform-based force/torque sensor with genetic algorithms

This paper proposes a novel hyperstatic six-component force/torque sensor structure based on the Stewart platform, and presents the parameter optimization of the sensor structure with genetic algorithms. The structure characteristic of the hyperstatic sensor is analyzed compared with the traditional Stewart platform-based sensor. The mathematical expression of the sensor’s force mapping matrix is built by using the screw theory. In this paper, the condition number and generalized amplifying index are used to evaluate the isotropy and sensitivity of the sensor, respectively. Based on genetic algorithms, the optimal design of the sensor structure is performed with the objective of achieving high measurement sensitivity and well-conditioned transformation between the input and output forces. The computed results prove the better performance of the proposed sensor structure and the effectiveness of the optimization algorithm.     

Optimal design of ball-screw driven servomechanisms through an integrated mechatronic approach

This paper proposes a novel model-based mechatronic approach for the design of ball-screw driven servomechanisms. The proposed technique is aimed at selecting the optimal combination of electric motor and ball-screw which minimizes the motor torque, while ensuring the achievement of the prescribed dynamic performances of the closed loop system. Such performances, as well as feasibility constraints related to the component characteristics, are translated in the method as bounds in the lead – diameter space. In particular, feasible torque and speed are the constraints posed by the motor; screw critical speed, ball critical speed, service life, buckling load are those due to the ball-screw, while the controlled system performances are included through the inertia ratio and the bandwidth requirements. To this purpose, the ball-screw non-ideal characteristics are represented through models with different complexity and are accounted for in the design. A straightforward graphical approach is formulated to trade off between the many conflicting requirements and to prevent both overly conservative and undersized design, while ensuring problem solvability.     

Images encryption by the use of evolutionary algorithms

Increasing information transmission in public networks raises a significant number of questions. For example, the security, the confidentiality, the integrity and the authenticity of the data during its transmission are very problematical. So, encryption of the transmitted data is one of the most promising solutions. In our work, we focus on the security of image data, which are considered as specific data because of their big size and their information which are of two-dimensional nature and also redundant. These data characteristics make the developed algorithms in the literature unavailable in their classical forms, because of the speed and the possible risk of information loss. In this paper, we develop an original “images encryption” algorithm based on evolutionary algorithms. The appropriateness of the proposed scheme is demonstrated by the sensitivity to images, the key and the resistibility to various advanced attacks.     

Multiobjective placement of electronic components using evolutionary algorithms

The optimal placement of electronic components on a printed circuit board is a well-studied optimization task. However, despite the involvement of multiple conflicting objectives, researchers have mainly used a single objective of minimizing the overall wire length or minimizing the overall heat generation or minimizing the overall time delay in its functioning. In this paper, the problem is treated as a two-objective optimization problem of minimizing the overall wire length and minimizing the failure-rate of the board arising due to uneven local heat accumulation. The proposed strategy uses a novel representation procedure and a multiobjective evolutionary algorithm capable of finding multiple Pareto-optimal solutions simultaneously. Moreover, the flexibility and efficacy of the proposed strategy have been demonstrated by simultaneously optimizing the placement of components and the layout of the board. The convergence and the extent of spread obtained in the solutions reliably by repetitive applications of the proposed procedure should encourage further application of the approach to more complex placement design problems.     

Optimal seismic deconvolution: distributed algorithms

Deconvolution is one of the most important aspects of seismic signal processing. The objective of the deconvolution procedure is to remove the obscuring effect of the wavelet's replica making up the seismic trace and therefore obtain an estimate of the reflection coefficient sequence. This paper introduces a new deconvolution algorithm. Optimal distributed estimators and smoothers are utilized in the proposed solution. The new distributed methodology, perfectly suitable for a multisensor environment, such as the seismic signal processing, is compared to the centralized approach, with respect to computational complexity and architectural efficiency. It is shown that the distributed approach greatly outperforms the currently used centralized methodology offering flexibility in the design of the data fusion network     

Modeling, design and characterization of a new low-jitter analog dual tuning LC-VCO PLL architecture

This paper describes the modeling, design, and characterization of a low-jitter 2.4-GHz LC-VCO PLL architecture realized in a standard 0.12-/spl mu/m CMOS technology. It features an analog dual control loop for fine and coarse VCO tuning that allows very low VCO gain (60 MHz/V) for noise rejection while maintaining a wide tuning range. The coarse input of the VCO is driven by an analog circuit that adjusts the VCO gain in a continuous manner. Measurements demonstrate an integrated jitter of 0.74 ps that is 43% lower compared to results from a standard PLL topology (STD PLL) with a single control loop. The PLLs have the same bandwidth and output frequency range and were built on the same wafer for comparison. The circuit area of the proposed LC-VCO PLL is 0.7 mm/sup 2/ and the power consumption is 32 mW. The area and power consumption of the proposed LC-VCO PLL are less than 1% larger compared to the STD PLL.     

Channel assignment through evolutionary optimization

The problem of assigning appropriate channels to the individual members of a cellular network is an important challenge facing network designers. Heuristics may be used to solve this problem, although in recent years parallel distributed methods have also been suggested. We investigate how an evolutionary inspired computing technique known as genetic algorithms (GAs) may be used. These global optimization techniques avoid many of the shortcomings exhibited by local search techniques on difficult search spaces. The new approach is tested on several problems of different sizes and complexity. The critical aspects of this technique and additional improvements are also discussed     

Human enhancement through evolutionary technology

The thrust of this article is this: There is a need now, more than ever before, for men to stretch their capacities in what we shall call evolutionary skills. Moreover, it is at last becoming possible technologically to enhance these skills in man by incorporating somewhat similar evolutionary skills in the machines which we design and build. However, if engineers are to develop machines with evolutionary capabilities, they will need to restructure their own way of thinking, throw out traditional ways of thinking, and find their way, through playing with evolutionary design techniques, into an ever-deepening understanding of the significance of such techniques. They must bootstrap themselves into a new kind of ``think,' into a new climate of man-machine interaction, in which men evolve intelligent machines and intelligent machines evolve men. This new kind of think is what this article tries to unfold in an effort to spur lively support for the evolutionary direction.     

Accelerating evolutionary algorithms with Gaussian process fitness function models

We present an overview of evolutionary algorithms that use empirical models of the fitness function to accelerate convergence, distinguishing between evolution control and the surrogate approach. We describe the Gaussian process model and propose using it as an inexpensive fitness function surrogate. Implementation issues such as efficient and numerically stable computation, exploration versus exploitation, local modeling, multiple objectives and constraints, and failed evaluations are addressed. Our resulting Gaussian process optimization procedure clearly outperforms other evolutionary strategies on standard test functions as well as on a real-world problem: the optimization of stationary gas turbine compressor profiles.     

Optimal data-based kernel estimation of evolutionary spectra

Complex demodulation of evolutionary spectra is formulated as a two-dimensional kernel smoother in the time-frequency domain. First, a tapered Fourier transform, y_v(f, t), is calculated. Then the log-spectral estimate, is smoothed. As the characteristic widths of the kernel smoother increase, the bias from the temporal and frequency averaging increases while the variance decreases. The demodulation parameters, such as the order, length, and bandwidth of spectral taper and the kernel smoother, are determined by minimizing the expected error. For well-resolved evolutionary, spectra, the optimal taper length is a small fraction of the optimal kernel halfwidth. The optimal frequency bandwidth, w, for the spectral window scales as w²~λ/τ, where τ is the characteristic time and λ_F is the characteristic frequency scalelength. In contrast, the optimal halfwidths for the second stage kernel smoother scales as h~1/(τλ_F )¹(p+2)/ where p is the order of the kernel smoother. The ratio of the optimal-frequency halfwidth to the optimal-time halfwidth is determined     

Commissioning of a state-controlled high-powered electrical driveusing evolutionary algorithms

The subject of this research is the automated startup procedure of a PI state-controlled rolling-mill motor by using evolutionary algorithms. Compared to the conventional PI speed control, applying the method of deliberate pole placement to the state controller design succeeds in improving the transient response of setpoint and disturbance changes. To put the PI state-controlled drive with observer into operation to obtain a controller with a high robustness and dynamics, the precise knowledge of this physical parameter is necessary. An evolution-based system is used to solve the estimation problem. A high degree of reliability respecting multimodal characteristics and robustness against random noise is expected from the identification method. Evolutionary algorithms fulfill this requirement. With genetic operators like mutation, crossover, and selection, evolutionary algorithms mimic the principles of organic evolution in order to solve the optimization problem     

Optimization of high-speed multistation SMT placement machinesusing evolutionary algorithms

Surface mount technology (SMT) is a robust methodology that has been widely used in the past decade to produce circuit boards. Analyses of the SMT assembly line have shown that the automated placement machine is often the bottleneck, regardless of the arrangement of these machines (parallel or sequential) in the assembly line. Improving and automating the placement machine is a key issue for increasing SMT production line throughput. This paper presents experimental results using genetic algorithms to optimize the feeder slot assignment problem for a high-speed parallel, multistation SMT placement machine. Four crossover operators, four selection methods, and two probability settings are used in our experiments. A penalty function is used to handle constraints. A comparison of genetic algorithms with several other optimization methods (human experts, vendor supplied software, expert systems, and local search) is presented, which supports the use of genetic algorithms for this problem     

Analog circuit optimization system based on hybrid evolutionary algorithms

This paper investigates a hybrid evolutionary-based design system for automated sizing of analog integrated circuits (ICs). A new algorithm, called competitive co-evolutionary differential evolution (CODE), is proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through electrical simulation, to the optimization system in the MATLAB environment, once a circuit topology is selected. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met, even in highly-constrained situations. Comparisons with available methods like genetic algorithms and differential evolution, which use static penalty functions to handle design constraints, have also been carried out, showing that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.     

Optimal matrix-filter design

A matrix filter produces N output values given a block of N input values. Matrix filters are particularly useful for filtering short data records (e.g. N⩽20). We introduce a new set of matrix-filter design criteria and show that the design of a matrix filter can be formulated as a convex optimization problem. Several examples are given of lowpass and bandpass designs as well as a Hilbert transformer design     

Fuzzy rules generation using new evolutionary algorithms combinedwith multilayer perceptrons

Based on evolutionary algorithms (EAs) and multilayer perceptrons (MLPs), a fuzzy rules generation method inclusive of two main learning stages is presented in this paper. In the primary stage, a new EA is developed to generate numerical control rules from input-output data without the help of experts, which increases the diversity of individuals to reduce the opportunities of falling into local optima. Every generated numerical rule is accumulated in a lookup table called a numerical-rule-based controller (NRC). In the secondary stage, both antecedent and consequent variables of the numerical rules are fuzzified by training MLPs with the backpropagation algorithm. All training data are directly derived from the NRC with simple manipulations. Consequently, a linguistic-rule-based controller (LRC) consisting of the generated fuzzy rules is completed. Two illustrative experiments are successfully made on the computer simulation and hardware implementation of the NRCs and LRCs of different types using the new EA combined with the MLPs. The experimental results reveal that the proposed EA-MLP MLP approach is efficient and effective to generate fuzzy rules which control nonlinearly dynamical systems exceedingly well     

Optimal and robust kernel algorithms for passive stochasticapproximation

The problem of estimating a root of an equation f(x )=0 is considered in the situation where the values of f( x) are measured with random errors at random points and the choice of these points cannot be controlled. Nonlinear modification of the recursive Hardle-Nixdorf method is studied. Almost sure and mean square convergence is proved, and the rate of convergence is estimated. The optimal choice of parameters and of a kernel is presented; it is shown that for the optimal procedure the lower bound for the accuracy of arbitrary methods of solving the problem is attained