Orthogonal Waveform Design of MIMO Radar Based on Niche Genetic Algorithm

Learning Classifier Systems (LCS) evolve IF-THEN rules for classification and control tasks. The earliest Michigan-style LCS used a panmictic Genetic Algorithm (GA) (in which all rules compete for selection) but newer ones tend to use a niche GA (in which only a certain subset of rules compete for selection at any one time). The niche GA was thought to be advantageous in all learning tasks, but recent research suggests it has difficulties when the rules composing the solution overlap. Furthermore, the niche GA's effects are implicit, making it difficult study, and fixed, which prevents tuning its performance. Given these issues, we set out on a long-term project to reevaluate the niche GA. This work is our starting point and in it we address the implicit and unquantified effects of the niche GA by building a mathematical model of the probability of rule selection. This model reveals a number of insights into the components of rule fitness, particularly the bonus for rule generality and penalty for overlaps, both previously unquantified. These theoretical results are our primary contribution. However, to demonstrate one way to apply this theory, we then introduce a new variant of the UCS algorithm, which uses a hybrid panmictic/niche GA. Preliminary results suggest, unexpectedly, that the niche GA may have even more drawbacks than previously thought.

We propose a new framework of computing-inspired multifocal cancer detection procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are regarded as solutions of the objective function, the tissue region around the cancer areas represents the parameter space, and the nanorobots loaded with contrast medium molecules for cancer detection correspond to the optimization agents. The process that the nanorobots detect tumors by swimming in the high-risk tissue region can be regarded as the process that the agents search for the solutions of an objective function in the parameter space with some constraints. For multimodal optimization (MMO) aiming to locate multiple optimal solutions in a single simulation run, the niche technology has been widely used. Specifically, the niche genetic algorithm (NGA) has been shown to be particularly effective in solving MMO. It can be used to identify the global optima of multiple hump functions in a running, effectively keep the diversity of the population, and prematurely avoid the genetic algorithm. Learning from the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to locate the tumor targets efficiently while taking into account realistic in vivo propagation and controlling of nanorobots, which is different from the use scenario of the standard NGA. To improve the performance of the MCDP, we also modify the crossover operator of the original NGA from crossing within a population to crossing between two populations. Finally, we present comprehensive numerical examples to demonstrate the effectiveness of the NGA-inspired MCDP when the biological objective function is associated with the blood flow velocity profile caused by tumor-induced angiogenesis.

Public landscape is a settlement landscape formed under human intervention, which is both cultural resources and ecological environment resources. Niche genetic algorithm is the most widely used algorithm in modern times. It greatly expands the space of system design and optimizes all links in public landscape environment optimization system design. This emerging technology also meets a wide range of public landscape needs. Starting from the influence of environmental factors and niche genetic algorithm, this paper discusses various links of modern public landscape environmental design in urban environment, deeply discusses the relationship and background between niche genetic algorithm and optimizing public landscape environmental design system, and analyzes the limitations of public landscape design at present, Combined with the research of niche genetic algorithm at home and abroad, this paper analyzes the performance links and characteristics of public landscape environment design system, focuses on its application principles and aesthetic evaluation in improving the efficiency of public landscape environment optimization system, and analyzes and discusses the advantages of this emerging tool in the use of public landscape environment optimization system.

It was demonstrated that a niching genetic algorithm (NGA) could be efficient for the optimization of a SiC crystal growth system. And several design parameters of SiC crystal growth system could be optimized at the same time, and high diversity of population was maintained to obtain global optimization solution by NGA. Firstly, the NGA and thermal models were described and applied for SiC crystal growth by physical vapor transport (PVT) method, and the combination method of NGA and thermal models were presented. Then two cases were carried out to demonstrate the automatic optimization of SiC crystal growth system by NGA. One case was a single-objective optimization problem, in which the axial position of coils was optimized to improve the growth rate of crystal. The another was a multi-objective optimization problem, in which the thickness of substrate holder and input current were optimized for uniform temperature distribution along the growth surface for reducing the thermal stresses in growing crystal. Finally, all the optimization results were analyzed.

At present, the evolution cryptanalysis methods mainly adopt the basic genetic algorithm (literature (1) and (2)) which has early phenomenon, searching stagnation in the near optimal solution, and low calculation efficiency. This paper inspired by biological genetic evolution proposes a niche genetic algorithm based on the code analysis method. This algorithm can keep the population diversity and prevent premature convergence. In genetic operator operation, this algorithm uses (μ + λ ) selection strategy which has the strongest selection pressure. In order to achieve compensating the shortage of groups diversity easy loss, improve the searching efficiency of algorithm, speed up the convergence speed, this algorithm applies the multi-point crossover operation, at the same time adopts variation operation by introducing evolution mutation probability. This p aper chooses examples to demonstrate the replace cryptogram. Experiments show that the algorithm can effectively reduce the complexity of the problem analysis, reduce the redundant computation, speed up the convergence. Therefore, in the limited time, it can effectively get an optimal solution or suboptimal solution of the analysis object.

Based on the study of the unidirectional loop layout, this paper analyzed the necessity of genetic algorithm to the workshop layout optimization. To minimize transportation travel, the mathematical model of the unidirectional loop workshop facility layout was set up, using the niche genetic algorithm with introduction of penalty function and elite reservation strategy to accomplish the facility layout optimization. Through the calculation of the example, the feasibility and effectiveness of this algorithm in facility layout optimization design is verified.

In this paper we calculate a synthetic medium surface displacement response that is consistent with real measurement data by applying the least-square principle and a niche genetic algorithm to the parameters inversion problem of the wave equation in a two-phase medium. We propose a niche genetic multi-parameter (including porosity, solid phase density and fluid phase density) joint inversion algorithm based on a two-phase fractured medium in the BISQ model. We take the two-phase fractured medium of the BISQ model in a two-dimensional half space as an example, and carry out the numerical reservoir parameters inversion. Results show that this method is very convenient for solving the parameters inversion problem for the wave equation in a two-phase medium, and has the advantage of strong noise rejection. Relative to conventional genetic algorithms, the niche genetic algorithm based on a sharing function can not only significantly speed up the convergence, but also improve the inversion precision.

We consider the problem of joint transmit waveform optimization and receiver filter design in a signal-dependent clutter environment for a multistatic radar. We devise an optimization procedure that, by maximizing the Signal to Interference Noise Ratio (SINR) under certain constraints on the distortion of the transmitted signals, will iteratively optimize both the orthogonal codes and the receiver filter at the radar with centralized cognition abilities. The waveform design is based on the exploitation of initial radar codes characterized by desired auto and cross-correlation properties. Various performance analyzes demonstrate the effectiveness of the algorithm in providing a significant increase in SINR in a highly reverberating environment.

In this paper, cluster splitting and merging algorithms are used for flood assessment using LISS-III (before flood) and SAR (during flood) images. Bayesian Information Criteria (BIC) is used to determine the optimal number of clusters. Keeping this constraint, the cluster centers are generated using the cluster splitting techniques, namely Mean Shift Clustering (MSC), and Niche Genetic Algorithm (NGA). The merging method is used to group the data points into their respective classes, using the cluster centers obtained from the above techniques. These techniques are applied on the LISS-III and SAR image. Further, the resultant images are overlaid to analyze the extent of the flood in individual land classes. A performance comparison of these techniques (MSC and NGA) is presented. From the results obtained, we deduce that the NGA is efficient.

Genetic algorithm is a kind of way to solve complex problems effectively, for it is not bound by the restrictive assumptions of the search space, and doesn't require the assumption conditions such as continuity and derivatives. So this algorithm has its advantage that the traditional algorithm can not compared. Genetic algorithm uses multi-point search. In each iteration, the new individuals are generated by mating and mutation, so the searching range can be expanded, and the local optimal solution can be effectively prevented. In this paper, a real number encoding method based the change of course is proposed. According to the change of course, the algorithm constructs the individuality, and constructs the temporary path by the individual coding vector. And on this basis, the related operators shall be designed through the new encoding method and a series of genetic operations to carry out the path planning. The simulation results show that this method can improve the global search ability of genetic algorithm, and also improves the quality of Unmanned Aerial Vehicle flight path. The Unmanned Aerial Vehicle could get a better path in terms of the performance cost.

This paper presents a crowding niche cellular genetic algorithm (referred to NCGA) aiming at solving the problems of local convergence and non-uniform population distribution in traditional genetic algorithm. The selecting operation in traditional genetic algorithm is improved by bringing in the concept of neighbors of cellular genetic algorithm, and the population distribution is greatly enhanced by introducing crowding niche mechanism, which betters the ability of global searching and helps to avoid the population local convergence. Meanwhile, the paper describes the crowding niche cellular genetic algorithm in details and compares it with simple genetic algorithm （SGA） and simple niche genetic algorithm （NGA）; the comparison results reveal that, NCGA outperforms the other two algorithms in terms of convergence rate and population diversity.

Three different optimization methods using genetic algorithms have been developed, aiming to achieve better aggressive intermediate turbine duct (ITD) performance. To overcome defects of simple genetic algorithms, a niche genetic algorithm is used, for its better adaptability to multi-peak function. These three methods are two-dimensional optimization for pursuing the highest static pressure coefficient; two-dimensional optimization via controlling static pressure coefficient; and a further three-dimensional optimization. The second method introduces a restrainer to make sure the maximum value of static pressure coefficient gradient less than a limitation. A simulation case, an ITD with eight struts, was implemented to demonstrate the capabilities of the presented optimization methods. Compared to the baseline ITD, the results show as follow. The first method obtains a best static pressure coefficient but a severe separation. The second optimization method with static pressure coefficient gradient control can definitely suppress separation in the ITD, the second method also obtains better static pressure coefficient and the lowest total pressure loss coefficient. The third method, a further three-dimensional optimization can obtain better ITD overall performance because of a more realistic simulation. Nevertheless, the second two-dimensional optimization method can get good enough results while it is apparently much more time-saving compared to three-dimensional one, which make it more suitable for engineering applications.

Based on safety assessment factors determined by operation characteristics of a certain tailing ,genetic BP neural network evaluation model is established. To overcome such problems of BP neural network as slow convergence ,poor generalization ability and easy to fall into local minimum value,this paper proposes to use genetic algorithm to optimize threshold value,weights and structure of neural network. Thus,by taking advantage of extensive mapping ability of neural network and global search ability of genetic algorithm,neural network and genetic algorithm will have complementary advantages and the learning speed of network will be accelerated. The application of the described method shows optimized fitting precision,improved accuracy and efficiency ,and enhanced generalization ability of BP neural network. In conclusion,this model can effectively reflect and accurately evaluate non-linear relations between security levels and evaluation factors in tailing.

In this paper, a novel radar waveform phase function design is presented that constructs a radar waveform that spectrally manuevers around in-band co-channel interference (CCI) from active communications users that are co-channel with the radar system. The design approach treats the CCI problem as the physical interpretable problem of optical refraction where the time-frequency (TF) signature of the radar waveform is made to be analogous to a light ray propagating from a start time and position (frequency) to an end time and position (frequency) and will naturally accelerate through the CCI as if the equivalent light ray accelerated in through different mediums (with an index of refraction related to CCI power spectral density (PSD)). Using the Euler-Lagrange equations, we design the radar phase function that is used to modulate a constant modulus (CM) waveform. The radar waveform design will generally result in a nonlinear-frequency modulated (NLFM) waveform that provides improved signal-to-interference-plus-noise-ratio (SINR) on a potential target in the presence of CCI leading to improved probability of detection performance. We provide theoretical analysis and simulation results for the proposed waveform design.

Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. This paper proposes to apply the artificial neural network theory and the genetic algorithm to solve the difficulties of gear fault diagnosis. Niche technique based on crowding mechanism is used in genetic algorithm, and punishing function is adopted to adjust individual fitness, so as to promote global search capability. Taking a certain gearbox fault signal acquisition experimental system for an example, Matlab software and its neural network toolbox are used to model and simulate. The experiment result shows that the founded network model has good performance for the common gear fault diagnosis and it can identify various types of faults stably and accurately.