Inverting reservoir parameters in a two-phase fractured medium with a 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.

The propagation of acceleration waves in a fluid-saturated porous medium is considered. The two-phase medium is the system consisting of a porous elastic solid skeleton, filled with a viscous compressible fluid. Two types of the media are taken into account: the medium composed of definite conductors and the medium composed of non-conductors. The method of singular surfaces has been used in these considerations. The acceleration waves in the medium consisting of non-conductors are not homentropic, in general. In this paper the conditions are determined which must be fulfilled to satisfy the acceleration waves to be homentropic. The propagation conditions of the waves are formulated and analysed. As usual in such a two-phase medium two longitudinal waves and one transverse wave are propagated. The growth equations of homothermal and homentropic waves are derived, and their solutions are analysed.

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.

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 order to optimize the orthogonal design of MIMO radar waveform, an improved algorithm based on niche genetic algorithm is proposed. The algorithm is able to eliminate individuals with similar structures in the population or data, maintain a certain distance between individuals, and effectively hold the diversity of them It thus eliminates the phenomenon of premature convergence, strengthens the global search ability of the algorithm, and improves its convergence speed. It is shown that the algorithm has both the advantages of overall search ability of genetic algorithm and the ability of the niche genetic algorithm to discover multiple optimal solutions. Simulation was given to show that the algorithm was cable to reduce the side-lobe of the auto-correlation function of the orthogonal polyphase code and the peak value of the cross-correlation function of the code with good performance of autocorrelation and cross-correlation obtained

Based on the Biot theory, a model is proposed and the features of the propagation of Lamb and Love waves in flat thin plates and coatings made of porous-elastic two-phase materials such as isolon and foamed polyurethane with free boundaries are analyzed. The developed model of a two-phase medium for porous gas-filled materials allows to analyze any types of volumetric, longitudinal and transverse waves at arbitrary values of porosity. The method of potentials was used for solving boundary value problems describing the propagation of waves in a two-phase porous medium with closed and permeable pores. The velocities of transverse Lamb waves in a plate and Love waves in a coating were calculated for different parameters of a porous body. A qualitative analysis of the behavior of the dependences of the velocities S0 of zero-order modes of Lamb waves and first-order Love waves on changes in the medium parameters and the porous medium geometry in the low-frequency region was carried out.

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.

A two-phase medium with a carrier phase in the form of an incompressible electrically neutral fluid and a dispersed phase in the form of inertial charged particles flows past an electrically charged sphere. It is assumed that the electrohydrodynamic interaction parameter is insignificant and that the flow conditions correspond to potential unseparated flow of the carrier medium over the sphere. The motion of the dispersed phase is described by continuum dynamic equations incorporating the electric field, which is the sum of the external field created by the sphere and the field induced by the dispersed particles. The electric field is determined by means of the equations of electrodynamics, which must be considered together with the dynamic equations. In the case considered a large electrostatic potential is applied to the sphere. This prevents the particles striking the surface of the sphere and leads to the intersection of the particle trajectories. In order to solve this problem within the framework of the two-velocity continuum we introduce a surface of discontinuity of the parameters to replace the zone of multiphase flow. The location of the surface of discontinuity, the distribution of the velocity and density of the dispersed phase and the distribution of electrostatic potential are found as a result of solving a system of elliptic and hyperbolic equations in two regions separated by the surface of discontinuity. The results of numerically integrating the system of equations formulated are presented.

Theory based on an integral equation for the average wave which includes almost all multiple scattering processes, but it ignores correlations among inclusions. Theory applied to fluid-solid suspensions and to fluid-saturated porous rocks.--Modified journal abstract.

The Lattice Monte Carlo method was used to calculate the effective diffusion in nanostructured 3D systems, comprising two-phase zones with different morphology. The results of the calculations allowed to establish and compare the dependences of effective diffusion coefficients in nanostructured two-phase systems on the volume fraction of phases. Calculations are made in the case of parallel, sequential and combined methods of phase coupling in two-phase zones of different morphology. The phenomenological parallel and serial connection models for the determination of the effective diffusion parameters of the inhomogeneous two-phase media are compared with the Monte Carlo Lattice method.

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.

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.