Inverting reservoir parameters in a two-phase fractured medium with a niche genetic algorithm

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.

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm

Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algo- rithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.

Niche pseudo-parallel genetic algorithms for path optimization of autonomous mobile robot

A new genetic algorithm named niche pseudo-parallel genetic algorithm （NPPGA） is presented for path evolution and genetic optimization of autonomous mobile robot. The NPPGA is an effective improvement to maintain the population diversity as well for the sake of avoiding premature and strengthen parallelism of the population to accelerate the search process combined with niche genetic algorithms and pseudo-parallel genetic algorithms. The proposed approach is evaluated by robotic path optimization, which is a specific application of traveler salesman problem （TSP）. Experimental results indicated that a shortest path could be obtained in the practical traveling salesman problem named ＂Robot tour around Pekin＂, and the performance conducted by NPPGA is better than simple genetic algorithm （SGA） and distributed paralell genetic algorithms （DPGA）.

Niche Genetic Algorithm with Accurate Optimization Performance

Based on crowding mechanism, a novel niche genetic algorithm was proposed which can record evolution- ary direction dynamically during evolution. After evolution, the solutions’s precision can be greatly improved by means of the local searching along the recorded direction. Simulation shows that this algorithm can not only keep population diversity but also find accurate solutions. Although using this method has to take more time compared with the standard GA, it is really worth applying to some cases that have to meet a demand for high solution precision.

A New Genetic Algorithm Based on Niche Technique and Local Search Method

The genetic algorithm has been widely used in many fields as an easy robust global search and optimization method. In this paper, a new generic algorithm based on niche technique and local search method is presented under the consideration of inadequacies of the simple genetic algorithm. In order to prove the adaptability and validity of the improved genetic algorithm, optimization problems of multimodal functions with equal peaks, unequal peaks and complicated peak distribution are discussed. The simulation results show that compared to other niching methods, this improved genetic algorithm has obvious potential on many respects, such as convergence speed, solution accuracy, ability of global optimization, etc.

Optimization of Rolling Force Distribution Based on Niche Genetic Algorithm in Continuous Hot Rolling Mills

Based on the niche genetic algorithm, the intelligent and optimizing model for the rolling force distribution in hot strip mills was put forward. The research showed that the model had many advantages such as fast searching speed, high calculating pre- cision and suiting for on-line calculation. A good strip shape could be achieved by using the model and it is appropriate and practica-ble for rolling producing.

Combined Novel Gate Level Model and Critical Primary Input Sharing for Genetic Algorithm Based Maximum Power Supply Noise Estimation

A gate level maximum power supply noise （PSN） model is defined that captures both IR drop and di/dt noise effects. Experimental results show that this model improves PSN estimation by 5.3% on average and reduces computation time by 10.7% compared with previous methods. Furthermore,a primary input critical factor model that captures the extent of primary inputs＇ PSN contribution is formulated. Based on these models,a novel niche genetic algorithm is proposed to estimate PSN more effectively. Compared with general genetic algorithms, this novel method can achieve up to 19.0% improvement on PSN estimation with a much higher convergence speed.

Distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm

A distributed blackboard decision-making framework for collaborative planning based on nested genetic algorithm （NGA） is proposed. By using blackboard-based communication paradigm and shared data structure, multiple decision-makers （DMs） can collaboratively solve the tasks-platforms allocation scheduling problems dynamically through the coordinator. This methodo- logy combined with NGA maximizes tasks execution accuracy, also minimizes the weighted total workload of the DM which is measured in terms of intra-DM and inter-DM coordination. The intra-DM employs an optimization-based scheduling algorithm to match the tasks-platforms assignment request with its own platforms. The inter-DM coordinates the exchange of collaborative request information and platforms among DMs using the blackboard architecture. The numerical result shows that the proposed black- board DM framework based on NGA can obtain a near-optimal solution for the tasks-platforms collaborative planning problem. The assignment of platforms-tasks and the patterns of coordination can achieve a nice trade-off between intra-DM and inter-DM coordination workload.

Numerical tests for effects of various parameters in niching genetic algorithm applied to regional waveform inversion

In this paper,we focus on the influences of various parameters in the niching genetic algorithm inversion procedure on the results,such as various objective functions,the number of the models in each subpopulation,and the critical separation radius.The frequency-waveform integration（F-K） method is applied to synthesize three-component waveform data with noise in various epicentral distances and azimuths.Our results show that if we use a zero-th-lag cross-correlation function,then we will obtain the model with a faster convergence and a higher precision than other objective functions.The number of models in each subpopulation has a great influence on the rate of convergence and computation time,suggesting that it should be obtained through tests in practical problems.The critical separation radius should be determined carefully because it directly affects the multiextreme values in the inversion.We also compare the inverted results from full-band waveform data and surfacewave frequency-band（0.02-0.1 Hz） data,and find that the latter is relatively poorer but still has a higher precision,suggesting that surface-wave frequency-band data can also be used to invert for the crustal structure.

Optimal design of hydraulic manifold blocks based on niching genetic simulated annealing algorithm

To solve the combinatorial optimization problem of outer layout and inner connection integrated schemes in the design of hydraulic manifold blocks （ HMB）, a hybrid genetic simulated annealing algo- rithm based on niche technology is presented. This hybrid algorithm, which combines genetic algorithm, simulated annealing algorithm and niche technology, has a strong capability in global and local search, and all extrema can be found in a short time without strict requests for preferences. For the complex restricted solid spatial layout problems in HMB, the optimizing mathematical model is presented. The key technologies in the integrated layout and connection design of HMB, including the realization of coding, annealing operation and genetic operation, are discussed. The framework of HMB optimal design system based on hybrid optimization strategy is proposed. An example is given to testify the effectiveness and feasibility of the algorithm.

Cognitive Radio Spectrum Allocation Strategy Based on Improved Genetic Algorithm

With the rapid development of wireless communication industry, shortage situation of spectrum resource is increasingly significant. It has become an important topic to study cognitive radio spectrum allocation algorithm that is of higher spectrum utilization ratio, less system power consumption and better algorithm efficiency. Analyzes spectrum allocation models based on genetic algorithm, and then puts forward new improved genetic algorithm. The algorithm adopts niche crowding operation to avoid individual inbreeding. It adaptively adjusts crossover and mutation probability to keep them always in the appropriate state. It provides more equal individual competition opportunity by hierarchical measures, which can effectively avert premature convergence to local optimal solution. It obviously improves the district's total transfer rate on the premise that it has met the requirements of minimum user transfer rate and limitations of maximum total power and maximum bit error rate. Simulation results prove the effectiveness of the proposed algorithm.

Application of Modified Genetic Algorithm to Optimal Design of Supporting Structure

The modified genetic algorithm was used for the optimal design of supporting structure in deep pits.Based on the common genetic algorithm, using niche technique and reserving the optimum individual the modified genetic algorithm was presented. By means of the practical engineering, the modified genetic algorithm not only has more expedient convergence, but also can enhance security and operation efficiency.

A New Method for Evolving Artificial Neural Networks Using Genetic Algorithm

In this paper, a new neuroevolution algorithm (NEGA) for simultaneous evolution of both architectures and weights of neural networks is described. A whole new network encoding method is shown. The competing conventions problem is solved absolutely. Heuristic methods are used to constrain the topology mutation probability and the trend of mutation kind choice. Also, the niching method is used to protect the network topologies evolution. The experiment results show the efficiency and rapidity of NEGA forcefully.

基于小生境算法的空气质量模糊认知图预测

工业化使得全球经济取得了突飞猛进的增长,但也使得环境污染愈发严重。大气污染成为世界各国讨论的话题。文中提出一种基于小生境遗传算法的改进形式的空气质量模糊认知图预测方法。利用模糊认知图表示空气污染物以及空气质量指数的关联关系,并应用改进的小生境遗传算法优化模型,使得训练结果更接近全局最优解。文中使用2015-2021年的空气数据训练模型,在2022年的数据集上测试模型。实验结果表明,与传统的遗传算法和BP神经网络相比,所提方法预测精度更高,且泛化性能更好,证明了其有效性。

基于NGA优化SVM的滚动轴承故障诊断

支持向量机(SVM)的分类性能受样本的特征以及SVM本身参数的选择影响较大。针对这种情况,基于Shannon能量熵、SVM和小生境遗传算法(NGA),提出了一种基于NGA优化SVM的滚动轴承故障诊断方法。该方法采用容错性强的Shannon能量熵作为特征参数,对信号进行EMD分解提取出前3个IMF分量作为特征信号,分别计算其Shannon能量熵作为特征向量得到样本集,作为多类别SVM的输入。在用样本训练SVM时,构造一种新的核函数,并采用NGA对SVM的核函数参数进行全局优化,使SVM获得最佳的分类性能,提高其分类识别的正确率。最后采用凯斯西储大学的滚动轴承故障样本进行了分类识别,并与其他几种方法进行了对比,结果表明该方法具有更好的可靠性和分类准确率。

基于改进遗传算法的磁流变阻尼半主动控制系统整体优化

为解决磁流变阻尼半主动控制系统中控制算法参数、阻尼器参数与布置位置优化问题,提出一种改进的自适应小生境遗传算法.该遗传算法在选择策略、交叉和变异操作、交叉概率和变异概率的自适应调整等方面作了改进,并同时采用预选择机制和共享机制这两种小生境技术.算例分析结果表明:改进的自适应小生境遗传算法和改进的基本遗传算法优化结果总体一致,表明前者分析结果是正确的;前者首次得到最优解耗费的机时比后者平均少32.7%,可见前者比后者收敛速度更快;30次优化分析结果表明,前者比后者稳定性更强;经前者优化的磁流变阻尼半主动控制系统取得良好减振效果,El Centro波、集集波、人工波输入时,半主动控制结构层间位移角和绝对加速度的最大值较无控时分别平均减小64.1%、54.7%、55.9%.算例表明了改进的自适应小生境遗传算法的有效性,实现了对磁流变阻尼半主动控制系统的整体优化.

应用自适应Morlet小波和NGA优化SVM的轴承故障诊断

针对滚动轴承故障诊断中出现的多故障分类问题,提出了一种利用自适应Morlet小波和小生境遗传算法(niche genetic algorithm,简称NGA)优化支持向量机(support vector machine,简称SVM)实现滚动轴承故障诊断的新方法。首先,采用自适应Morlet小波方法提取出最佳尺度附近的3个信号分量作为特征信号,分别计算它们的Shannon能量熵值作为特征量得到样本集,作为SVM的输入向量,并用样本集训练1-v-r SVM;然后,再构造一种新的核函数,并用NGA在SVM训练过程中对核函数参数进行优化,提高SVM学习机器的分类性能;最后,将本研究方法用于对含有较强噪声的实际滚动轴承的内圈、外圈、滚珠故障样本进行了分类识别。结果表明,该方法具有较好的抗噪和分类能力,验证了其有效性和可行性。

一种新的基于NGA/PCA和SVM的特征提取方法

提出了一种使用小生境遗传算法(NGA)和主成分分析(PCA)对支持向量机(SVM)进行封装的方法来选择特征子集。该方法首先使用PCA得到特征向量,然后产生若干随机特征向量子集,从而得到新的特征空间,将所有训练样本映射到这个特征空间来训练支持向量机,再使用支持向量机的半径间隔方法对每个特征向量子集的性能进行评价,最后使用小生境遗传算法来共享适应度,以及进行选择、交叉和变异操作得到新的特征向量子集,重复这个过程直至得到最优的特征向量子集。使用UCI数据集进行了相关的实验,实验结果表明了该方法可以减少特征的数量以及提高分类正确率。

基于NGA的特征选择和SVM参数优化

支持向量机(SVM)的分类性能受样本的特征以及SVM本身参数的选择影响较大。在以前的工作中,很多学者对特征子集的选择以及SVM参数的优化分别进行了研究,实际上,特征子集和参数相互影响,针对这种情况,本文提出了一种同时进行特征子集选择以及参数优化的方法,该方法基于小生境遗传算法(NGA),对参数和特征采用不同的编码方法,对得到的每个参数以及对应的特征子集,使用SVM对其进行性能评价。使用UC I数据集进行了相关的实验,实验结果证明了该方法可以减少特征的数量以及提高分类正确率。

基于Niche GA的灰色GM(1,1,λ,△t_k)优化模型

在非等间隔GM(1,1,D,△t_k)模型基础上,建立了更具一般性的非等间隔GM(1,1,λ,△t_k)模型,并给出了求解最优GM(1,1,λ,△t_k)模型的小生境遗传算法,实例证明,用GM(1,1,λ,△t_k)模型进行预测能取得好的效果。