Immune Genetic Algorithm for Optimal Design

A computing model employing the immune and genetic algorithm (IGA) for the optimization of part design is presented. This model operates on a population of points in search space simultaneously, not on just one point. It uses the objective function itself, not derivative or any other additional information and guarantees the fast convergence toward the global optimum. This method avoids some weak points in genetic algorithm, such as inefficient to some local searching problems and its convergence is too early. Based on this model, an optimal design support system (IGBODS) is developed.IGBODS has been used in practice and the result shows that this model has great advantage than traditional one and promises good application in optimal design.

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.

Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions

A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.

Optimization of Submarine Hydrodynamic Coefficients Based on Immune Genetic Algorithm

Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations,an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations.Some hydrodynamic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm.By using adaptive weight method to determine the weight and target function,the multi-objective optimization could be translated into single-objective optimization.For a certain kind of submarine,three typical maneuvers were chosen to be the objects of study:overshoot maneuver in horizontal plane,overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane.From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrodynamic coefficient,the efficiency of proposed method is proved.

Intelligent optimization of the structure of the large section highway tunnel based on improved immune genetic algorithm

As in the building of deep buried long tunnels,there are complicated conditions such as great deformation,high stress,multi-variables,high non-linearity and so on,the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country,it has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying,expressing and disposing such kind of multiple variables and complicated non-linear relations. In this paper,a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm,thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory,the global search capability of the immune genetic algorithm is raised,thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel,the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency,the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationization design of the wall rock of the tunnel.

A novel immune genetic algorithm based on quasi secondary response

Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also used in the process of IGA QSR. Theoretical analysis, numerical examples of three benchmark mathematical optimization problems and a trave ling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy （ SGA ES）. Besides, IGA QSR allows the designers to stop and restart the optimization process freely with out losing the best results that have already been obtained. These properties make IGA QSR be a fea sible, effective and robust search algorithm for complex engineering problems.

Adaptive immune-genetic algorithm for global optimization to multivariable function

An adaptive immune-genetic algorithm （AIGA） is proposed to avoid premature convergence and guarantee the diversity of the population. Rapid immune response （secondary response）, adaptive mutation and density operators in the AIGA are emphatically designed to improve the searching ability, greatly increase the converging speed, and decrease locating the local maxima due to the premature convergence. The simulation results obtained from the global optimization to four multivariable and multi-extreme functions show that AIGA converges rapidly, guarantees the diversity, stability and good searching ability.

Modified Self-adaptive Immune Genetic Algorithm for Optimization of Combustion Side Reaction of p-Xylene Oxidation

In recent years, immune genetic algorithm (IGA) is gaining popularity for finding the optimal solution for non-linear optimization problems in many engineering applications. However, IGA with deterministic mutation factor suffers from the problem of premature convergence. In this study, a modified self-adaptive immune genetic algorithm (MSIGA) with two memory bases, in which immune concepts are applied to determine the mutation parameters, is proposed to improve the searching ability of the algorithm and maintain population diversity. Performance comparisons with other well-known population-based iterative algorithms show that the proposed method converges quickly to the global optimum and overcomes premature problem. This algorithm is applied to optimize a feed forward neural network to measure the content of products in the combustion side reaction of p-xylene oxidation, and satisfactory results are obtained.

Adaptive template filter method for image processing based on immune genetic algorithm

To preserve the original signal as much as possible and filter random noises as many as possible in image processing,a threshold optimization-based adaptive template filtering algorithm was proposed.Unlike conventional filters whose template shapes and coefficients were fixed,multi-templates were defined and the right template for each pixel could be matched adaptively based on local image characteristics in the proposed method.The superiority of this method was verified by former results concerning the matching experiment of actual image with the comparison of conventional filtering methods.The adaptive search ability of immune genetic algorithm with the elitist selection and elitist crossover(IGAE) was used to optimize threshold t of the transformation function,and then combined with wavelet transformation to estimate noise variance.Multi-experiments were performed to test the validity of IGAE.The results show that the filtered result of t obtained by IGAE is superior to that of t obtained by other methods,IGAE has a faster convergence speed and a higher computational efficiency compared with the canonical genetic algorithm with the elitism and the immune algorithm with the information entropy and elitism by multi-experiments.

Immune and Genetic Algorithm Based Assembly Sequence Planning

In this paper an assembly sequence planning model inspired by natural immune and genetic algorithm (ASPIG) based on the part degrees of freedom matrix (PDFM) is proposed, and a proto system — DSFAS based on the ASPIG is introduced to solve assembly sequence problem. The concept and generation of PDFM and DSFAS are also discussed. DSFAS can prevent premature convergence, and promote population diversity, and can accelerate the learning and convergence speed in behavior evolution problem.

Application of a Genetic Algorithm Based on the Immunity for Flow Shop under Uncertainty

The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se- quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov- ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an- tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.

Immune Genetic Learning of Fuzzy Cognitive Map

This paper presents a hybrid methodology of automatically constructing fuzzy cognitive map (FCM). The method uses immune genetic algorithm to learn the connection matrix of FCM. In the algorithm, the DNA coding method is used and an immune operator based on immune mechanism is constructed. The characteristics of the system and the experts' knowledge are abstracted as vaccine for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Finally, an illustrative example is provided, and its results suggest that the method is capable of automatically generating FCM model.

NOVEL QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON IMMUNITY

A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.

An Improved Artificial Immune Algorithm with a Dynamic Threshold

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

A Fuzzy-based Adaptive Genetic Algorithm and Its Case Study in Chemical Engineering

Considering that the performance of a genetic algorithm （GA） is affected by many factors and their rela-tionships are complex and hard to be described,a novel fuzzy-based adaptive genetic algorithm （FAGA） combined a new artificial immune system with fuzzy system theory is proposed due to the fact fuzzy theory can describe high complex problems.In FAGA,immune theory is used to improve the performance of selection operation.And,crossover probability and mutation probability are adjusted dynamically by fuzzy inferences,which are developed according to the heuristic fuzzy relationship between algorithm performances and control parameters.The experi-ments show that FAGA can efficiently overcome shortcomings of GA,i.e.,premature and slow,and obtain better results than two typical fuzzy GAs.Finally,FAGA was used for the parameters estimation of reaction kinetics model and the satisfactory result was obtained.

Dynamic airspace sectorization via improved genetic algorithm

This paper deals with dynamic airspace sectorization （DAS） problem by an improved genetic algorithm （iGA）. A graph model is first constructed that represents the airspace static structure. Then the DAS problem is formulated as a graph-partitioning problem to balance the sector workload under the premise of ensuring safety. In the iGA, multiple populations and hybrid coding are applied to determine the optimal sector number and airspace sectorization. The sector constraints are well satisfied by the improved genetic operators and protect zones. This method is validated by being applied to the airspace of North China in terms of three indexes, which are sector balancing index, coordination workload index and sector average flight time index. The improvement is obvious, as the sector balancing index is reduced by 16.5 %, the coordination workload index is reduced by 11.2 %, and the sector average flight time index is increased by 11.4 % during the peak-hour traffic.

基于IGA-BP神经网络的PEMFC供氢系统模型预测控制算法

对化石能源的过度使用导致了严重的环境问题和能源担忧。氢能作为一种清洁的能源,被认为是实现能源转型和可持续发展的重要资源。在此背景下,氢燃料电池作为一种将氢能高效转化为电能的技术,展现出了巨大潜力。本文以质子交换膜燃料电池阳极供氢系统为研究对象,以氢气计量比和阴、阳极压强差为控制目标,设计了基于神经网络的模型预测控制算法。首先基于MATLAB/Simulink搭建了面向控制的燃料电池集总参数机理模型,通过实验验证了模型的可靠性;然后通过免疫遗传算法优化神经网络的学习过程,实现了对燃料电池系统状态的精确拟合与预测;最后,将离线训练的神经网络应用于模型预测控制器,并验证了控制算法的有效性。

基于IGA-FL融合算法的有色金属选矿精矿品位优化研究

为优化有色金属选矿过程的精矿品位,将模糊逻辑算法与免疫遗传算法进行融合,设计出了一种IGA-FL融合算法,并基于该融合算法构建有色金属选矿检测模型,对有色金属选矿的精矿品位进行检测和优化。对比试验结果显示,IGA-FL融合算法的数据查全率为99.7%,计算速度为16.7 bps;基于该算法的检测模型平均检测准确率为97.3%,检测耗时1.8 s。应用基于IGA-FL融合算法的检测模型后,有色金属选矿精矿品位达到70.5%,说明该检测模型能够对有色金属选矿的精矿品位进行优化。

基于BIM与IGA的选煤厂改扩建成本优化应用研究

由于选煤厂改扩建项目规模庞大、成本因素众多且相互关联,传统的项目成本优化方法往往难以找到最优解,提出了一种基于免疫遗传算法(IGA)的选煤厂改扩建项目成本优化方法。首先,对选煤厂改扩建项目利用建筑信息模型(BIM)技术建模优化设计布局;然后,建立数学模型,引入免疫遗传算法优化求解该模型,免疫遗传算法利用种群的多样性和进化操作,能够在大规模搜索空间中快速寻找到较优解,自适应进化过程对设计变量进行优化。通过对某选煤厂项目成本进行优化分析,验证了免疫遗传算法在选煤厂改扩建项目成本优化中的有效性,为选煤厂改扩建项目成本优化提供了新的参考方法。

基于IGA-BP的矿井构造复杂程度评价

为了准确评价矿井地质构造复杂程度,以黄陵一号煤矿为研究对象,在矿井地质构造发育特征与规律分析的基础上,选取了能够反映和影响该矿地质构造复杂程度的11个评价指标,按照1 km×1 km规格将井田剖分为185个评价单元,计算每个评价单元的评价指标值,借助有序地质量最优分割分析将每个评价指标值分割为4类,分别对应地质构造的简单、中等、复杂、极复杂4种类型,利用段内插值法获得BP神经网络的训练样本;为了克服单纯BP神经网络程序缺乏隐层神经元结构全局优化、收敛速度慢和易陷入局部最小值之缺陷,尝试采用基于免疫遗传算法(IGA)进行优化的BP神经网络算法(即IGA-BP)对矿井地质构造复杂程度进行综合评价;借助既定的训练样本,成功实现了BP网络隐层结构的全局优化和BP神经网络训练,最终利用训练好的IGA-BP网络对未知评价单元的地质构造复杂程度进行了综合评价,并绘制了矿井构造复杂程度分区图。结果显示:构造简单区位于研究区北部、东北部和南部,构造复杂区位于研究区中部偏西,构造中等区分布于研究区中部构造复杂区的南北两侧;与GA-BP、BP神经网络方法对比,基于IGA-BP的评价结果与矿井实际情况更为吻合,且IGA-BP评价方法无需考虑评价指标之间的相关性及权重,为矿井构造评价提供了1种新的评价方法,评价结果可以指导矿井合理的采掘部署。