Adaptive Immune Genetic Algorithm Research Articles

Retracted: Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm

Retracted: Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm

Adaptive Data‐Driven Modeling Strategy Based on Feature Selection for an Industrial Natural Gas Sweetening Process

AbstractAs the core process of natural gas purification plant, natural gas sweetening directly affects the production efficiency and product quality of the purification plant. However, process modeling based on sulfur content prediction presents challenges in adaptability and accuracy. To tackle this, a machine learning‐based modeling approach is proposed that integrates an adaptive immune genetic algorithm with random forest (RF) to intelligently select process features as input variables for RF modeling. The industrial result indicates that the proposed method is able to remove interfering variables and adaptively achieve optimal model precision for different scenarios. It offers a novel research instrument for product quality monitoring in natural gas sweetening plants.

Reliable design of urban surface-underground integrated logistics system network with stochastic demand and social-environmental concern

Transforming urban goods movement from roads to underground is an emerging concept to improve supply chain performance and alleviate overcrowded environments. Existing literature merely discussed the underground logistics system (ULS) network design problem within a simplified boundary, whereas several critical system features (e.g., stochastic freight demand and co-modality) were omitted. To bridge the knowledge gaps, this paper proposes a reliable modeling approach to solve an urban surface-underground integrated logistics network (USUIL) design problem with a holistic consideration of cost-benefit objectives, road-ULS modal split, and demand uncertainties. To begin with, we introduce a city-wide hub-and-spoke USUIL network structure with a two-tier parallel distribution process. Five major external benefits of underground goods movement are formulated using real-world data. Next, a deterministic model is developed to optimize the network decisions including hub-satellite location-allocation, deployment of tunnels and pipelines, logistics service assignment, and flow routing. Then, to deal with the influences of case-wise demand uncertainties, the model is reformulated into a reliable one by incorporating the ideas of solution robustness and stochastic equivalents. A two-stage heuristic-based optimization tool that hybridizes the adaptive immune genetic algorithm, plant growth simulation algorithm, and Clarke-Wright saving algorithm was proposed to solve the problem. Finally, numerical experiments are conducted on the Beijing city case to validate the proposed method, and the best network schemes obtained from different models and scenarios are compared. Results show that a greater robust control factor in our model helps the network to lower the integrated freight transport cost while improving the social-environmental benefit at the expense of deploying more facilities. The reliable model performs well in balancing the conservativeness (i.e., less capacity insufficiency risk) and optimality (i.e., less cost due to redundant facilities) of the USUIL network design when faced with diverse demand cases.

Intelligent Height Adjustment Method of Shearer Drum Based on Rough Set Significance Reduction and Fuzzy Rough Radial Basis Function Neural Network

The intelligent adjustment method of the shearer drum is the key technology to improve the intelligent level and safety degree of fully mechanized mining face. This paper proposes a shearer drum intelligent height adjustment model based on rough set significance attribute reduction (AR) and fuzzy rough radial basis function neural network (FRRBFNN) optimized by adaptive immune genetic algorithm (AIGA). The model first selects the parameters of shearer process monitoring based on the importance attribute reduction algorithm of rough set, and establishes the attribute reduction set of shearer operation characteristic parameters and the drum height decision rule base. Next, a fuzzy rough radial basis function neural network determined by the decision rule space is proposed. By introducing the fuzzy rough membership function as the connection weight, the network can accurately describe the complex nonlinear relationship between the working characteristic parameters of the attribute shearer and the drum height, and measure the uncertainty of the coal seam distribution. Finally, to further optimize the performance of FRRBFNN, the adaptive immune genetic algorithm is introduced to optimize its parameters, to build a high-precision shearer drum height prediction system. For the evaluation method of the model, we use three indicators: mean absolute error, mean absolute percentage error, and root mean square error. Based on the measured data in Yujialiang area, Shaanxi Province, the experimental results show that—compared with the FRRBFNN and support vector regression (SVR) models, a gated current neural network (GRU), a radial basis function neural network (RBF), the memory strengthen long short-term memory (MSLSTM) model, and the adaptive fuzzy reasoning Petri net (AFRPN)—the MAE of the AR-AIGA-FRBFNN model for predicting the height of the left and right rollers are 18.3 mm and 17.2 mm, respectively; the MAPE is 0.96% and 0.93%, respectively; and the RMSE is 21.2 mm and 22.4 mm, respectively. The AR-AIGA-FRBFNN model is therefore more effective than the other considered methods.

Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

To reduce the complexity and multiple constraints of logistics distribution routing problems, the author proposes an improved genetic algorithm, adaptive immune genetic algorithm (AIGA). The algorithm utilizes a new immune vaccine selection strategy and immune operation method, the optimization process is adaptively changed with the evolutionary algebra, combined with the parallel selection method to optimize the multiobjective logistics distribution path, and the specific steps to solve the multiobjective logistics distribution path problem are given. The experimental results show that the optimal path is calculated as 0—2—8—5—3—1—0; 0—4—7—6—0. AIGA not only efficiently converges to the optimal solution but also stabilizes to a fitness value of 66.5, reflecting better precision than IGA. The computational efficiency and convergence of the algorithm are significantly improved, which verifies the practicability and effectiveness of the algorithm. The adaptive immune genetic algorithm realizes the path optimization of complex logistics distribution, which can better change the global search performance of the original immune genetic algorithm, greatly improve the algorithm convergence speed, and achieve good results in practical applications.

Evaluation of M&A Risk Based on Adaptive Immune Genetic Algorithm

In order to improve the convergence and accuracy of M&A risk assessment method, an M&A risk assessment method based on adaptive immune genetic algorithm is proposed. The risk of M&A is divided into M&A preparation stage, M&A implementation stage, and M&A integration stage. The event tree analysis method is used to clarify the inducement of M&A risk events. We optimize the traditional genetic algorithm, design adaptive immune genetic algorithm to ensure the convergence of risk assessment algorithm, select the risk assessment indicators of enterprise M&A, determine the weight of the assessment indicators, and construct the quantitative model of enterprise M&A risk assessment. The experimental results show that the proposed risk assessment method takes less time, has higher accuracy, and has less probability of enterprise data imbalance.

Development of an Adaptive Model for the Rate of Steel Corrosion in a Recirculating Water System

The stable quality of circulating water ensures the long-term stable operation of various processes in petrochemical production and achieves energy savings and emission reduction while reducing environmental pollution and yielding economic benefits to petrochemical enterprises. However, traditional circulating water quality evaluation and modeling for corrosion rate prediction suffer from adaptability and accuracy problems. To address these problems, the water quality analysis data of the circulating water in the field were subjected to data preprocessing and water quality index calculation to perform feature engineering, followed by modeling using a machine learning method that integrates the adaptive immune genetic algorithm and random forest (RF) algorithm and can intelligently select the water quality parameters to be used as the input variables for the RF modeling. Finally, the method was validated using an industrial example, and the results indicate that the method is capable of removing interference variables and is suitable for carbon steel corrosion rate prediction based on water quality models. The proposed method provides a basis for water quality management and real-time decision-making by circulating water field personnel.

A Hybrid Discrete Grey Wolf Optimizer to Solve Weapon Target Assignment Problems

We propose a hybrid discrete grey wolf optimizer (HDGWO) in this paper to solve the weapon target assignment (WTA) problem, a kind of nonlinear integer programming problems. To make the original grey wolf optimizer (GWO), which was only developed for problems with a continuous solution space, available in the context, we first modify it by adopting a decimal integer encoding method to represent solutions (wolves) and presenting a modular position update method to update solutions in the discrete solution space. By this means, we acquire a discrete grey wolf optimizer (DGWO) and then through combining it with a local search algorithm (LSA), we obtain the HDGWO. Moreover, we also introduce specific domain knowledge into both the encoding method and the local search algorithm to compress the feasible solution space. Finally, we examine the feasibility of the HDGWO and the scalability of the HDGWO, respectively, by adopting it to solve a benchmark case and ten large-scale WTA problems. All of the running results are compared with those of a discrete particle swarm optimization (DPSO), a genetic algorithm with greedy eugenics (GAWGE), and an adaptive immune genetic algorithm (AIGA). The detailed analysis proves the feasibility of the HDGWO in solving the benchmark case and demonstrates its scalability in solving large-scale WTA problems.

RETRACTED: A novel parameter design for level grid-connected smart inverters

Abstract LCL filter is an important component in the grid-connected inverters. The parameters of LCL filter have a strong influence on the output current distortion of inverters. For three-level inverter system, a parameters design method is proposed in this paper based on genetic algorithm optimization of output current total harmonic distortion (THD). By deducing the transfer function of the filter, output current THD is calculated through double Fourier analysis. Then an adaptive immune genetic algorithm is designed to solve the objective parameters optimization issue under constraint condition. With harmonics attenuation in both fundamental band and carrier band taken into account, the filter parameters are optimized scientifically by the global search algorithm, which contains punishment mechanism, adaptive parameters and the mechanism of immune algorithm. The feasibility of the proposed method is verified by conducting simulation and experiment.

Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-US; mso-bidi-language: AR-SA;">This paper aims to set up new rules for processing seawater quality monitoring data collected by photoelectric sensor network, and mine out the useful information contained in the data. For this purpose, the immune algorithm was introduced to the classical genetic algorithm, the fitness function was designed, and the crossover and mutation probabilities were adjusted, thus creating the adaptive immune genetic algorithm (IIGA). The new algorithm was described in details and applied in an actual case. Through the comparison between the IIGA, IGA and apriori algorithms, the author concluded that the IIGA not only shortened the mining time, but also ensured the operation accuracy. The research findings are of great importance to the association rules mining in various fields.</span>

Improved adaptive immune genetic algorithm for optimal QoS-aware service composition selection in cloud manufacturing

Developments in new information technology have indicated that single manufacturing services are now unable to satisfy users’ multi-objective demands, especially in the process industry. As a new user-centric, service-oriented, demand-driven manufacturing model, cloud manufacturing can provide high-reliability, low-cost, fast-time, high-ability services. This study presents a new Manufacturers to Users (M2U) mode for cloud manufacturing, aiming at solving the core manufacturing service composition optimal selection (MSCOS) problem. The M2U mode expands the service areas and improves its dynamic optimal allocation capabilities of resources by efficient and flexible management and operation of services. Firstly, a comprehensive mathematical evaluation model with four critical quality of service (QoS)-aware indexes (time, reliability, cost, and ability) is constructed. Secondly, a new information entropy immune genetic algorithm (IEIGA) is proposed for the model solution. Finally, nine MSCOS problems of different scales are illustrated so as to compare the performance of the three algorithms. The results prove the effectiveness and superiority of the proposed algorithm and its suitability for solving large-scale service composition problems.

Application and Research of Elman Neural Network Model Optimized Input by Improved Immune Genetic Algorithm in MBR Membrane Flux Prediction

Membrane flux is an important indicator to show and prove the extent of membrane pollution . Our team adopted the Elman neural network to establish the MBR contamination simulation prediction model. By setting up the non-linear relationship of influencing MBR membrane pllution’s factors and membrane flux, to complete the membrane flux prediction. Research shows that the MBR membrane flux prediction model based on Elman neural network still has some problems. After reviewing a large number of literatures, we applies improved adaptive immune genetic algorithm based on antibody concentration to optimize this model. At the same time, we compares the optimization effect of this algorithm with the optimization effect of traditional genetic algorithm. By the contrast and analysis, the Elman neural network model optimized input by the improved immune genetic algorithm is superior to the Elman neural network model optimized input by the traditional genetic algorithm on the prediction accuracy and stability of MBR membrane flux.

The Optimization Model and Algorithm of Remanufacturing Supply Chain Logistics Network with Option Contracts

By the point of view of remanufacturing enterprise, the studies of supply chain logistics network optimization problem focused on network optimization and logistics distribution between manufacturing enterprise and customer area. The present business environment is full of uncertainties. To compete effectively in such an environment, remanufacturing enterprises need to develop the capability of responding flexibly to changing market conditions and optimize effectively logistics networks. The mechanism of option contracts has effectively coordinated the relationship of remanufacturing enterprise and retailer and hedged against the risks of over- and under-production. There is few papers considers the mechanism of option contracts although there are lots of studies on supply chain logistics networks optimization problem. Therefore, the mechanism of option contracts is led into the study of remanufacturing supply chain logistics networks optimization problem. Considering environmental pollution influence factors in the remanufacturing process, a multi-objective optimization model of supply chain is developed. Dual-layer genetic algorithm mechanism was brought up. The first layer is responsible for supply chain logistics network structure with genetic algorithm. The second layer answers for specific supply chain logistics distribution with adaptive immune genetic algorithm. Finally, numerical examples demonstrate the validity of the model and algorithm for the optimization problem.

Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment

Military applications are producing massive amounts of data due to the use of multiple types of sensors on the battlefield. The aim of this paper is to investigate the weapon system portfolio probl...

Economic dispatch of power system containing wind power and photovoltaic considering carbon trading and spare capacity variation

ABSTRACTThe drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.

Research on Image Enhancement Method Based on Adaptive Immune Genetic Algorithm

Research on Image Enhancement Method Based on Adaptive Immune Genetic Algorithm

An Improved Immune Genetic Algorithm for Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem is an NP-hard problem. In this paper, it proposes a kind of dynamically adaptive immune genetic algorithm with automatic immune monitoring function. By setting an immune monitor, the algorithm automatically obtains the timing of regulating the antibody concentration and vaccination, and the dynamic antibody concentration regulation mechanism is designed to keep the variety of the antibody evolution process and prevent the algorithm from getting in trouble with local optimal solution. What is more, the manner of vaccination and extraction is designed to improve the global optimization of the algorithm, and the elite antibodies ensure the convergence. The experiment results show that the improved immune genetic algorithm takes good performance on the capacitated vehicle routing problem.

Study on Improved Multi-Objective Adaptive Artificial Immune Genetic Algorithm

In this paper, the traditional NSGA model in solving multi-objective optimization problem exists the computational complexity, lack of elitism and the need to set shared radius etc. defects. we use the advantage of the artificial immune system, such as good generalization, self-organization and so on, propose a improved multi-objective adaptive artificial immune genetic algorithm that by the use of fast non-dominated sorting and crowding distance, reduces the algorithm complexity and improves its stability and versatility; utilizes the immune memory cells to optimize the population quality, accelerate the antibody reaction speed and raise the optimize efficiency; updates the populations by the non-inferior rank and crowding distance to improve the algorithm function of the search. This paper elaborates the algorithm steps in detail and verifies this algorithm. Through the three different dimensions of test functions, the simulation results show that this algorithm is effective and feasible.

Research on Fault Diagnosis of Power System Based on Adaptive Immune Genetic Algorithm

In this article, the fault problem of power system is investigated with an adaptive immune genetic algorithm (AIGA) for the complications of fault. By introducing new crossover rate and mutation rate, considering the general characteristics of population, vaccines are extracted with dynamic self-adaption approach, thus avoiding the disadvantage of standard genetic algorithm. On the other hand, with the idea of survival of the fittest, the antibody population with low fitness is replaced by parts of new antibody generated randomly, which allows the variety of population. A general power system is employed to show the efficiency of the new method.

Parameter Optimization of PID Controller for Boiler Combustion System by Applying Adaptive Immune Genetic Algorithm

For power plant boiler combustion control system has large inertia, nonlinear and other complex characteristics, a control algorithm of PID optimized by means of adaptive immune genetic algorithm is presented. A variety of improved schemes of GA were designed, include: initial population generating scheme, fitness function design scheme, immunization strategy, adaptive crossover probability and adaptive mutation probability design scheme. By taking the rise time, error integral and overshoot of system response as the performance index, and using genetic algorithm for real-coded of PID parameters, then a group of optimal values were obtained. Simulation results show that the method has a good dynamic performance, superior to the conventional PID controller.