Immune Evolutionary Algorithm Research Articles

Techno-Economic Optimization Study of Interconnected Heat and Power Multi-Microgrids with a Novel Nature-Inspired Evolutionary Method

The world is once again facing massive energy- and environmental challenges, caused by global warming. This time, the situation is complicated by the increase in energy demand after the pandemic years, and the dramatic lack of basic energy supply. The purely “green” energy is still not ready to substitute the fossil energy, but this year the fossil supplies are heavily questioned. Consequently, engineering must take flexible, adaptive, unexpected directions. For example, even the natural gas power plants are currently considered “green” by the European Union Taxonomy, joining the “green” hydrogen. Through a tight integration of highly intermittent renewable, or other distributed energy resources, the microgrid is the technology of choice to guarantee the expected impacts, making clean energy affordable. The focus of this work lies in the techno-economic optimization analysis of Combined Heat and Power (CHP) Multi-Micro Grids (MMG), a novel distribution system architecture comprising two interconnected hybrid microgrids. High computational resources are needed to investigate the CHP-MMG. To this aim, a novel nature-inspired two-layer optimization-simulation algorithm is discussed. The proposed algorithm is used to execute a techno-economic analysis and find the best settings while the energy balance is achieved at minimum operational costs and highest revenues. At a lower level, inside the algorithm, a Sequential Least Squares Programming (SLSQP) method ensures that the stochastic generation and consumption of energy deriving from CHP-MMG trial settings are balanced at each time-step. At the upper level, a novel multi-objective self-adaptive evolutionary algorithm is discussed. This upper level is searching for the best design, sizing, siting, and setting, which guarantees the highest internal rate of return (IRR) and the lowest Levelized Cost of Energy (LCOE). The Artificial Immune Evolutionary (AIE) algorithm imitates how the immune system fights harmful viruses that enter the body. The optimization method is used for sensitivity analysis of hydrogen costs in off-grid and on-grid highly perturbed contexts. It has been observed that the best CHP-MMG settings are those that promote a tight thermal and electrical energy balance between interconnected microgrids. The results demonstrate that such mechanism of energy swarm can keep the LCOE lower than 15 c€/kWh and IRR of over 55%.

An Immune Evolutionary Algorithm with Punishment Mechanism for Public Procurement Expert Selection

In the past decade, fairness in public procurement expert selection has attracted research attention. This paper proposes an immune evolutionary algorithm (IEA) with a punishment mechanism for expert selection, in which an ordered weighted aggregation (OWA) operator is applied to adjust the score weights to reduce expert evaluation committee abuse discretion and Grubbs method is employed to test the outliers. The results from a real-life public procurement case demonstrated that the abnormal experts could be effectively suppressed during the selection process and that the proposed method performed better than either the random selection algorithm or IEA, neither of which considers a punishment mechanism. Therefore, the proposed method, which applied the abnormal data detected in the scoring process to the expert selection process with a punishment mechanism, was proven to be effective in solving public procurement problems that may have doubtful or abnormal experts.

Multi-objective cooperative control based on evolutionary process optimization of immune algorithm

Based on evolutionary process optimization immune evolution algorithm, a multi-balloon cooperative control strategy is proposed, which targets at the problem that an artificial immune algorithm is apt to turn premature hastily when solving multi-objective problems. First, the control model of the multi-balloon robot is transformed into a multi-objective optimization problem, and an artificial immune algorithm is used to map and control, then Gaussian perturbation is added to the cloning behavior in the evolution process to ensure that there is a difference in each progeny after cloning. Weighting the most concentrated solution of the variation results, so that the scope of the solution is more diversified. Finally, the extinction mutations are performed on the extinct antibodies, which reduce the probability of antibody death and further maintains the diversity of the population. The simulation results show that compared with the standard support vector machine,the least squares support vector machine method and the standard artificial immune algorithm, the optimization result of the improved algorithm proposed in this paper is better in the solution of multi-objective optimization problem and the effect of the multi-balloon synergy control.

Dynamic prediction of uneven-aged natural forest for yield of Pinus taiwanensis using joint modelling.

In order to determine the stand age in the uneven-aged natural forest, a dynamic prediction model of stand volume and biomass was established in this study. In the model, the site quality grade was used as the dumb variable and the interval was used as the independent variable. In addition, the parameters of the model were estimated using immune evolutionary algorithm. The model was verified with the field data and the result revealed that the model had high accuracy. On this basis, the dynamic prediction model for forest stock was applied to evaluate the asset evaluation of uneven-aged natural forest and estimate carbon storage/sink potential of forest biomass. The selective logging period of the forest in the four plots was determined at the selective logging intensity of 40%. However, at the selective logging intensity of 40%, the forest ecological environment was suffered from the adverse effect to a certain extent from the perspective of scientific management, diversity of species, etc. Based on the comprehensive consideration of all the factors, it is recommended to set the selective cutting intensity in the range of 30 to 35%. The results can provide technical support for the application of selective logging income method in asset evaluation. Therefore, the results of this study have theoretical significance and practical application value in dynamic prediction of forest resources, asset evaluation and management, decision-making, etc.

Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment.

Medical data have the characteristics of particularity and complexity. Big data clustering plays a significant role in the area of medicine. The traditional clustering algorithms are easily falling into local extreme value. It will generate clustering deviation, and the clustering effect is poor. Therefore, we propose a new medical big data clustering algorithm based on the modified immune evolutionary method under cloud computing environment to overcome the above disadvantages in this paper. Firstly, we analyze the big data structure model under cloud computing environment. Secondly, we give the detailed modified immune evolutionary method to cluster medical data including encoding, constructing fitness function, and selecting genetic operators. Finally, the experiments show that this new approach can improve the accuracy of data classification, reduce the error rate, and improve the performance of data mining and feature extraction for medical data clustering.

A Biologically Inspired Energy-Efficient Duty Cycle Design Method for Wireless Sensor Networks

The recent success of emerging wireless sensor networks technology has encouraged researchers to develop new energy-efficient duty cycle design algorithm in this field. The energy-efficient duty cycle design problem is a typical NP-hard combinatorial optimization problem. In this paper, we investigate an improved elite immune evolutionary algorithm (IEIEA) strategy to optimize energy-efficient duty cycle design scheme and monitored area jointly to enhance the network lifetimes. Simulation results show that the network lifetime of the proposed IEIEA method increased compared to the other two methods, which means that the proposed method improves the full coverage constraints.

Spatio-temporal variation of groundwater contamination using IEA-UEF in urban areas of Jilin City, North-eastern China

Groundwater monitoring wells located in urban areas of Jilin City were sampled from 1980 to 2009 for eight groundwater quality parameters: pH, SO42−, Cl−, NO3−-N, NO2− -N, NH4+-N, F−, and total hardness (TH). The data were analysed by a universal exponential formula based on an immune evolutionary algorithm, and later mapped with the Kriging interpolation method. The primary objectives were to assess the main parameters that influenced groundwater quality and the spatio-temporal variability of groundwater contamination over several years. The results showed that NO3−, NH4+, and TH were the main parameters that influenced groundwater quality. Spatially, groundwater was polluted in all urban areas to varying degrees, and the Jiangbei district was the most heavily polluted location. Temporally, the groundwater contamination status could generally be classified into four stages and showed the following pattern during 1980–2009: heavy – light – heavy – light.

Risk Assessment of Drought Based on IEAPP-IDM in Qujing, Yunnan Province, China

A new model for risk assessment of drought based on projection pursuit optimized by immune evolutionary algorithm and information diffusion method (IEAPP-IDM) was proposed. Due to the fact that drought risk assessment is a complex multicriteria and multilevel problem, the IEAPP-IDM model can project the multidimensional indicators of samples into one-dimension projection scores; then, the information carried by the projection scores was diffused into drought risk levels; finally, the drought disaster risk estimate was obtained. In the present study, Qujing was employed to assess the drought risk with the proposed model. The results showed that Xuanwei possessed higher risk, while Luliang and Zhanyi possessed lower risk. At the same time, the probability risk of drought in Malong and Luoping was increasing, while the probability risk of drought in in Qilin and Shizong was decreasing. The results obtained by the assessment model are consistent with the actual situation of Qujing and verify the model’s effectiveness. The study can provide scientific reference in drought risk management for Qujing and other places of China.

Measurement uncertainty evaluation of conicity error inspected on CMM

The cone is widely used in mechanical design for rotation, centering and fixing. Whether the conicity error can be measured and evaluated accurately will directly influence its assembly accuracy and working performance. According to the new generation geometrical product specification(GPS), the error and its measurement uncertainty should be evaluated together. The mathematical model of the minimum zone conicity error is established and an improved immune evolutionary algorithm(IIEA) is proposed to search for the conicity error. In the IIEA, initial antibodies are firstly generated by using quasi-random sequences and two kinds of affinities are calculated. Then, each antibody clone is generated and they are self-adaptively mutated so as to maintain diversity. Similar antibody is suppressed and new random antibody is generated. Because the mathematical model of conicity error is strongly nonlinear and the input quantities are not independent, it is difficult to use Guide to the expression of uncertainty in the measurement(GUM) method to evaluate measurement uncertainty. Adaptive Monte Carlo method(AMCM) is proposed to estimate measurement uncertainty in which the number of Monte Carlo trials is selected adaptively and the quality of the numerical results is directly controlled. The cone parts was machined on lathe CK6140 and measured on Miracle NC 454 Coordinate Measuring Machine(CMM). The experiment results confirm that the proposed method not only can search for the approximate solution of the minimum zone conicity error(MZCE) rapidly and precisely, but also can evaluate measurement uncertainty and give control variables with an expected numerical tolerance. The conicity errors computed by the proposed method are 20%-40% less than those computed by NC454 CMM software and the evaluation accuracy improves significantly.

Displacement back analysis for underground engineering based on immunized continuous ant colony optimization

The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.

Optimization Study on Interval Number Judgment Matrix Weight Vector Based on Immune Evolution Algorithm

This paper focuses on the approach to solve the immune evolution optimization with interval number judgment matrix weight vector. In light of the features of interval number judgment matrix, we transform the solution problem of weights into the optimization one of nonlinear with restriction, design immune evolution algorithm on the base of immune mechanism, and use the convergence ability of immune system to search the optimal access to get interval numbers from matrix weight vector. Moreover, we construct immune operator and form vaccine and optimization strategies by priori knowledge of target problem, by which we can keep individual diversity and elite individuals and abandon useless individuals as soon as possible. In the evolution process, search can be dramatically improved by overcoming the problems of earliness and degradation during global search. Simulation results of experiments show the advantages of this algorithm in accuracy, convergence, convergence rate and so on.

Integral transport scheduling in emergency relief based on an immune evolutionary algorithm

The integral transport scheduling problem in emergency relief is studied with the following features: simultaneous transport of people and resources, integral forward and reverse transport, matching between scattered vehicles and demands. The proposed solution has two stages: first, feasible paths are generated by an exact algorithm and an immune evolutionary algorithm, all based on an open path optimisation algorithm; secondly, an integer linear programming model is designed to choose a set of paths from the set of feasible paths. The approach can be extended to be a part of integral logistics systems with scattered transport services and demands.

Multiclass maximum margin clustering via immune evolutionary algorithm for automatic diagnosis of electrocardiogram arrhythmias

Maximum margin clustering algorithm can obtain outstanding clustering performance by finding the maximum margin hyperplanes between clusters that can separate the data from different classes in an unsupervised way. However, it is only suitable for the clustering of small data set, since requires solving non-convex integer problem, which is computationally expensive. In this paper, to further improve the clustering performance, a new multiclass clustering method based on maximum margin clustering algorithm and immune evolutionary algorithm (IEMMMC) is proposed for diagnosis of electrocardiogram (ECG) arrhythmias. Five types of ECG arrhythmias obtained from MIT-BIH database are analyzed in the experiment, including normal sinus rhythm (N), premature ventricular contraction (PVC), atrial premature contraction (APC), fusion of ventricular and normal beat (FVN), fusion of paced and normal beat (FPN). And three types of performance evaluation indicators are used to assess the effect of the IEMMMC method for ECG arrhythmias, such as sensitivity, specificity and accuracy. Compared with K-means, fuzzy c-means and LS-SVM algorithms, our method reflects better performance not only in clustering result but also in terms of global search ability and convergence ability, which proves its effectiveness for the detection of ECG arrhythmias.

Application of particle swarm algorithm in the optimal allocation of regional water resources based on immune evolutionary algorithm

The optimal allocation model of regional water resources is built with the purpose of maximizing the comprehensive economic, social and environmental benefits of regional water consumption. In order to solve the problems that easily appear during the model solution of regional water resource optimal allocation with multiple water sources, multiple users and multiple objectives like “curse of dimensionality” or sinking into local optimum, this paper proposes a particle swarm optimization (PSO) algorithm based on immune evolutionary algorithm (IEA). This algorithm introduces immunology principle into particle swarm algorithm. Its immune memorizing and self-adjusting mechanism is utilized to keep the particles in the fitness level at a certain concentration and guarantee the diversity of population. Also, the global search characteristics of IEA and the local search capacity of particle swarm algorithm have been fully utilized to overcome the dependence of PSO on initial swarm and the deficiency of vulnerability to local optimum. After applying this model to the allocation of water resources in Zhoukou, we obtain the scheme for optimization allocation of water resources in the planning level years, i.e. 2015 and 2025 under the guarantee rate of 50%. The calculation results indicate that the application of this algorithm to solve the issue of optimal allocation of regional water resources is reliable and reasonable. Thus it offers a new idea for solving the issue of optimal allocation of water resources.

Information Security System of the New Energy Automobile Production Line Based on RFID

The application of RFID technology in the new energy automobile manufacturing, can achieve that the visualization, on time and collaborative business processes of energy vehicles discrete manufacturing production process, to establish a advanced "just-in-time" production model, provide real-time and accurate information for production plans and management decisions, which will help to improve the quality of enterprise production management decision-making and the overall optimization of production operations management functions. On the basis of proposed mainly from management systems and supporting techniques to solve the security issues of the RFID system, give the basic framework of build the RFID system safety management system. The security of the RFID system is technically divided into two security domains, focusing on security issues by the reader to the application system. The intrusion detection technology is introduced in RFID system, and research on the improved of intrusion detection from two aspects: First, the use of clustering algorithm for data preprocessing to reduce the computational complexity, improve the detection efficiency; second, use immune particle swarm evolutionary algorithm directly improved feature selection to improve detection accuracy.

A Novel Network Survivability Analysis and Evaluation Model

Network survivability has the characteristics of complexity, dynamic evolution and uncertainty, which has become one of the most important factors for analyzing and evaluating network performance. Network survivability analysis and evaluation is a process of analyzing and quantifying the degree to which network system can survive in network threats. This paper proposes a novel network survivability analysis and evaluation model.Firstly, network survivability is abstracted as a dynamic game process among network attacker, network defender and normal user, thereafter network survivability evolutionary game model is established and network survivability analysis algorithm is proposed based on the game model. Secondly, the survivability characteristics of the network can be measured and evaluated based on the analyzed information based on the proposed immune evolutionary algorithm for network survivability metric weight solving and network survivability evaluation method using multiple criteria decision making. Finally, the proposed network survivability analysis and evaluation model is experimented in a typical network environment and the correctness and effectiveness of the model is validated through experimental analysis.

Model and algorithms for dynamic and stochastic vehicle routing problem

This paper researched a dynamic vehicle routing problem of stochastic requests, in which at the scheduling moment t whether customers need service can be identified, but the demand is a random variable and submits to Poisson distribution. Based on model of M.H Lars, considering the multi-depot and open characteristics, two-stage stochastic programming models with resource are established. Then an adaptive immune quantum-inspired evolutionary algorithm (AIQEA) for this dynamic problem was proposed. In the AIQEA, in order to avoid the search process into the local minimum value an immune operator is introduced to optimise sub-routes. Finally, simulation examples are tested, and analyse the optimisation results of different values of the parameters l, experiment results show that this method can effectively solve the dynamic vehicle routing problem of stochastic requests.

Task Allocation of Multi-Robot Systems Based on a Novel Explosive Immune Evolutionary Algorithm

To solve the task allocation of multi-robot systems, a novel explosive evolution - based immune genetic algorithm (EIGA) is presented. On the basis of the immune genetic algorithm (IGA), the population number of EIGA is increased quickly through explosive evolutionary mode, and then the better individuals are selected through the comparison of allelic genes, which can improve the population quality with the premise of ensuring the population diversity, and enhance the search speed and search precision of EIGA. Compared with the IGA and genetic algorithm (GA), the simulation results indicate that the proposed EIGA is characterized by quick convergence speed, high optimization precision and good stability, and the tasks are allocated rationally and scientifi-cally which realizes the task cooperation of multi-robot systems well.

Monkey King Immune Evolutionary Algorithm

Monkey-King genetic algorithm has the shortages of the lower searching ability in the local area and further in the whole area at monkey-king point in spite of the advantages of the simple principle and easy calculation. Monkey-king point was optimized iteratively by using immune evolutionary algorithm. This method overcomes the premature convergence because of the optimal searching in the out as well as in of the monkey-king point. At the same time, with the lapse of iteration, the algorithm closes in the whole of optimal solution with the higher precision because of the gradual strengthening of local searching ability. Calculation and comparison with several methods, such as monkey-king genetic algorithm, improved monkey-king genetic algorithm and common climbing operator genetic algorithm et al, has made. The results show that the monkey-king immune evolutionary algorithm has the optimal searching ability and the stability all the better.

Adaptive Immune Evolutionary Algorithm for Multi-objective Function Optimization

Adaptive Immune Evolutionary Algorithm for Multi-objective Function Optimization