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作者： Qiu, Ziliang Shenzhen Power Supply Limited Company Shenzhen51800 China

The electrical industry is an important part of the energy industry and an important foundation of the national economy. Therefore, its safe power operation plays a very important role in social production and life. However, the current power system faces challenges in the scale and complexity of simulation calculations, so it is very important to study the dynamic equivalence of power systems. Based on the artificial immune algorithm, this paper studies and designs a power system dynamic equivalent system. First, this article explains the concept of artificial immunity, and discusses the application of artificial immune algorithms in depth;then, a system framework for power system dynamic equivalence is designed, and its dynamic equivalence changes are verified. The final results show that the model based on the equivalent of artificial immune algorithm can reflect the dynamic power characteristics of the external system well, and it can also be suitable for the study of static characteristics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial immunity Dynamic equivalent immune algorithm Power system

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Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 78卷

作者： Chai, Zhengyi Li, Wangwang Li, Yalun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & infomat Engn Tianjin 300387 Peoples R China

Feature selection (FS) is a significant data preprocessing technology in many fields. FS generally has two primary conflicting goals: 1) reducing features number and 2) improving classification accuracy, so it can be considered as a multi-objective optimization problem. This paper studies a symmetric uncertainty (SU) based decomposition multi-objective immune algorithm for feature selection, called MOIA/D-SU. Three novel strategies are proposed in MOIA/D-SU, namely population initialization strategy (PIS), proportional clone strategy (PCS) and population update strategy (PUS). The symmetric uncertainty based PIS calculates the SU value between each feature and class label, and initializes the population based on SU value. In the PCS, a new fitness function is defined to evaluate the performance of solutions, and then the proportional cloning process of excellent solutions will be performed to form a clone population that can be used to guide the evolution of the population. PUS uses the generated new solution to update the neighbor of the weight vector that minimizes the Tchebycheff distance of this new solution, instead of updating the neighbor of the current weight vector, so the update process is more efficient. Extensive experiments are performed on 13 benchmark data sets and compared the proposed method with five state-of-the-art multi-objective evolutionary algorithms. The experimental results demonstrate that MOIA/D-SU can better balance these two conflicting goals, and can achieve higher classification accuracy.

关键词： Classification Feature selection Decomposition -based method immune algorithm Symmetric uncertainty

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Weak Population-Empowered Large-Scale Multiobjective immune algorithm

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期2025卷

作者： Chen, Yang Li, Wenshan He, Junjiang Li, Tao Fang, Wenbo Lan, Xiaolong Sichuan Univ Sch Cyber Sci & Engn 24 South Sect 1Yihuan Rd Chengdu 610065 Sichuan Peoples R China Chengdu Univ Informat Technol Sch Cyber Sci & Engn 24 Sect 1Xuefu Rd Chengdu 610225 Sichuan Peoples R China

The multiobjective immune optimization algorithms (MOIAs) utilize the principle of clonal selection, iteratively evolving by replicating a small number of superior solutions to optimize decision vectors. However, this method often leads to a lack of diversity and is particularly ineffective when facing large-scale optimization problems. Moreover, an overemphasis on elite solutions may result in a large number of redundant offspring, reducing evolutionary efficiency. By delving into the causes of these issues, we find that a key factor is that existing algorithms overlook the role of weak solutions during the evolutionary process. With this in mind, we propose a weak population-empowered large-scale multiobjective immune algorithm (WP-MOIA). The core of this algorithm is to construct, in addition to the traditional elite population, a cooperative evolutionary population based on a portion of the remaining solutions, referred to as the weak population. During the evolution, both populations work together: the elite population maximizes its advantageous status for local searches, focusing on exploitation, while the weak population seeks greater variation to escape its disadvantaged position, engaging in broader exploration. At the same time, the sizes of both populations are dynamically adjusted to collaboratively maintain the balance of evolution. Through comparisons with nine state-of-the-art multiobjective evolutionary algorithms (MOEAs) and four powerful MOIAs on 30 benchmark problems, the proposed algorithm demonstrates superior performance in both small-scale and large-scale multiobjective optimization problems (MOPs), and exhibits better convergence efficiency. Especially in large-scale MOPs, the new algorithm's performance nearly surpasses all 13 advanced algorithms being compared.

关键词： dynamic cooperation immune algorithm large-scale multiobjective optimization weak population

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immune algorithm-Based Optimal Control Method for Electrical Dispatching of Distribution Lines

Immune Algorithm-Based Optimal Control Method for Electrical...

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2022年第五届智慧教育与人工智能发展国际学术会议

作者： Ying Lang Xin Zhang College of Aeronautical Engineering Beijing Polytechnic

The electrical dispatching of distribution lines can ensure the safe and reliable operation of the power system. To this end, an optimal control method for electrical dispatching of distribution lines based on immune algorithm is proposed. By constructing an optimal control model for electrical dispatching of distribution lines under the artificial immune algorithm, solving it, and obtaining the optimal control results, the optimal control of electrical dispatching of distribution lines is realized. Experiments show that the scheduling control method can effectively reduce the power consumption of electrical equipment and achieve good scheduling results.

关键词： immune algorithm Distribution line Electrical scheduling Optimal control

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Location Selection of Electric Vehicle Charging Station Based on Improved immune algorithm 40

Location Selection of Electric Vehicle Charging Station Base...

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40th Chinese Control Conference (CCC)

作者： Xu, Di Pei, Wenhui Li, Yongjing ShandongJiaotong Univ Sch Informat Sci & Elect Engn Jinan 250357 Peoples R China

ISBN: (纸本)9789881563804

This paper is devoted to the location selection method for electric vehicle charging stations utilizing the improved immune algorithm. Firstly, the user satisfaction evaluation function of electric vehicles is proposed based on the satisfaction optimization theory. Then, mathematical model of users' satisfaction is established. Based on the model, a novel optimal algorithm is designed. Because of the improvement of cross and mutation, the proposed strategy is more accurate and convergent than the conventional immune algorithm. Furthermore, the proposed algorithm is implemented based on the immune algorithm, which offers the best site selection for EVs. Finally, Jinan City was used as an example to conduct a simulation experiment.

关键词： Electric vehicles location selection satisfaction immune algorithm

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Electric Vehicle Charging Station Planning Based on immune algorithm and Voronoi Diagram 40

Electric Vehicle Charging Station Planning Based on Immune A...

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40th Chinese Control Conference (CCC)

作者： Li, Yongjing Pei, Wenhui Xu, Di Shandong Jiaotong Univ Sch Informat Sci & Elect Engn Jinan 250357 Peoples R China

ISBN: (纸本)9789881563804

With the rapid development of electric vehicles, charging stations have become an important part of charging facilities. In this paper, based on the traditional P-median model and the maximum coverage model, a charging station site selection planning model is established. Then a method of location optimization is proposed, which combines the immune algorithm and the voronoi diagram. Finally, in order to obtain the optimal selection, we analyzed the voronoi diagram with the simulation results. This is a new method to optimize the location of charging stations by using advanced algorithms. The feasibility of the method is verified by an example.

关键词： immune algorithm Voronoi diagram Charging station planning

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Research on Location Selection of Electric Vehicle Charging Station Based on Improved immune algorithm

Research on Location Selection of Electric Vehicle Charging ...

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International Conference on Smart Transportation and City Engineering

作者： Fu, YanXin Lanzhou Jiaotong Univ Network Informat Ctr Lanzhou 730070 Gansu Peoples R China

ISBN: (数字)9781510649767

ISBN: (纸本)9781510649767;9781510649750

With the rapid development of technology and the strong support of the government, the development of electric vehicles is getting faster and faster. One of the most important factors affecting the development of electric vehicles is the construction of charging stations. In order to solve the problem of electric vehicle charging station construction, a comprehensive model of electric vehicle charging station location is proposed. In the model, the influence of the distance from the charging user to the charging station and the traffic volume of the road network on the selection of the user's charging station is fully considered. The immune algorithm is introduced into the charging station location planning problem, and the antibody fitness and antibody concentration are fully considered when solving. In order to avoid the elimination of high fitness individuals, an improved adaptive immune algorithm is proposed. The algorithm dynamically adjusts the crossover probability and the concentration according to the fitness. The probability of mutation is to achieve the purpose of searching for the optimal solution of the problem as soon as possible. Taking the real data of 48 junction nodes in a certain area as an example, the optimal location and number of charging stations are found. Finally, through a comparative analysis of different algorithms, it is verified that the improved adaptive immune algorithm has faster convergence speed and better fitness value in solving the location of electric vehicle charging stations.

关键词： electric vehicle charging station location immune algorithm individual optimal strategy

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Many-objective optimization by using an immune algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 69卷 101026-101026页

作者： Su, Yuchao Luo, Naili Lin, Qiuzhen Li, Xia Shenzhen Univ Coll Elect & Informat Engn Shenzhen Peoples R China Shenzhen Univ Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Coll Optoelect Engn Shenzhen Peoples R China

Multiobjective optimization is important in practical engineering applications. With the increased number of objectives, multiobjective optimization becomes more challenging due to the difficulty of convergence in population selection. A number of many-objective evolutionary algorithms (MaOEAs) have been designed to enhance population selection, but studies selecting parents for evolution are still rare. Fortunately, multiobjective immune algorithms (MOIAs) provide a promising approach to select high-quality parents for evolution. However, the existing MOIAs are not effective for solving many-objective optimization problems (MaOPs), as these algorithms consider only the local information of solutions for cloning but ignore the global information of populations;consequently, the populations of these algorithms may easily be trapped in local optima. To solve this problem, this paper proposes a many-objective immune algorithm with a novel immune cloning operator. In this approach, the global information in the population is used to estimate the quality of each solution, and only a few offspring from high-quality parents are generated in each generation to improve the convergence and diversity of the population. When the proposed algorithm is compared with nine MaOEAs and six MOIAs on three MaOP benchmarks with 5, 10, and 15 objectives, the experimental results validate that the proposed algorithm obtains the best performance in most cases. Moreover, the effectiveness of the proposed algorithm is also validated on one real-world optimization problem.

关键词： immune algorithm Many-objective optimization Cloning operator

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Vertical distance-based clonal selection mechanism for the multiobjective immune algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2021年 63卷

作者： Li, Lingjie Lin, Qiuzhen Li, Ke Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter Devon England

Traditional multiobjective immune algorithms (MOIAs) widely use the domination relationship and crowding distance metric to run the cloning operator, which places more attention on nondominated solutions with larger crowding distance values. This "convergence-first " cloning principle may hamper population diversity. To better maintain population diversity in most traditional MOIAs, we propose a novel vertical distance-based clonal selection mechanism for MOIAs, called VD-MOIA in this paper, which arranges the cloning numbers for each solution based on the vertical distance value between the solution and its corresponding weight vector. First, the decomposition approach is used to decompose the target MOP into a set of subproblems, which are then optimized cooperatively and simultaneously. Second, the vertical distance-based clonal selection mechanism is performed, which puts more emphasis on the solutions with small vertical distance values. A large number of clones is allocated to the solutions that show good performance in terms of diversity, as these solutions are closer to their corresponding weight vectors than solutions that result in poor diversity. Moreover, the aggregated values quantified by the Chebyshev function are used to ensure convergence, while differential evolution (DE) is employed to strengthen the exploration ability of the algorithm, which further improves the population diversity as well. To validate the effectiveness of our proposed algorithm, twenty-eight multiobjective benchmark problems are adopted with complicated Pareto-optimal sets and fronts. The experimental comparison results validate the superiority of our proposed VD-MOIA when compared with four competitive MOIAs and four state-of-the-art multiobjective evolutionary algorithms.

关键词： Multiobjective optimization immune algorithm Clonal selection mechanism

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Parameter Identification of the Planar Integrated EMI Filter Based on the Improved immune algorithm

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APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL 2021年第1期36卷 20-26页

作者： Bai, Jinjun Zhao, Hong Liu, Yulei Guo, Kaibin Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China

The planar integrated EMI filter has been widely used because of its small size and good highfrequency characteristics. During the production process, if the electrical parameters of the integrated LC structure can be accurately identified, it will help improve the high-frequency characteristics of the filter. However, this identification problem is a multi-peak problem, which can easily fall into a local optimal solution. Based on the Feature Selective Validation method, this paper proposes an improved immune algorithm. The proposed method keeps changing the fitness function in the iterative process to avoid the algorithm falling into the local optimal solution. Finally, comparing with the measured impedance characteristic curve, it is verified that the proposed method is more accurate than the common immune algorithm.

关键词： immune algorithm parameter identification planar integrated EMI filter Feature selective validation

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