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A method for determining crack tip location during the tuff crack propagation based on ultra-fast time resolution method and immune algorithm

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ENGINEERING FRACTURE MECHANICS 2025年 321卷

作者： Zhang, Mingyuan Shen, Muao Li, Dejian He, Manchao Li, Yingjun China Univ Min & Technol State Key Lab Tunnel Engn Beijing 100083 Peoples R China China Earthquake Adm Inst Geol State Key Lab Earthquake Dynam Beijing 100029 Peoples R China Inst Deep Underground Sci & Engn Beijing 100083 Peoples R China China Univ Min & Technol Sch Mech & Civil Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Phys Beijing 100190 Peoples R China

Rock is a typical brittle material, and the crack propagation rate of rock is approximately 300-700 m/s, and some even reach over 1000 m/s. This brought many challenges for accurately determining the crack tip location during the rock crack propagation by traditional high-speed photography. Because the crack propagated continuously within the shooting interval of highspeed photography, systematic errors emerged in the tracking of the crack - tip location and the determination of the crack propagation rates. Moreover, because the stress intensity factor (SIF) is a singularity parameter, determining the SIF first requires tracking the location of the crack tip in rock mechanics experiments. Thus, a new method combining the ultra-fast time resolution method, immune algorithm and DIC technology was proposed for quantitatively describing the crack propagation behavior of tuff samples. Its time resolution can reach 15 picoseconds. During this time interval, the crack propagation process can be considered frozen. Then continuous tracking of the crack tip location during the fracture process is realized by using the immune algorithm and digital image correlation (DIC) technology. Then, a series of threepoint bending tests were conducted, and the fracture trajectory was obtained via this method. In addition, the SIF at the real-time crack tip was determined based on linear elastic fracture mechanics (LEFM) with the Williams stress function. The experimental results showed that the whole-field strain distribution at the picosecond scale can be obtained with this method, and the key mechanical parameters of rock fracture were determined more accurately.

关键词： Digital image correlation (DIC) technology Ultrafast time resolution method Crack propagation Stress intensity factor immune algorithm Rockburst

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A Novel immune algorithm for Multiparty Multiobjective Optimization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1238-1252页

作者： Chen, Kesheng Luo, Wenjian Zhou, Qi Liu, Yujiang Xu, Peilan Shi, Yuhui Harbin Inst Technol Sch Comp Sci & Technol Guangdong Prov Key Lab Novel Secur Intelligence Te Shenzhen 518055 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Southern Univ Sci & Technol Sch Comp Sci & Engn Shenzhen 518055 Peoples R China

Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pareto front of each DM as much as possible. This poses challenges for evolutionary algorithms in terms of searching and selecting. To better solve MPMOPs, this paper proposes a novel approach called the multiparty immune algorithm (MPIA). The MPIA incorporates an inter-party guided crossover strategy based on the individual's non-dominated sorting ranks from different DM perspectives and an adaptive activation strategy based on the proposed multiparty cover metric (MCM). These strategies enable MPIA to activate suitable individuals for the next operations, maintain population diversity from different DM perspectives, and enhance the algorithm's search capability. To evaluate the performance of MPIA, we compare it with ordinary multiobjective evolutionary algorithms (MOEAs) and state-of-the-art multiparty multiobjective optimization evolutionary algorithms (MPMOEAs) by solving synthetic multiparty multiobjective problems and real-world biparty multiobjective unmanned aerial vehicle path planning (BPUAV-PP) problems involving multiple DMs. Experimental results demonstrate that MPIA outperforms other algorithms.

关键词： Optimization Autonomous aerial vehicles Search problems Vectors Safety Minimization Evolutionary computation Convergence Sorting Heuristic algorithms Multiparty multiobjective optimization evolutionary algorithm immune algorithm UAV path planning

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Single-stage filter-based local feature selection using an immune algorithm for high-dimensional microarray data

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APPLIED SOFT COMPUTING 2025年 172卷

作者： Wang, Yi Li, Wenshan Li, Tao Tian, Hao Hubei Univ Econ Sch Informat Engn Wuhan 430205 Peoples R China Chengdu Univ Informat Technol Sch Cybersecur Chengdu 610225 Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Peoples R China

Feature selection is crucial for disease classification and prognosis in high-dimensional microarray data, as it reduces dimensionality, enhances model accuracy, and improves computational efficiency. However, most existing methods rely on a global set of features and overlook the relationships between features and sample subspaces. To address this limitation, this study proposes a filter-based local feature selection method based on an immune algorithm (IA-FLFS). This method dynamically assigns a unique feature subset to each sample neighborhood, replacing reliance on a global feature subset. It incorporates three key innovations: (1) a single- stage, filter-based approach that considers feature interactions and is independent of any learning model, ensuring high effectiveness and efficiency for high-dimensional datasets;(2) an enhanced clonal selection algorithm is utilized to identify feature subsets, enhancing search capabilities through filter-based initialization, adaptive differential evolution-based mutation, and symmetric uncertainty-based local search. (3) thread-level parallelism is applied to each feature subset, significantly reducing computation time. Experimental results on twelve datasets demonstrate IA-FLFS's superior accuracy, efficiency, and ability to produce smaller feature subsets, outperforming fourteen state-of-the-art feature selection methods on most datasets. Notably, compared to other local feature selection algorithms, it achieves significant accuracy improvements on over eight datasets, highlighting its potential as a powerful and efficient tool for high-dimensional microarray analysis.

关键词： Local feature selection immune algorithm Evolutionary computation Gene selection

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immune algorithm

Immune algorithm

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4th World Congress on Intelligent Control and Automation

作者： Ge, H Mao, ZY SCUT CEIE Guangzhou 510631 Peoples R China

immune algorithm is a new optimization algorithm imitating the immune system to solve the multi-modal function optimization problem. This paper offers a newly modified immune algorithm based on several former immune A... 详细信息

ISBN: (纸本)0780372689

关键词： antigen antibody immune algorithm information entropy multi-modal function

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immune algorithm for discretization of decision systems in rough set theory

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第4期7卷 602-606页

作者： JIA Ping DAI Jian-hua CHEN Wei-dong PAN Yun-he ZHU Miao-liang Institute of Artificial Intelligence Zhejiang University Hangzhou 310027 China

Rough set theory plays an important role in knowledge discovery, but cannot deal with continuous attributes, thus discretization is a problem which we cannot neglect. And discretization of decision systems in rough set theory has some particular characteristics. Consistency must be satisfied and cuts for discretization is expected to be as small as possible. Consistent and minimal discretization problem is NP-complete. In this paper, an immune algorithm for the problem is proposed. The correctness and effectiveness were shown in experiments. The discretization method presented in this paper can also be used as a data pre- treating step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

关键词： Rough sets Discretization immune algorithm Decision system

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immune algorithm Combined with Estimation of Distribution for Traveling Salesman Problem

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2016年第Sup1期11卷 S142-S154页

作者： Xu, Zhe Wang, Yirui Li, Sheng Liu, Yanting Todo, Yuki Gao, Shangce Toyama Univ Fac Engn Toyama 9308555 Japan Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Taizhou Univ Coll Comp Sci & Technol Taizhou 225300 Jiangsu Peoples R China Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

This paper describes an artificial immune algorithm (IA) combined with estimation of distribution algorithm (EDA), named IA-EDA, for the traveling salesman problem (TSP). Two components are incorporated in IA-EDA to further improve the performance of the conventional IA. First, aiming to strengthen the information exchange during different solutions, two kinds of EDAs involving univariate marginal distribution algorithm and population-based incremental learning are altered based on the permutation representation of TSP. It is expected that new promising candidate solutions can be sampled from the constructed probabilistic model of EDA. Second, a heuristic refinement local search operator is proposed to repair the infeasible solutions sampled by EDA. Therefore, IA-EDA can alleviate the deficiencies of the conventional IA and can find better solutions for TSP by well balancing the exploitation and exploration of the search. Experiments are conducted based on a number of benchmark instances with size up to 100 000 cities. Simulation results show that IA-EDA is effective for improving the performance of the conventional IA and can produce better or competitive solutions than other hybrid algorithms. (C) 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： estimation of distribution algorithm immune algorithm probabilistic model traveling salesman problem hybridization

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immune algorithm with adaptive sampling in noisy environments and its application to stochastic optimization problems

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2007年第4期2卷 29-40页

作者： Zhang, Zhuhong Xin, Tu Guizhou Univ Guizhou Peoples R China

An immune optimization algorithm in noisy environments, suitable for high-dimenisional stochastic optimization problems, is proposed based on the hypothesis test and simplified immune metaphors of Immoral immunity. The focus of design is concentrated on constructing three types of operators: (1) population sampling that decides sampling sizes of both the current population and the memory set, (2) sample-allocation scheme, and (3) antibody evolution that is aimed at designing several immune operators to evolve some potential antibodies into better ones. The algorithm, depending on dynamic suppression radiuses and suppression probabilities of antibodies from evolving populations, can strongly suppress noise and rapidly discover the desired solution, even if prior information on noise is unknown. Experimental results and comparison with three well-known algorithms show that the proposed algorithm can achieve satisfactory performances including the quality of optimization, noise compensation and performance efficiency.

关键词： high-dimensional stochastic optimization immune algorithm adaptive sampling hypothesis test

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immune algorithm with dynamic environments and its application to greenhouse control

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OPTIMIZATION AND ENGINEERING 2010年第1期11卷 125-144页

作者： Zhang, Zhuhong Qian, Shuqu Guizhou Univ Coll Sci Inst Syst Sci & Informat Technol Guiyang 550025 Guizhou Peoples R China

A novel immune algorithm with dynamic environments (IADE), suitable for time-varying single-objective optimization problems with the static or variable dimension of design space, is proposed based on the immune response principle. Several immune operators, relying upon the functions or metaphors of somatic maturation, immune memory and immune cells, are designed to adapt the changing environment and track the location of the optimum. Especially, the environmental recognition rule and memory pool are established to speed up to search the optimum of the environment. Several algorithms reported are participated in comparison against IADE through using theoretical test problems and a practical greenhouse control problem. Preliminary experiments show that IADE can not only obtain great superiority, but also track rapidly time-varying environments. Comparative analysis illustrates IADE's potential value.

关键词： Dynamic optimization Artificial immune systems immune algorithm immune response Moving peak Greenhouse control

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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： Yang Chen Wenshan Li Junjiang He Tao Li Wenbo Fang Xiaolong Lan School of Cyber Science and Engineering Sichuan University No. 24 South Section 1 Yihuan Road Chengdu 610065 Sichuan China *** School of Cyber Science and Engineering Chengdu University of Information Technology No. 24 Section 1 Xuefu Road Chengdu 610225 Sichuan 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 for Selecting Optimum Services in Web Services Composition

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Wuhan University Journal of Natural Sciences 2006年第1期11卷 221-225页

作者： GAO Yan NA Jun ZHANG Bin YANG Lei GONG Qiang DAI Yu College of Information Science and Engineering Northeastern University Shenyang 110004 LiaoningChina

For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an immune algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.

关键词： Web services composition optimum selection immune algorithm

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