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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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Trajectory tracking control of a manipulator based on an immune algorithm-optimized neural network in the presence of unknown backlash-like hysteresis

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APPLIED MATHEMATICS AND COMPUTATION 2024年 470卷

作者： Chen, Jiqing Zhang, Haiyan Zhu, Tongtong Pan, Shangtao Guangxi Univ Sch Mech Engn Nanning Peoples R China

A neural network control strategy based on the immune algorithm is proposed to address the problem of chattering and trajectory tracking performance degradation of multi -joint manipulators due to unknown backlash -like hysteresis and system uncertainty. The controller is designed based on backstepping sliding mode technology and is suitable for Euler-Lagrange nonlinear systems. Firstly, to deal with the uncertainty problem of the manipulator model, we consider using radial basis function neural networks (RBFNN) to approximate, and on this basis, we add immune algorithms to optimize the RBFNN parameters to improve the approximation ability. Secondly, to compensate for the unknown backlash -like hysteresis error and RBFNN approximation error, an adaptive reaching law is designed to improve the control accuracy and reduce chattering to a certain extent. Finally, taking a 3-DOF manipulator as the research object, based on comparative simulation results, the feasibility and superiority of this control method can be proven.

关键词： immune algorithm Trajectory tracking Backlash-like hysteresis Radial basis function neural networks

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Learning-Inspired immune algorithm for Multiobjective-Optimized Multirobot Maritime Patrolling

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IEEE INTERNET OF THINGS JOURNAL 2024年第6期11卷 9870-9881页

作者： Huang, Li Zhou, Mengchu Han, Hua Wang, Shouguang Albeshri, Aiiad Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Zhejiang Gongshang Univ Coll Informat & Elect Engn Hangzhou Peoples R China King Abdulaziz Univ Dept Comp Sci Jeddah 21481 Saudi Arabia

Multirobot patrolling systems with various sensing and communications devices are deployed to guarantee maritime safety. Patrolling path planning for multiple robots can be modeled as a multiobjective optimization problem. The positions of patrolling nodes impact the length of patrolling paths and execution efficiency of robots. To compute them, a huge solution space is encountered. Besides, multiple patrolling nodes on the same line lead to the same patrolling scheme. Thus, how to promote solution (population) diversity becomes a new challenge. To tackle it, this work proposes a learning-inspired immune algorithm. It uses the historical information in the previous generations during iterations to realize a learning process. Unlike saving all the individuals themselves and training a model for them, the useful historical information is extracted by using upper confidence bound-based and actor-critic-inspired methods. Both time consumption and storage space can be dramatically saved. The experimental results indicate that the proposed algorithm can generate multiple patrolling schemes for the decision makers and outperforms the state-of-the-art.

关键词： Actor-critic immune algorithm multiobjective optimization multirobot maritime patrolling upper confidence bound (UCB)

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Optimal OCR coordination in a high penetration distribution power system using a refined immune algorithm with an auto-tuning reproductive mechanism

AIMS ENERGY

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AIMS ENERGY 2024年第6期12卷 1225-1263页

作者： Zhan, Tung-Sheng Lee, Yih-Der Jiang, Jheng-Lun Natl Kaohsiung Univ Sci & Technol Dept Elect Engn 415Jiangong Rd Kaohsiung 80778 Taiwan Natl Atom Res Inst Dept Elect Engn & Informat Technol 1000 Wenhua Rd Taoyuan City 32546 Taiwan

Integrating an increasing number of distributed energy resources into medium-voltage and low-voltage radial distribution networks is causing significant shifts in power flow and fault current distribution. These changes introduce new challenges for power system protection coordination. We present an adaptive protection coordination strategy designed to address these challenges. The proposed approach involved tracking the connectivity of the system structure to establish a relay numbering sequence, which served as a tracking route. These routes were further categorized into main feeder and branch paths based on the system topology. The strategy to optimize the operation time of overcurrent relays involved adjusting the time multiplier setting (TMS) and pickup current setting (PCS) for each relay, focusing on improving relay coordination. The coordination problem was formulated to minimize the total operation time of both primary and backup relays while adhering to coordination time interval (CTI) constraints. A refined immune algorithm, augmented with an auto-tuning reproductive mechanism, was proposed to determine the optimal time multiplier settings and pickup current settings parameters along the tracking route. We used a 16-bus actual distribution network and the IEEE 37 Bus system with distributed generators to evaluate the effectiveness of the proposed adaptive protection coordination. The results demonstrated that the proposed algorithm significantly reduced overall operation time and mitigated the impact on protection coordination settings following the integrations. Furthermore, a comparative analysis with other metaheuristic algorithms highlighted the superior efficiency and performance of the proposed approach.

关键词： protection coordination time multiplier setting pickup current setting coordination time interval immune algorithm

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Dynamic Path Planning of Vehicles Based on the Adaptive Potential Field and Hierarchical Replacement immune algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 12113-12129页

作者： Pan, Yuheng Tao, Yixin Lu, Weijia Li, Guoyan Cong, Jia Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China

A dynamic path planning method combining the adaptive potential field with the hierarchical replacement immune algorithm is proposed to realize the optimal navigation path and real-time obstacle avoidance. An improved ant-crawling mechanism, which incorporates the initial pheromones and heuristic information, is designed to achieve the initial population viability. Then to select superior antibodies from this initial population, the elite retention strategy and the roulette approach are applied simultaneously. According to the affinity, the number of antibodies is adaptively adjusted using the novel clone hierarchy model. Meanwhile, a new replacement mutation operator and adaptive replacement probability function are designed to produce better individuals. Finally, an adaptive-potential-field obstacle avoidance strategy is introduced to predict the imminent collision between vehicles and dynamic obstacles and activate the artificial potential field to replan the local path. The experiments prove that the method can improve the quality of the global path and realize real-time dynamic obstacle avoidance to ensure unmanned vehicle safety. The results show that the program running time, convergence iterations and the number of turns can be reduced by 87.35, 64.85 and 18.18%, respectively, in the complex environment.

关键词： Global path planning Dynamic obstacle avoidance Ant-crawling mechanism immune algorithm Artificial potential field

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An improved immune algorithm with parallel mutation and its application

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第7期20卷 12211-12239页

作者： Liu, Lulu Wang, Shuaiqun Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

The objective of this paper is to design a fast and efficient immune algorithm for solving various optimization problems. The immune algorithm (IA), which simulates the principle of the biological immune system, is one of the nature-inspired algorithms and its many advantages have been revealed. Although IA has shown its superiority over the traditional algorithms in many fields, it still suffers from the drawbacks of slow convergence and local minima trapping problems due to its inherent stochastic search property. Many efforts have been done to improve the search performance of immune algorithms, such as adaptive parameter setting and population diversity maintenance. In this paper, an improved immune algorithm (IIA) which utilizes a parallel mutation mechanism (PM) is proposed to solve the Lennard-Jones potential problem (LJPP). In IIA, three distinct mutation operators involving cauchy mutation (CM), gaussian mutation (GM) and lateral mutation (LM) are conditionally selected to be implemented. It is expected that IIA can effectively balance the exploration and exploitation of the search and thus speed up the convergence. To illustrate its validity, IIA is tested on a twodimension function and some benchmark functions. Then IIA is applied to solve the LJPP to exhibit its applicability to the real-world problems. Experimental results demonstrate the effectiveness of IIA in terms of the convergence speed and the solution quality.

关键词： immune algorithm mutation operators Cauchy mutation Gaussian mutation lateral mutation Lennard-Jones potential problem

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Trajectory tracking system of wheeled robot based on immune algorithm and sliding mode variable structure (Publication with Expression of Concern)

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INTELLIGENT SERVICE ROBOTICS 2023年第2期16卷 231-241页

作者： Lan, Yanting Chen, Xiaodong North Univ China Sch Elect & Control Engn Taiyuan Peoples R China China Agr Univ Coll Agron Beijing Peoples R China

An immune algorithm and a technique of sliding mode variable structure control (VSC) are proposed to achieve real-time and accurate control of wheeled robot in view of uncertainties for time-varying, strong coupling and nonlinear characteristics of wheeled robots, coupled with change of load and influence of external disturbance in traditional control algorithm based on classical control theory. First, the VSC parameters are adjusted online by the immune algorithm, which overcomes limitation that the parameters of reaching law need to be set in advance in conventional VSC. Secondly, the algorithm not only retains advantages of the traditional reaching law, but also effectively improves control quality and eliminates chattering of the system. The experimental results show that the control technique makes the wheeled robot move on sliding surface in an ideal way, for main concern of this paper is to provide an effective systemic for control of wheeled robot.

关键词： immune algorithm Sliding mode control Trajectory tracking Wheeled robot

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Computing Offloading Decision Based on Multi-objective immune algorithm in Mobile Edge Computing Scenario

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WIRELESS PERSONAL COMMUNICATIONS 2023年第2期130卷 1025-1043页

作者： Zhu, Si-feng Sun, En-lin Zhang, Qing-hua Cai, Jiang-hao Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Tianjin Chengjian Univ Lib Tianjin 300384 Peoples R China

Mobile edge computing (MEC) technology enables mobile devices in communication network systems offloading tasks to edge servers, effectively reducing response time and energy consumption. However, task offloading decisions become a significant difficulty when the system's mobile and service device count rises. In this paper, the problem of response time and energy consumption of the system is modeled as a multi-objective optimization problem, and we design an improved evolutionary algorithm based on immune algorithm, which can effectively obtain a set of solutions between response time and energy consumption. The simulation results show that our scheme can meet the response time requirements and obtain a lower energy consumption strategy when compared to the offloading scheme in the existing literature.

关键词： Mobile edge computing Computing offloading Multi-objective algorithm immune algorithm

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