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A decomposition-based multi-objective immune algorithm for feature selection in learning to rank

KNOWLEDGE-BASED SYSTEMS 2021年 234卷 1页

作者： Li, Wangwang Chai, Zhengyi Tang, Zengjie Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Minist Educ Key Lab Universal Wireless Commun BUPT Tianjin Peoples R China

Learning-to-rank (L2R) based on feature selection has been proved effectively. However, feature selection problem is more challenging due to two conflicting objectives, namely, maximizing classification performance and minimizing the number of features. Therefore, in this article, a novel decomposition-based multi-objective immune algorithm for feature selection in L2R, called MOIA/DFSRank, is proposed. The proposed algorithm associates each solution with a scalar subproblem based on Tchebycheff decomposition approach, which makes the optimization process more efficient. In addition, we propose an Elite Selection Strategy (ESS) in initialization phase, which can significantly improve the diversity and convergence of the initial population. Moreover, the proposed algorithm uses two effective operators, a clonal selection operator and a mutation operator, where the clonal selection operator is to generate clone population to better guide the search direction of the evolution and the mutation operator aims to retain excellent features with a higher probability in evolution. In this paper, the pairwise method with O(n2) size is used to train the ranking model, and extensive experiments are conducted on the four public LETOR benchmark data sets. The experimental results demonstrate that the proposed algorithm can obtain significant performances on the ranking accuracy and the number of features. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Learning-to-rank Multi-objective optimization immune algorithm Decomposition Large-scale data

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An immune Genetic algorithm Based Combinatorial Auction Model in Smart Grid 38

An Immune Genetic Algorithm Based Combinatorial Auction Mode...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Wang, Yiran An, Dou Xi'an Jiaotong University Faculty of Electronic and Information Engineering Xi'an China

ISBN: (纸本)9798350303636

The application of distributed energy and renewable energy in the smart grid has broadened power sources and increased the diversity of power supply and demand. To meet the needs for combined electric energy, and to achieve the balance of power supply and demand, this paper chooses the transaction mechanism of the combinatorial auction to realize optimal energy scheduling. In the combinatorial auction, bidders can bid for any combination of all energies, which meets the diversified power needs of users. A combinatorial auction model to maximize the overall auction proceeds is constructed considering the needs of both the user and generation side. For the electricity trading optimal scheduling problem, this paper employs a genetic algorithm to solve the combinatorial auction model, and the immune operator is added to the algorithm to address the problem of degeneration and better extract features of the auction problem. Moreover, a large number of simulations are performed to verify the feasibility, effectiveness, and practicability of the immune genetic algorithm. The adaptive value of the population is improved and population degeneration is prevented. © 2023 IEEE.

关键词： Combinatorial auction electricity trading Energy optimal dispatching Genetic algorithm Heterogeneous energy immune algorithm

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Balancing Convergence and Diversity in Multiobjective immune algorithm 12

Balancing Convergence and Diversity in Multiobjective Immune...

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12th International Conference on Advanced Computational Intelligence (ICACI)

作者： Li, Lingjie Lin, Wu Lin, Qiuzhen Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781728142487

Recently, multiobjective immune algorithms (MOIAs) become popular, which are designed for multiobjective optimization problems (MOPs). However, most existing MOIAs put more attention on maintaining diversity as the used clonal selection strategy will allocate more cloning for the sparse areas, which may hamper the convergence to speed to the optimal Pare-to front, especially for some complicated MOPs. To alleviate the phenomenon mentioned above, we propose a dynamic mechanism into traditional MOIAs in this paper, aiming to balance convergence and diversity, called BCD-MOIA. First, MOP will be decomposed into several single subproblems by decomposition method, and then these subproblems will be optimized simultaneously. Second, we propose a novel measure metric instead of the crowding distance to assign the clone number for each solution, which includes two main parts. The first part focuses on the diversity performance, i.e., the perpendicular distance between solution and its associated weight vectors. The second part uses the aggregated function values quantified by the decomposition method, which is more efficient for accelerating the convergence speed and maintaining diversity as well. Moreover, a dynamic mechanism is performed during the whole evolutionary process, focusing on diversity and convergence at different stages. By this way, our proposed algorithm can tradeoff the performance on convergence and diversity dynamically. The effectiveness of our proposed algorithm BCD-MOIA is validated by comparing with three competitive MOIAs and three multi-objective evolutionary algorithms for tackling two sets of complicated problems.

关键词： immune algorithm multiobjective optimization problems convergence and diversity

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Improving an immune-inspired algorithm by linear regression: A case study on network reliability

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KNOWLEDGE-BASED SYSTEMS 2024年 299卷

作者： Cutello, Vincenzo Pavone, Mario Zito, Francesco Univ Catania Dept Math & Comp Sci Vle Andrea Doria 6 I-95125 Catania Italy

Bio-inspired algorithms are widely and successfully used in solving complex optimization problems. However, a common challenge faced by these algorithms is getting stuck in local search spaces and failing to explore new regions to further enhance the solution. Such a limitation is often attributed to the influence of the working parameters that need to be carefully tuned so to obtain an optimal performance. Unfortunately, finding the right parameter settings is problem -specific and can vary from one instance to another. To address this issue, this research investigates the integration of machine learning techniques with bio-inspired algorithms to dynamically adjust algorithm parameters based on solution quality. Specifically, the study focuses on an immune system -inspired algorithm and introduces a new strategy to mutate individuals in order to explore the search space more efficiently. By leveraging prediction techniques such as Linear Regression, the algorithm can predict local optima and adjust its search direction accordingly. The proposed method, referred to as Dynamic -IA, is evaluated using the Network Reliability Problem as a case study. Several network topologies are considered in our experiments and the results obtained by our algorithm when compared with the standard immune algorithm and other metaheuristic algorithms prove our strategy.

关键词： immune algorithm Metaheuristic Machine learning Ordinary least squares regression Network reliability maximization Dynamic mutation rate Network optimization

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Variational shadow quantum neural network based on immune optimisation algorithm

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QUANTUM INFORMATION PROCESSING 2024年第5期23卷 155-155页

作者： Dong, Yumin Zhu, Tingting Fu, Yanying Mou, Dingkang Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China

Quantum neural networks are neural network models based on the principles of quantum mechanics, a combination of the advantages of classical neural networks and quantum information, using quantum bits instead of neurons in classical neural networks and quantum gates instead of activation functions in classical neural networks. immune algorithms are a class of algorithms based on the theory of artificial immune systems, which implement functions similar to the biological immune system in terms of antigen recognition, cell differentiation, memory and self-regulation, using a population search strategy and iterative computation to eventually obtain the optimal solution to the problem with a higher probability. Quantum neural networks, one of the most important approaches in the field of quantum machine learning, can be implemented by means of variational quantum circuits. However, the classification training accuracy of variational quantum algorithms is not very high. In this paper, we propose a variational shadow quantum neural network based on the immune optimisation algorithm to classify data in order to improve the training accuracy and also the training speed. Experiments show that the immune optimisation algorithm-based variational shadow quantum neural network can not only improve the training accuracy, but also the training speed.

关键词： Quantum neural networks Variational quantum algorithms immune algorithm Optimisation

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

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

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第40届中国控制会议

作者： Yongjing Li Wenhui Pei Di Xu School of Information Science and Electrical Engineering Shandong Jiaotong University

With the rapid development of electric vehicles, charging stations have become an important part of charging *** 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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Location Selection of Electric Vehicle Charging Station Based on Improved immune algorithm

Location Selection of Electric Vehicle Charging Station Base...

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第40届中国控制会议

作者： Di Xu Wenhui Pei Yongjing Li School of Information Science and Electrical Engineering Shandong Jiaotong University

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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A preliminary study of automatic generation of credibility test cases based on immune algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第6-Sup期22卷 14867-14875页

作者： Yu, Xuejun Wang, Jing Beijing Univ Technol Dept Informat Beijing 100124 Peoples R China

As a part of software non-functional testing, credibility testing is an important means to ensure the quality of software. However, there are few researches on the credibility testing method at present. In this paper, a new method of generating Credibility test cases is proposed based on immune algorithm. By mining trustworthy requirements and combining with immune algorithm, a test case population is generated. Based on the result of test case, the Application Behavior Declaration is improved and a test case library is established. The conclusion proves that the trustworthy test cases based on immune algorithm can cover the credibility requirements and improve the testing efficiency.

关键词： immune algorithm Credibility Test case automatic generation

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A multi-objective immune algorithm for intrusion feature selection

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APPLIED SOFT COMPUTING 2020年 95卷 106522-106522页

作者： Wei, Wenhong Chen, Shuo Lin, Qiuzhen Ji, Junkai Chen, Jianyong Dongguan Univ Technol Sch Comp Sci Dongguan Peoples R China Shenzhen Univ Sch Comp & Software Engn Shenzhen Peoples R China

Feature selection plays a crucial role in classification problems, which tries to remove redundant or irrelevant features by mapping high-dimensional data to low-dimensional ones. Thus, this approach can improve the classification accuracy and reduce the computational cost to train the classification model. In this paper, we suggest an improved multi-objective immune algorithm (MOIA) for feature selection in intrusion detection. Specifically, the feature subsets for intrusion detection are treated as the individuals for immune optimization, which will select suitable feature subsets in order to reduce the dimensions of the dataset. After that, a neural network is used to train the classification model using the selected suitable feature subsets, and the output of the classification model is regarded as the target fitness value for each individual. As multiple attack types are considered in this paper, a traditional MOIA is modified by using an elite selection strategy based on the reference vectors, which can maintain the individuals with more promising performance when distinguishing more than five attack types in intrusion detection. By this way, the proposed algorithm can accelerate the convergence speed of classification, which also improves the classification accuracy. Experimental results on the NSL-KDD and UNSW-NB15 datasets validate the higher classification accuracy of the proposed algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Feature selection immune algorithm Multi-objective optimization Intrusion detection

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An adaptive immune algorithm for service-oriented agricultural Internet of Things

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Neurocomputing 2019年 344卷 3-12页

作者： Yang, Zhen Ding, Yongsheng Hao, Kuangrong Cai, Xin Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education College of Information Science and Technology Donghua University Shanghai China College of Information Engineering Huzhou University Huzhou China

Internet of Things (IoT) has the characteristics of multi-source, multi-type and unequal amount of task. Considering multiple requests at stochastic moment in the agricultural IoT service, an optimization model of minimum service cost is built in this paper, and an adaptive immune algorithm with endocrine regulation (AIE) is proposed to solve the problem of dynamic service for multiple requests. Based on this model, two service providing strategies are put forward, i.e. single service and collaborative service. The former means that one request task is completed by one service provider, while the latter means that one request task is collaboratively completed by multiple service providers. The service cost and service time are respectively evaluated under two service providing strategies. The simulation results verify the feasibility and effectiveness of the proposed AIE through comparison to the immune algorithm without endocrine regulation. Meanwhile, it is demonstrated that the proposed AIE can obtain more excellent performance than genetic algorithm and particle swarm optimization. © 2019

关键词： Agricultural Dynamic service immune algorithm Internet of Things (IoT) Multiple requests

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