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A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem

JOURNAL OF INTELLIGENT MANUFACTURING 2022年第4期33卷 953-972页

作者： Liu, Junqi Zhang, Zeqiang Chen, Feng Liu, Silu Zhu, Lixia Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu 610031 Peoples R China

Aiming at the lack of relevant research on relationship constraints between facilities in the corridor allocation problem (CAP). In this paper, fixed position constraints and ordering constraints are considered in CAP, and the logistics cost is minimized. Considering that the existing search technology is complicated and time-consuming in dealing with such constrained CAP (cCAP), and immune clone selection algorithm with variable neighborhood operation (ICSAVNS) is provided for solving this problem. Two approaches to initial solution generation are designed to improve the quality of the initial population. A variable neighborhood search operator is embedded to improve the accuracy of the local search. A threshold is set in the mutation operation of the ICSAVNS to achieve population expansion better. A double index of sequences consisting of affinity values and constrained facility index values is used to select and reselect, achieving population compression in the clonal selection part. Finally, by exactly solving the model, the rationality of the model is verified. The hybrid clone selection algorithm is used to solve the cCAP and cbCAP benchmark instances of different sizes, and compared with the state-of-the-art optimization algorithms. The results show that the proposed algorithm exhibits better performance.

关键词： Facility layout Constrained corridor allocation problem immune clonal selection algorithm Variable neighbourhood search operation

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An ensemble kernel classifier with immune clonal selection algorithm for automatic discriminant of primary open-angle glaucoma

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NEUROCOMPUTING 2012年 83卷 1-11页

作者： Cheng, Lijun Ding, Yongsheng Hao, Kuangrong Hu, Yifan Donghua Univ Minist Educ Engn Res Ctr Digitized Text & Fash Technol Shanghai 201620 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

An ensemble kernel classifier is proposed in this paper by integrating a kernel principal component analysis (KPCA) with a support vector machine (SVM) as well as an immune clonal selection algorithm (ICSA). The KPCA approach is used to extract features, whereas the SVM technique is employed to deal with classification, and the ICSA is applied to optimize the parameters of the proposed scheme. The proposed ensemble classifier can automatically select the kernel type and optimize its parameter sets, in order to produce various SVM classifiers with different kernels. Regardless of whether the data is linear or nonlinear, an optimum classification result can be obtained. In order to demonstrate the effectiveness of the classifier, it is applied to discriminate the primary open-angle glaucoma (POAG) using a standard classification dataset. Experimental results reveal that the proposed ensemble classifier is accurate and more effective when compared to other approaches in the literature. It is envisaged that ensemble kernel classifier could hold a high potential in classification of pattern recognition problems. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Ensemble kernel classifier Kernel principal component analysis Support vector machine immune clonal selection algorithm Primary open-angle glaucoma

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Optimized Feature Extraction by immune clonal selection algorithm

Optimized Feature Extraction by Immune Clonal Selection Algo...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Zhang, Xiangrong Zhang, Erlei Li, Runxin Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

ISBN: (纸本)9781467315098

A new method of feature extraction based on immune clonal selection algorithm is proposed, in which the immune clonal selection algorithm is used to optimize the projection vector. Some orthogonal bases are randomly selected as the initial basis vector sets from the original feature space, and the direction of the basis vectors is optimized to generate the optimal projection vector using the immune clonal selection algorithm. This method provides a new scheme of applying the immune clonal algorithm to feature extraction. Experimental results on benchmark datasets and MSTAR dataset for SAR target recognition verify the effectiveness of the proposed method.

关键词： feature extraction immune clonal selection algorithm optimal projection vector

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SVM Optimized by immune clonal selection algorithm for Fault Diagnostics

SVM Optimized by Immune Clonal Selection Algorithm for Fault...

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Pacific-Asia Conference on Circuits, Communications and Systems

作者： Li, Dongyan Chen, Zhenguo N China Inst Sci & Technol Dept Comp Sci & Technol Beijing 101601 Peoples R China

ISBN: (纸本)9780769536149

This paper presents a fault diagnosis method using Support Vector Machines (SVM) and immune clonal selection algorithm (ICSA). Support Vector Machines (SVM) has been well recognized as a powerful computational tool for nonlinear problems which have high dimensionalities. Whereas the parameters in SVM are usually selected by man's experience, it has hampered the efficiency of SVM in practical application. Immunity clonal selection algorithm (ICSA) is a new intelligent algorithm which can carry out the global search and the local search in many directions rather than one direction around the same individual simultaneously, and can effectively overcome the prematurity and slow convergence speed of traditional evolution algorithm. To improve the capability of the SVM classifier, we apply the immunity clonal selection algorithm to optimize the parameter of SVM in this paper. The experimental result shows that the fault diagnostics based on SVM optimized by ICSA can give higher recognition accuracy than the general SVM.

关键词： fault diagnostics Support vector machines immune clonal selection algorithm

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Atmospheric Quality Assessment Model Based on immune clonal selection algorithm

Atmospheric Quality Assessment Model Based on Immune Clonal ...

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International Conference on Civil Engineering and Building Materials (CEBM)

作者： Wang, Limin Han, Xuming Li, Ming Zhang, Chijun Zhang, Yanting Changchun Univ Technol Inst Informat & Spreading Engn Changchun 130012 Peoples R China Jilin Univ Finance & Econ Sch Informat Changchun 130117 Peoples R China Int Sch Beijing Univ Posts & Telecommun Dept E Commerce Engn Law Beijing 102209 Peoples R China

ISBN: (纸本)9783037851395

The immune clonal selection algorithm is used to optimize the parameters in the formula of S growth curve index in the paper, thus we can obtain an assessment model for atmospheric comprehensive pollution that is suitable to the cases of multi-pollutants. Moreover the proposed assessment model is applied in the field of atmosphere assessment. Experimental results show that the assessment method proposed for atmosphere quality has many advantages such as pellucid principle, physical explication and correct assessment results etc. It is a new effective approach for intelligence theory and technology applied in the field of environment. Therefore it has great potential in the field of assessment the atmospheric quality.

关键词： immune clonal selection algorithm Parameter Optimization Assessment Model

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Dynamic Recurrent Elman Neural Network Based on immune clonal selection algorithm

Dynamic Recurrent Elman Neural Network Based on Immune Clona...

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4th International Conference on Digital Image Processing (ICDIP)

作者： Wang, Limin Han, Xuming Li, Ming Sun, Haibo Li, Qingzhao Changchun Univ Technol Inst Informat & Spreading Engn Changchun 130012 Peoples R China Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun 130117 Peoples R China Jilin Univ Finance & Econ Econ Simulat Res Inst Changchun 130117 Peoples R China

ISBN: (纸本)9780819489913

Owing to the immune clonal selection algorithm introduced into dynamic threshold strategy has better advantage on optimizing multi-parameters, therefore a novel approach that the immune clonal selection algorithm introduced into dynamic threshold strategy, is used to optimize the dynamic recursion Elman neural network is proposed in the paper. The concrete structure of the recursion neural network, the connect weight and the initial values of the contact units etc. are done by evolving training and learning automatically. Thus it could realize to construct and design for dynamic recursion Elman neural networks. It could provide a new effective approach for immune clonal selection algorithm optimizing dynamic recursion neural networks.

关键词： Dynamic threshold strategy immune clonal selection algorithm forecast multi-parameters optimization

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Optimum M-sequence search via immune clonal selection algorithm

Optimum M-sequence search via immune clonal selection algori...

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CIE International Conference on Radar

作者： Lian Jiaqi Luo Feng Wu Shunjun Yin Cailing Xidian Univ Natl Lab Radar Signal Proc Xian Shaanxi Peoples R China

ISBN: (纸本)9780780395824

The binary pulse compression codes such as the Barker code or m-sequences have been widely used in modern radar systems. The selection of the initial shift register of m-sequence, whose pulse compression waveform has low peak power and large compression ratios, is a key technique in pulse compression waveform design. Based on the antibody clonal selection theory of immunology, we propose the immune clonal selection algorithm (ICSA) to select the best initial shift register of m-sequences. Compared with the standard genetic algorithm (SGA) and a hybrid genetic algorithm (HGA), the approach we used can overcome the prematurity availably and has higher searching results. Experimental results prove the feasibility and validity of this algorithm.

关键词： M-sequences initial register immune clonal selection algorithm peak sidelobe ratio

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Locating and sizing of charging station based on neighborhood mutation immune clonal selection algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartB期215卷

作者： Wang, Yufei Liu, Donglin Wu, Yu Xue, Hua Mi, Yang Shanghai Univ Elect Power Coll Elect Engn Shanghai Peoples R China State Grid Shanghai Municipal Elect Power Co Shanghai Peoples R China

Considering the location problem of charging station caused by the popularity of electric vehicles, a locating and sizing method of charging station based on the neighborhood mutation immune clone selection algorithm is proposed. First, the K-MEANS clustering method is used to analyze the randomness of electric vehicles charging load;Secondly, the distance between EVs and charging station, and the capacity of charging station are combined to determine the service range of charging stations;Then, a planning model of charging station is established to minimize the annual cost of charging station under multiple constraints, which consider the actual load, charging power, charging distance and other factors;Last, an improved immune clonal selection algorithm with neigh-borhood mutation is proposed, which make it more suitable for the iterative solution of the planning model of charging stations. Using MATLAB to analyze the calculation examples, the results verify the effectiveness of the model and algorithm. When solving the charging station planning model, compared with the traditional immune clone selection algorithm and chaotic simulated annealing particle swarm optimization, the optimal solution ratio of the neighborhood mutation immune clone selection algorithm is increased by 55% and 20%, respec-tively, and the optimization accuracy is increased by 20% and 12%, respectively.

关键词： Electric vehicle immune clonal selection algorithm K -means clustering algorithm Locating and sizing method Neighborhood mutation

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Intelligent PID Controller Using immune clonal selection algorithm Based on FPGA Implementation

Intelligent PID Controller Using Immune Clonal Selection Alg...

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2011 International Conference on Management Science and Intelligent Control(ICMSIC 2011)

作者： HUANG Ming Anhui University of Science & Technology Institute of Electrical & Information Engineering Huainan, Anhui, 232001, China

It has quite high requirement to the real time adjustment of PID parameters for the real-time and non-liner in process of PID industrial control, so in this paper, the immune clone algorithm parameter optimization of PID controller, hardware, intelligent PID controller using FPGA design, software use DSP Builder to build closed-loop test system, and made in QuartusII Modelsim VHDL simulation design. The results show that the artificial immune algorithm to optimize the PID controller parameters are improved accuracy, the adaptive control system is enhanced, PID controller, real-time parallel computation in the FPGA and 3-stage pipeline is guaranteed under this theory and PID controller Far-reaching practical significance.

关键词： immune clonal selection algorithm PID Intelligent FPGA Modelsim SE DSP Builder

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The Application of an immune clonal selection algorithm Based on the Information Entropy in Truss Structure Multi-Objective Optimization

The Application of an Immune Clonal Selection Algorithm Base...

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2012 3rd International Conference on Applied Mechanics and Mechanical Engineering(ICAMME 2012)

作者： Changyuan Hu Hesheng Tang Lixin Deng Songtao Xue Research Institute of Structural Engineering and Disaster Reduction Tongji University State Key Laboratory for Disaster Reduction in Civil Engineering Tongji University

In order to solve the conflict multi-objective optimization of truss structures between the structure minimum weight and safety redundancy, the immune clonal selection algorithm based on information entropy was adopted in this paper. Based on the immunology theory, the non-dominated neighbor-based selection, proportional cloning and elitism strategy were introduced in the multi-objective immune clonal selection algorithm(MOICSA) to enhance the diversity, the uniformity and the convergence of the obtained solution. Mathematical models for truss multi-objective optimization design are constructed, in which the information entropy value of bar stress is taken as one of objective functions, and penalty function method was used to deal with violated constraints. Several classical problems are solved using the MOICSA algorithm, and the results compared with other optimization methods. The simulation results show that the method can achieve the effect of multiple-objective optimization successfully.

关键词： Information Entropy Multi-Objective Optimization Trusses Engineering immune clonal selection algorithm

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