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2014 Second World Conference on Complex Systems (WCCS)

作者： Sahraoui, Mustapha Salem, Mohammed Khelfi, Mohamed Faycal Univ Oran Fac Appl & Exact Sci RIIR Lab Oran Algeria

ISBN: (纸本)9781479946471

This paper is dedicated to present one of newly developed evolutionary algorithm;we considered a new way to tune the PID controller of nonlinear systems where the parameters of the controller are optimized using the artificial immune algorithm (AIA) based on the immune system which defends the body against harmful diseases and infections. This work describes the application of AIA Technique based on two fitness functions to optimally tune the three terms of the classical PID controller for nonlinear process. Matlab simulations of the proposed algorithm are carried out over a mass-spring damper system. The obtained results indicate that artificial immune algorithm is a reasonable and effective method for parameter optimization of PID controllers.

关键词： artificial immune algorithm PID optimization system non lineair

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An Enhanced artificial immune Based Base Station Planning Approach for Wireless Networks with Uneven User Distribution 15

An Enhanced Artificial Immune Based Base Station Planning Ap...

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Yingying, Xu Zhiwen, Pan Nan, Liu Xiaohu, You Southeast University National Mobile Communications Research Laboratory Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (纸本)9798350324662

Base station (BS) planning has always been a challenging task, especially in ultra-dense heterogeneous Networks (HetNets) with miniatured and densely deployed BSs. In this paper, we propose an enhanced artificial immune based BS planning approach for two-layer HetNets with uneven user distribution. The goal is to minimize the deployment costs while ensuring coverage and capacity. BS locations is optimized and redundant BSs are eliminated. Simulation results demonstrate that the proposed scheme performs well in terms of convergence and finding the optimal solution. © 2023 IEEE.

关键词： artificial immune algorithm base station planning BS elimination heterogeneous network metaheuristic algorithms

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Multi-objective Pareto adaptive algorithm for capacitated lot-sizing problems in glass lens production

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APPLIED MATHEMATICAL MODELLING 2018年 53卷 731-738页

作者： Ho, Wen-Hsien Chiu, Yu-Hsien Chen, Yenming J. Kaohsiung Med Univ Dept Healthcare Adm & Med Informat 100 Shin Chuan 1st Rd Kaohsiung 807 Taiwan Kaohsiung Med Univ Hosp Dept Med Res 100 Shin Chuan 1st Rd Kaohsiung 807 Taiwan Natl Kaohsiung First Univ Sci & Technol Dept Logist Management 1 Univ Rd Kaohsiung 824 Taiwan

An island artificial immune algorithm based on the nonautonomous Lotka-Volterra equation is used to establish an adaptive algorithm for solving multi-objective Pareto capacitated lot-sizing problems in glass lens production under multiple constraints. Solving this complex nonlinear problem requires computations such as optimizing the use of production resources to meet customer demand while maximizing performance and competitiveness. The satisfactory experimental results obtained in this show that the proposed algorithm can be used to simplify the decision-making process when selecting lot sizes in glass lens production and other manufacturing processes. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Multi-objective problem artificial immune algorithm Capacitated lot-sizing problems Glass lens production Pareto optimal front

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Continual learning classification method and its application to equipment fault diagnosis

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APPLIED INTELLIGENCE 2022年第1期52卷 858-874页

作者： Li, Dong Liu, Shulin Gao, Furong Sun, Xin Changzhou Univ Sch Petr Engn Changzhou 213164 Peoples R China Hong Kong Univ Sci & Technol Dept Chem & Biol Engn Clear Water Bay Hong Kong Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China

Classification methods play a significant role in the fault diagnosis field. However, they cannot effectively recognize new types of fault data and improve their classification performance timely through learning the testing data, for they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system, we propose a continual learning classification method (CLCM) and apply it to equipment fault diagnosis. During the testing stage, it continually cultivates new memory cells and new types of memory cells through learning the testing data to improve its classification performance. It classifies the known types of data and clusters the new types of data. Experimental evidence on six well-known datasets from the UCI repository and ball bearing test data verified its effectiveness and superiority. Results show that it has better classification performance when the testing data include all types of data, and it outperforms the other methods when the testing data include new types of data, especially new types of labeled data. The fewer types of training data, the more advantages it has.

关键词： artificial immune algorithm Classification Clustering Continual learning Bearing Fault diagnosis

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A novel negative selection algorithm with hyper-rectangle detectors based on full coverage of state space for anomaly detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第1期45卷 719-730页

作者： Gu, Ming Li, Dong Gong, Lanlan Liu, Jia Liu, Shulin Changzhou Univ Sch Petr Engn Changzhou Peoples R China Shanghai Univ Sch Mech Engn & Automat Shanghai Peoples R China

The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions.

关键词： artificial immune algorithm negative selection algorithm anomaly detection hyper-rectangle detectors artificial intelligence

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Continual learning classification method with single-label memory cells based on the intelligent mechanism of the biological immune system

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期42卷 3975-3991页

作者： Li, Dong Gong, Lanlan Liu, Shulin Sun, Xin Gu, Ming Qian, Kun Changzhou Univ Sch Petr Engn Changzhou Peoples R China Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China

The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%.

关键词： Classification continual learning biological immune system machine learning artificial immune algorithm

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A new holistic systems approach to the design of heat treated alloy steels using a biologically inspired multi-objective optimisation algorithm

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ENGINEERING APPLICATIONS OF artificial INTELLIGENCE 2015年 37卷 103-114页

作者： Chen, Jun Mahfouf, Mandi Sidahrned, Gaffour Lincoln Univ Sch Engn Lincoln LN6 7TS England Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Sonatrach Div AVAL Downstream Act Oran Algeria

The primary objective of this paper is to introduce a new holistic approach to the design of alloy steels based on a biologically inspired multi-objective immune optimisation algorithm. To this aim, a modified population adaptive based immune algorithm (PAIA2) and a multi-stage optimisation procedure are introduced, which facilitate a systematic and integrated fuzzy knowledge extraction process. The extracted (interpretable) fuzzy models are able to fully describe the mechanical properties of the investigated alloy steels. With such knowledge in hand, locating the 'best' processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of steels is possible. The research has also enabled to unravel the power of multi-objective optimisation (MOP) for automating and simplifying the design of the heat treated alloy steels and hence to achieve 'right-firsttime' production. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimisation Fuzzy modelling artificial immune algorithm 'Right-First-Time' production of alloy steels

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A novel hybrid immune algorithm and its convergence based on the steepest descent algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2011年第4期218卷 1291-1296页

作者： Liu, X. Y. Zhang, A. L. Gao, Y. L. Zhao, W. Inner Mongolia Univ Technol Coll Sci Hohhot 010051 Peoples R China Changzhi Coll Dept Math Changzhi 046011 Peoples R China

This paper proposes a novel hybrid immune algorithm (HIA) that can overcome the typical drawback of the artificial immune algorithm (AIA), which runs slowly and experiences slow convergence. The HIA combines the adaptive AIA based on the steepest descent algorithm. The HIA fully displays global search ability and the global convergence of the immune algorithm. At the same time, it inserts a quasi-descent operator to strengthen its local search ability. A good convergence of the HIA with the quasi-descent idea is shown as well. Numerical experiment results show that the HIA successfully improves running speed and convergence performance. (C) 2011 Elsevier Inc. All rights reserved.

关键词： artificial immune algorithm Steepest descent algorithm Quasi-descent method Convergence

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A Memetic Multi-objective immune algorithm for Reservoir Flood Control Operation

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WATER RESOURCES MANAGEMENT 2016年第9期30卷 2957-2977页

作者： Qi, Yutao Bao, Liang Sun, Yingying Luo, Jungang Miao, Qiguang Xidian Univ Sch Comp Sci & Technol 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Software 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China Xian Univ Technol Inst Water Resources & Hydroelect Engn 5 South Jinhua Rd Xian 710048 Shaanxi Peoples R China

Reservoir flood control operation (RFCO) is a challenging optimization problem with multiple conflicting decision goals and interdependent decision variables. With the rapid development of multi-objective optimization techniques in recent years, more and more research efforts have been devoted to optimize the conflicting decision goals in RFCO problems simultaneously. However, most of these research works simply employ some existing multi-objective optimization algorithms for solving RFCO problem, few of them considers the characteristics of the RFCO problem itself. In this work, we consider the complexity of the RFCO problem in both objective space and decision space, and develop an immune inspired memetic algorithm, named M-NNIA2, to solve the multi-objective RFCO problem. In the proposed M-NNIA2, a Pareto dominance based local search operator and a differential evolution inspired local search operator are designed for the RFCO problem to guide the search towards the and along the Pareto set respectively. On the basis of inheriting the good diversity preserving in immune inspired optimization algorithm, M-NNIA2 can obtain a representative set of best trade-off scheduling plans that covers the whole Pareto front of the RFCO problem in the objective space. Experimental studies on benchmark problems and RFCO problem instances have illustrated the superiority of the proposed algorithm.

关键词： Multi-objective optimization artificial immune algorithm Memetic algorithm Reservoir flood control operation

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Solving scheduling tournament problems using a new version of CLONALG

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CONNECTION SCIENCE 2015年第1期27卷 5-21页

作者： Perez-Caceres, Leslie Cristina Riff, Maria Univ Libre Bruxelles IRIDIA CoDE B-1050 Brussels Belgium Univ Tecn Federico Santa Maria Dept Informat Valparaiso Chile

The travelling tournament problem (TTP) is an important and well-known problem within the collective sports research community. The problem is NP-hard which makes difficult finding quality solution in short amount of time. Recently a new kind of TTP has been proposed 'The Relaxed Travelling Tournament Problem'. This version of the problem allows teams to have some days off during the tournament. In this paper, we propose an immune algorithm that is able to solve both problem versions. The algorithm uses moves which are based on the team home/away patterns. One of these moves has been specially designed for the relaxed travel tournament instances. We have tested the algorithm using well-known problem benchmarks and the results obtained are very encouraging.

关键词： relaxed travelling tournament problem artificial immune algorithm CLONALG

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