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2nd International Symposium on Knowledge Acquisition and Modeling

作者： Dai, Hongwei Li, Cunhua Huaihai Inst Technol Sch Comp Engn Lianyungang Jiangsu Peoples R China

ISBN: (纸本)9780769538884

Immune clonal selection algorithm (ICSA), based on the clonal selection theory proposed by Burnet, has gained much attention in the last decade. ICSA can exploit and explore the solution space in parallel and effectively. However, antibody random initialization and premature convergence are two problems of ICSA. To solve these problems, chaotic initialization mechanism and quantum interference mechanism are introduced into ICSA. Simulation results based on the traveling salesman problems have demonstrated the efficiency and robustness of the novel clonal selection algorithm.

关键词： clonal selection algorithm chaotic initialization quantum crossover hybrid

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Knowledge Extraction of Agricultural Data Using Artificial Immune System

Knowledge Extraction of Agricultural Data Using Artificial I...

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International Conference on Fuzzy Systems and Knowledge Discovery

作者： Ayodele Lasisi Rozaida Ghazali Fola Lasisi Tutut Herawan Mustafa Mat Deris Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia 86400 Parit Raja Batu Pahat Johor Malaysia Department of Agricultural Engineering Obafemi Awolowo University Ile-Ife Nigeria Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

ISBN: (纸本)9781467376839

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (clonalG) and artificial immune recognition system (AIRS) form the bedrock of this paper. A fuzzy-rough feature selection (FRFS) method is coupled with clonalG and AIRS for improved detection and computational efficiency. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the clonalG and AIRS produced significant results. Their respective FRFS upgrades namely;FRFS - clonalG and FRFS - AIRS are able to generate highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

关键词： data mining clonal selection algorithm artificial immune recognition system fuzzy-rough set

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A novel artificial immune clonal selection classification and rule mining with swarm learning model

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CONNECTION SCIENCE 2013年第2-3期25卷 75-127页

作者： Al-Sheshtawi, Khaled A. Abdul-Kader, Hatem M. Elsisi, Ashraf B. King Abdulaziz Univ Dept Informat Technol Jeddah 21589 Saudi Arabia Menoufia Univ Fac Comp & Informat Dept Informat Syst Menoufia Egypt Menoufia Univ Fac Comp & Informat Dept Comp Sci Menoufia Egypt

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid clonal selection Classification and Rule Mining with Swarm Learning algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of clonal selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, clonalG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naive Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

关键词： artificial immune system clonal selection algorithm particle swarm optimisation hybrid mutation

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Design of Optimizing Stack Filters by an Ant Colony-clonal selection algorithm

Design of Optimizing Stack Filters by an Ant Colony-Clonal S...

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Chinese Conference on Pattern Recognition/1st CJK Joint Workshop on Pattern Recognition

作者： Tian, Ye Zhao, Chunhui Harbin Normal Univ Coll Phys & Elect Engn Harbin 150025 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781424441990

Stack filters are a class of nonlinear digital filters that satisfy a weak superposition property known as the threshold decomposition and stacking property. They have good performances of suppressing noises and preserving details. Their design is formulated as a course of optimizing PBF. This paper presents an ant colony-clonal selection algorithm for stack filters' optimizing. Under the MMAE criterion, the capability of the stack filters optimized by this method has been proven through simulation. And the results prove its advancing compared with the stack filters optimized by genetic algorithm, particle swarm algorithm and clonal selection algorithm.

关键词： Stack filters clonal selection algorithm Ant colony algorithm MMAE criterion Image processing

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An Improved clonal selection algorithm for Job Shop Scheduling

An Improved Clonal Selection Algorithm for Job Shop Scheduli...

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2009 International Symposium on Intelligent Ubiquitous Computing and Education(2009国际智能普适计算与教育研讨会 IUCE 2009)

作者： Hong Lu Jing Yang Department of Electronic Engineering Huaihai Institute of Technology Lianyungang China College of Electric Engineering Guizhou University Guiyang China

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes an improved immune clonal selection algorithm, called improved clonal selection algorithm for the JSSP. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times, and the results indicate the effectiveness and flexibility of the immune memory clonal selection algorithm.

关键词： artificial immune systems clonal selection algorithm job shop scheduling problem

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Stretching Technique-based clonal selection algorithm for Flexible Job-shop Scheduling

Stretching Technique-based Clonal Selection Algorithm for Fl...

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2009 International Conference on Computational Intelligence and Natural Computing(CINC 2009)

作者： Lu Hong Department of Electronic Engineering Huaihai Institute of Technology Lianyungang China

Flexible Job-shop Scheduling Problem （FJSP） is expanded from the traditional Job-shop Scheduling Problem （JSP）,which possesses wider availability of machines for all the operations. The aim is to find an allocation for each operation and to define the sequence of operations on each machine so that the resulting schedule has a minimal completion time. This paper introduces a hybrid metaheuristic,the stretching technique-based immune algorithm,consisting of a combination of the stretching technique and clonal selection algorithm （CSA）. The proposed method is used for solving the multi-objective FJSP. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP,especially for large scale problems.

关键词： immune systems clonal selection algorithm stretching technique flexible job shop scheduling problem

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A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer

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KNOWLEDGE-BASED SYSTEMS 2013年 54卷 128-136页

作者： Zhou, Zhi-Guo Liu, Fang Jiao, Li-Cheng Zhou, Zhi-Jie Yang, Jian-Bo Gong, Mao-Guo Zhang, Xiao-Peng Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China High Tech Inst Xian Xian 710025 Shaanxi Peoples R China Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England Peking Univ Sch Oncol Beijing Canc Hosp & InstDept Radiol Key Lab Carcinogenesis & Translat ResMinist Educ Beijing 100142 Peoples R China

Lymph Node Metastasis (LNM) in gastric cancer is an important prognostic factor regarding long-term survival. As it is difficult for doctors to combine multiple factors for a comprehensive analysis, Clinical Decision Support System (CDSS) is desired to help the analysis. In this paper, a novel Bi-level Belief Rule Based (BBRB) prototype CDSS is proposed. The CDSS consists of a two-layer Belief Rule Base (BRB) system. It can be used to handle uncertainty in both clinical data and specific domain knowledge. Initial BRBs are constructed by domain specific knowledge, which may not be accurate. Traditional methods for optimizing BRB are sensitive to initialization and are limited by their weak local searching abilities. In this paper, a new clonal selection algorithm (CSA) is proposed to train a BRB system. Based on CSA, efficient global search can be achieved by reproducing individuals and selecting their improved maturated progenies after the affinity maturation process. The proposed prototype CDSS is validated using a set of real patient data and performs extremely well. In particular, BBRB is capable of providing more reliable and informative diagnosis than a single-layer BRB system in the case study. Compared with conventional optimization method, the new CSA could improve the diagnostic performance further by trying to avoid immature convergence to local optima. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Clinical decision support system Gastric cancer Lymph node metastasis Belief rule base clonal selection algorithm

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FEM Calibrated ARMAX Model Updating Method for Time Domain Damage Identification

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ADVANCES IN STRUCTURAL ENGINEERING 2013年第1期16卷 51-60页

作者： Wang, Ying Khoo, Suiyang Li, An-jui Hao, Hong Deakin Univ Sch Engn Waurn Ponds Vic 3216 Australia Univ Western Australia Sch Civil & Resource Engn Crawley WA 6009 Australia

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. To do this, a finite element model of a steel pipe laid on the soil is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then built and calibrated. The calibrated ARMAX model is used to identify different damage scenarios through model updating process using clonal selection algorithm (CSA). The results demonstrate the application potential of the proposed method in identifying the pipeline conditions. To further verify its performance, laboratory tests of a steel pipe laid on the soil with and without soil support (free span damage) are carried out. The identification results of pipe-soil system show that the proposed method is capable of identifying damagein a complex structural system. Therefore, it can be applied to identifying pipeline conditions.

关键词： civil infrastructure damage identification ARMAX model time domain clonal selection algorithm finite element model

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Efficient Power Control and Clustering Method Based on Hybrid clonal Genetic algorithm in Wireless Sensor Networks

Efficient Power Control and Clustering Method Based on Hybri...

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International Conference on Information Technology and Management Innovation (ICITMI2012)

作者： Zhu, Xiangxian Lu, Sufeng Suzhou Inst Ind Technol Suzhou 215104 Peoples R China Huaian Coll Informat Technol Huaian 223003 Peoples R China

ISBN: (纸本)9783037855744

Wireless. sensor networks (WSNs) lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. How to manage the combination of the sensor nodes efficiently to prolong the whole network's lifetime while insuring the network reliability, it is one of the most important problems to research in WSNs. An effective optimization framework is then proposed, where genetic algorithm and clonal selection algorithm are hybridized to enhance the searching ability. Our goal can be described as minimizing the number of active nodes and the scheduling cost, thus reducing the overall energy consumption to prolong the whole network's lifetime with certain coverage rate insured. We compare the proposed algorithm with different clustering methods used in the WSNs. The simulation results show that the proposed algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station.

关键词： wireless sensor networks genetic algorithm clonal selection algorithm clustering base station

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FEM Calibrated ARMAX Model Updating Method for Time Domain Damage Identification

FEM Calibrated ARMAX Model Updating Method for Time Domain D...

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14th Asia Pacific Vibration Conference (APVC) on Dynamics for Sustainable Engineering

作者： Wang, Ying Khoo, Suiyang Li, An-jui Hao, Hong Deakin Univ Sch Engn Waurn Ponds Vic 3216 Australia Univ Western Australia Sch Civil & Resource Engn Crawley WA 6009 Australia

关键词： civil infrastructure damage identification ARMAX model time domain clonal selection algorithm finite element model

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