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2010 IEEE World Congress on Computational Intelligence

作者： Liu, Ruochen Jiao, Licheng Zhang, Wei Ma, Jingjing Xidian Univ Inst Intelligent Informat Proc Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9781424481262

Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector quantization (M/RVQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, clonal selection algorithm for Image Compression (CSAIC) is proposed. In CSAIC, Based on M/RVQ algorithm, an improved clonal selection algorithm is used to cluster the data of compressed images in order to obtain the optimal codebook. The proposed method has been extensively compared with Linde-Buzo-Gray(LBG), Self-Organizing Mapping (SOM) and Modified K-means(Mod-KM) over a test suit of seven natural images. The experimental results show that CSAIC outperforms other three algorithms in terms of image compression performance.

关键词： Linde-Buzo-Gray clonal selection algorithm data compression data compression data vectors encoding time image coding image compression mean-residual vector quantization modified k-means optimal codebook self-organising feature maps selforganizing mapping vector quantisation

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Cumulative Updating of Network Reliability with Diameter Constraint and Network Topology Optimization 16th

Cumulative Updating of Network Reliability with Diameter Con...

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16th International Conference on Computational Science and Its Applications (ICCSA)

作者： Migov, Denis A. Nechunaeva, Kseniya A. Nesterov, Sergei N. Rodionov, Alexey S. Inst Computat Math & Math Geophys SB RAS Prospect Akad Lavrentjeva 6 Novosibirsk 630090 Russia

ISBN: (纸本)9783319420851

Reliability-based optimization of a network topology is to maximize the network reliability within certain constraints. For modeling of unrelaible networks we use random graphs due to their good applicability, wide facilities and profound elaborating. However, graph optimization problems in conditions of different constraints are NP-hard problems mostly. These problems can be effectively solved by optimization methods based on biological processes, such as genetic algorithms or clonal selection algorithms. As a rule, these techiques can provide an applicable solution for network topology optimization within an acceptable time. In order to speed up fitness function calculation, we improve operators of a genetic algorithm and a clonal selection algorithm by using the method of cumulative updating of lower and upper bounds of network reliability with diameter constraint. This method allows us to make a decision about the network reliability (or unreliability) with respect to a given threshold without performing the exhaustive calculation. Based on this method, we obtain the genetic algorithm and the clonal selection algorithm for network topology optimization. Some computational results are also presented for demonstration of an applicability of the proposed approach.

关键词： Network reliability Network topology optimization Genetic algorithm clonal selection algorithm Random graph Diameter constraint Factoring method Cumulative updating

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Predict Two-Dimensional Protein Folding Based on Hydrophobic-Polar Lattice Model and Chaotic clonal Genetic algorithm 1

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17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Wang, Shuihua Wu, Lenan Huo, Yuankai Wu, Xueyan Wang, Hainan Zhang, Yudong Nanjing Normal Univ Sch Comp Sci & Technol Nanjing 210023 Jiangsu Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

In order to improve the performance of prediction of protein folding problem, we introduce a relatively new chaotic clonal genetic algorithm (abbreviated as CCGA) to solve the 2D hydrophobic-polar lattice model. Our algorithm combines three successful components-(i) standard genetic algorithm (SGA), (ii) clonal selection algorithm (CSA), and (iii) chaotic operator. We compared this proposed CCGA with SGA, artificial immune system (AIS), and immune genetic algorithm (IGA) for various chain lengths. It demonstrated that CCGA had better performance than other methods over large-sized protein chains.

关键词： Protein folding Chaotic clonal genetic algorithm clonal selection algorithm Hydrophobic-polar model Artificial immune system

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A Chaotic clonal selection algorithm and its Application to Synthesize Multiple-Valued Logic Functions

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2010年第1期5卷 105-114页

作者： Gao, Shangce Cao, Qiping Zhang, Zhiqiang Tang, Zheng Toyama Univ Grad Sch Innovat Life Sci Toyama 9308555 Japan Tateyama Inst Syst Toyama 9300016 Japan

In this paper, a chaotic clonal selection algorithm (CCSA) is proposed to Synthesize multiple-valued logic (MVL) functions. The MVL function is realized ill a multiple-valued sum-of-products expression where product is indicated by MIN and sum by TSUM. The proposed CCSA, in which chaos is incorporated into the clonal selection algorithm to initialize antibodies and maintain the Population diversity, is utilized to learn a given target MVL truth table. Furthermore, an adaptive length strategy of antibodies is also introduced to reduce the computational complexity, whereas all improved affinity function enables the algorithm to find less product terms for all MVL function. Simulation results based oil a large number of MVL functions demonstrate the efficiency of the proposed method when compared with other traditional methodologies. (C) 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： multiple-valued logic clonal selection algorithm minimization chaos

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LAMARCKIAN LEARNING IN clonal selection algorithm FOR NUMERICAL OPTIMIZATION

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2010年第1期19卷 19-37页

作者： Gong, Maoguo Jiao, Licheng Yang, Jie Liu, Fang Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Inst Intelligent Informat Proc Xian 710071 Shaanxi Peoples R China

In this paper, we introduce Lamarckian learning theory into the clonal selection algorithm and propose a sort of Lamarckian clonal selection algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the clonal selection algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.

关键词： Lamarckian learning local search clonal selection algorithm artificial immune systems numerical optimization

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Dynamic Multiobjective clonal selection algorithm for Engineering Design

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IEEE TRANSACTIONS ON MAGNETICS 2010年第8期46卷 3033-3036页

作者： Batista, Lucas S. Oliveira, Diogo B. Guimaraes, Frederico G. Silva, Elson J. Ramirez, Jaime A. Univ Fed Minas Gerais Dept Engn Eletr BR-31270901 Belo Horizonte MG Brazil

We propose a Multiobjective clonal selection algorithm (MCSA) with dynamic variation of its main parameters for the solution of engineering design problems. The MCSA performs a cloning process using different probability distributions, in which the mutation strengths are guided based on a logarithmic rule and on information implicitly created by a simple differential evolution technique. This feature results in a self-adapting search in the algorithm. The efficiency of the MCSA is studied comparing its performance with the Nondominated Sorting Genetic algorithm II (NSGA-II) in analytical test problems and also in the design of a microwave heating device. The MCSA has outperformed the NSGA-II in all problems investigated.

关键词： Multiobjective optimization clonal selection algorithm self-adaptation

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Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and clonal selection Theory Inspired algorithms

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2016年第9期30卷 1660008-1660008页

作者： Lasisi, Ayodele Ghazali, Rozaida Deris, Mustafa Mat Herawan, Tutut Lasisi, Fola Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Obafemi Awolowo Univ Dept Agr Engn Ife Nigeria

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. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with clonalG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the clonalG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-clonalG, FRFS-AIRS, VQRS-clonalG, and VQRS-AIRS, are able to generate the 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： clonal selection algorithm artificial immune recognition system fuzzy-rough set vaguely quantified rough set

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Differential evolution combined with clonal selection for dynamic economic dispatch

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2015年第3期27卷 325-350页

作者： Niu, Q. Li, K. Irwin, G. W. Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Intelligent Syst & Control Belfast BT9 5AH Antrim North Ireland

Dynamic economic load dispatch (DELD) is one of the most important steps in power system operation. Various optimisation algorithms for solving the problem have been developed;however, due to the non-convex characteristics and large dimensionality of the problem, it is necessary to explore new methods to further improve the dispatch results and minimise the costs. This article proposes a hybrid differential evolution (DE) algorithm, namely clonal selection-based differential evolution (CSDE), to solve the problem. CSDE is an artificial intelligence technique that can be applied to complex optimisation problems which are for example nonlinear, large scale, non-convex and discontinuous. This hybrid algorithm combines the clonal selection algorithm (CSA) as the local search technique to update the best individual in the population, which enhances the diversity of the solutions and prevents premature convergence in DE. Furthermore, we investigate four mutation operations which are used in CSA as the hyper-mutation operations. Finally, an efficient solution repair method is designed for DELD to satisfy the complicated equality and inequality constraints of the power system to guarantee the feasibility of the solutions. Two benchmark power systems are used to evaluate the performance of the proposed method. The experimental results show that the proposed CSDE/best/1 approach significantly outperforms nine other variants of CSDE and DE, as well as most other published methods, in terms of the quality of the solution and the convergence characteristics.

关键词： clonal selection algorithm differential evolution dynamic economic load dispatch valve-point effects

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Solving Multi-objective Portfolio Optimization Problem for Saudi Arabia Stock Market Using Hybrid clonal selection and Particle Swarm Optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2015年第8期40卷 2407-2421页

作者： Bin Shalan, Sara A. Ykhlef, Mourad King Saud Univ Dept Nat & Engn Sci Coll Appl Studies & Community Serv Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

The portfolio is a group of assets held by an institution or a private individual. Each asset has an investment share of the total investment. The investor tries to distribute the investment to these different assets. The main issue in portfolio optimization is the allocation of different assets for maximum return and minimum risk within a suitable time. These two objectives lead to the multi-objective portfolio optimization problem, which must be solved. Several previous studies have addressed this issue. In this article, we propose a new intelligence hybrid evolutionary algorithm that combines clonal selection with particle swarm optimization to optimize the portfolio's return and risk. We then show the results of the proposed solution through experiments that are conducted using stocks in Kingdom of Saudi Arabia stock exchange market (Tadawul). Moreover, we compare our hybrid algorithm, clonal selection and particle swarm optimization-based solution.

关键词： clonal selection algorithm Hybrid evolutionary algorithm Portfolio optimization Particle swarm optimization algorithm

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Dynamic facility layout problem in footwear industry

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JOURNAL OF MANUFACTURING SYSTEMS 2015年 36卷 55-61页

作者： Ulutas, Berna Islier, A. Attila Eskisehir Osmangazi Univ Dept Ind Engn TR-26480 Eskisehir Turkey

Dynamic facility layout problem (DFLP) deals with the arrangement of machines in a site as to minimize the sum of materials handling and re-layout costs by considering multi periods. The DFLP studies in the literature provide several different algorithms and utilize the well known test problems to assess their performance. However, real life applications are overlooked. The industries such as footwear and clothing are prone to seasonal demand changes. Therefore, time horizons and layout/re-layout of the machines within the facility should be studied carefully. This study considers a footwear facility and several scenarios are generated by using the real life data. A clonal selection based algorithm is proposed to solve the real life DFLP. The performance of the algorithm, further the effect of time periods on solution quality and applicability of the results are tested and promising results are obtained. (C) 2015 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Artificial immune system clonal selection algorithm Dynamic facility layout problem Footwear industry Shoe manufacturing

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