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A fast algorithm for color image enhancement with total variation regularization

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Science china(Information Sciences) 2010年第9期53卷 1913-1916页

作者： LI Ming1,2 1School of Computer Science and Technology,Huazhong University of Science and Technology,wuhan 43.074,china 2key laboratory of education ministry for image processing and intelligent control,wuhan 43.074,china 1. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China2. Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan 430074 China

In this paper,a novel algorithm for color image enhancement is *** proposed method,which is based on Retinex theory and total variational framework,improves the original variational method by using a TV penalty term to force spatial smoothness on the reflectance *** split Bregman algorithm is employed to solve the proposed *** practical experiments,it is verified that our method obtains better enhancement performance and much better calculation efficiency.

关键词： color image enhancement Retinex theory total variational model split Bregman algorithm

Sufficient and necessary conditions for Lyapunov stability of Lorenz system and their application

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Science china(Information Sciences) 2010年第8期53卷 1574-1583页

作者： LUO Qi 1 , LIAO XiaoXin 2 & ZENG ZhiGang 2 1 College of Information and control, Nanjing University of Information Science and Technology, Nanjing 210044, china 2 Department of control Science and Engineering, Huazhong University of Science and Technology, image processing and intelligent control key laboratory of education ministry of china, wuhan 43.074, china 1. College of Information and control Nanjing University of Information Science and Technology Nanjing 210044 China2. Department of Control Science and Engineering Huazhong University of Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan 430074 China

The sufficient and necessary conditions for Lyapunov stability of the zero equilibrium point of Lorenz system are discussed, and some brief criteria are presented for globally exponential stability, globally asymptotical stability and instability. Furthermore, the behavior of the non-zero equilibrium point of Lorenz system is also investigated, and several sufficient and necessary conditions are provided for locally exponential stability and instability. The established theorems in this paper develop and extend the existing achievements on Lyapunov stability of Lorenz system. In conclusion, by applying the obtained results, some less conservative feedback-control laws are derived to guarantee the globally exponential stability of the chaos control of Chen system, Lu¨ system, Yang-Chen system and Yu-Xia Li system.

关键词： Lorenz system Lyapunov stability sufficient and necessary condition bifurcation values globally exponential synchronization

Immunodominance and clonal selection inspired multiobjective clustering

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Progress in Natural Science:Materials International 2009年第6期19卷 751-758页

作者： Wenping Ma , Licheng Jiao, Maoguo Gong key laboratory of intelligent Perception and image Understanding of the ministry of education of china, Institute of intelligent Information processing, Xidian University, Xi’an 710071, china [a]key laboratory of intelligent Perception and image Understanding of the ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The biological immune system is a highly parallel and distributed adaptive system. The information processing abilities of the immune system provide important insights into the field of computation. Based on immunodominance in the biological immune system and the clonal selection mechanism, a novel data mining method, Immune Dominance Clonal Multiobjective Clustering algorithm (IDCMC), is presented. The algorithm divides an individual population into three sub-populations according to three different measurements, and adopts different evolution and selection strategies for each sub-population. The update of each sub-population, however, is not carried out in isolation. The periodic combination operation of the analysis of the three sub-populations represents considerable advantages in its global search ability. The clustering task is a multiobjective optimization problem, which is more robust with respect to the variety of cluster structures of different datasets than a single-objective clustering algorithm. In addition, the new algorithm can determine the number of clusters automatically, which should identify the most promising clustering solutions in the candidate set. The experimental results, using artificial datasets with different manifold structure and handwritten digit datasets, show that the IDCMC outperforms the PESA-Ⅱ-based clustering method, the genetic algorithm-based clustering technique and the original K-Means algorithm in solving most of the problems tested.

关键词： Artificial immune systems Multiobjective optimization Clustering Unsupervised learning

A global heuristically search algorithm for DNA encoding

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Progress in Natural Science:Materials International 2007年第6期17卷 745-749页

作者： Zhang Kai, Pan Linqiang and Xu Jin (The key laboratory of image processing and intelligent control, Department of control Science and Engineering, Huazhong University of Science and Technology, wuhan 43.074, china) [a]The key laboratory of image processing and intelligent control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China

A new efficient algorithm is developed to design DNA words with equal length for DNA computing. The algorithm uses a global heuristic optimizing search approach and converts constraints to a carry number to accelerate the convergence, which can generate a DNA words set satisfying some thermodynamic and combinatorial constraints. Based on the algorithm, a software for DNA words design is developed.

关键词： DNA computing encoding heuristic optimizing search

Multi-objective carrier chaotic evolutionary algorithm for DNA sequences design

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Progress in Natural Science:Materials International 2007年第12期17卷 1515-1520页

作者： Xiao Jianhua, Xu Jin, Geng Xiutang and Pan Linqiang(key laboratory of image processing and intelligent control, Department of control Science and Engineering, Huazhong University of Science and Technology, wuhan 43.074, china) key laboratory of image processing and intelligent control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

DNA computing is a new vista of computation, which is of biochemical type. Since each piece of information is encoded in biological sequences, their design is crucial for successful DNA computation. DNA sequence design is involved with a number of design criteria, which is difficult to be solved by the traditional optimization methods. In this paper, the multi-objective carrier chaotic evolution algorithm (MCCEA) is introduced to solve the DNA sequence design problem. By merging the chaotic search base on power function carrier, a set of good DNA sequences are generated. Furthermore, the simulation results show the efficiency of our method.

关键词： DNA computing carrier chaotic search multi-objective evolutionary algorithm DNA sequences design.

A novel quantum-inspired immune clonal algorithm with the evolutionary game approach

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Progress in Natural Science:Materials International 2009年第10期19卷 1341-1347页

作者： Qiuyi Wu,Licheng Jiao,Yangyang Li,Xiaozheng Deng key laboratory of intelligent Perception and image Understanding of ministry of education of china,Institute of intelligent Information processing,Xidian University,Xi’an 710071,china [a]key laboratory of intelligent Perception and image Understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

The quantum-inspired immune clonal algorithm(QICA) is a rising intelligence *** on evolutionary game theory and QICA,a quantum-inspired immune algorithm embedded with evolutionary game(EGQICA) is proposed to solve combination optimization *** this paper,we map the quantum antibody’s finding the optimal solution to player’s pursuing maximum utility by choosing strategies in evolutionary *** dynamics is used to model the behavior of the quantum antibody and the memory mechanism is also introduced in this *** results indicate that the proposed approach maintains a good diversity and achieves superior performance.

关键词： Game theory Evolutionary game Replicator dynamics Quantum-inspired optimization Artificial immune system

intelligent multi-user detection using an artificial immune system

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Science in china(Series F) 2009年第12期52卷 2342-2353页

作者： GONG MaoGuo, JIAO LiCheng, MA WenPing & MA JingJing key laboratory of intelligent Perception and image Understanding of ministry of education of china Institute of intelligent Information processing, Xidian University, Xi’an 710071, china 1. key laboratory of intelligent Perception and image Understanding of ministry of education of china Xidian University Xi’an 710071 China 2. Institute of intelligent Information processing Xidian University Xi’an 710071 China

Artificial immune systems （AIS） are a kind of new computational intelligence methods which draw inspiration from the human immune system. In this study, we introduce an AIS-based optimization algorithm, called clonal selection algorithm, to solve the multi-user detection problem in code-division multipleaccess communications system based on the maximum-likelihood decision rule. Through proportional cloning, hypermutation, clonal selection and clonal death, the new method performs a greedy search which reproduces individuals and selects their improved maturated progenies after the affinity maturation process. Theoretical analysis indicates that the clonal selection algorithm is suitable for solving the multi-user detection problem. Computer simulations show that the proposed approach outperforms some other approaches including two genetic algorithm-based detectors and the matched filters detector, and has the ability to find the most likely combinations.

关键词： artificial immune systems clonal selection multi-user detection code-division multiple-access genetic algorithm

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Memetic computation based on regulation between neural and immune systems: the framework and a case study

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Science china(Information Sciences) 2010年第8期53卷 1519-1527页

作者： GONG MaoGuo , JIAO LiCheng, LIU Fang & YANG Jie key Lab of intelligent Perception and image Understanding of ministry of education of china, Institute of intelligent Information processing, Xidian University, Xi’an 710071, china 1. key Lab of intelligent Perception and image Understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China

Lamarckian learning has been introduced into evolutionary computation to enhance the ability of local search. The relevant research topic, memetic computation, has received significant amount of interest. In this study, a novel memetic computational framework is proposed by simulating the integrated regulation between neural and immune systems. The Lamarckian learning strategy of simulating the unidirectional regulation of neural system on immune system is designed. Consequently, an immune memetic algorithm based on the Lamarckian learning is proposed for numerical optimization. The proposed algorithm combines the advantages of immune algorithms and mathematical programming, and performs well in both global and local search. The simulation results based on ten low-dimensional and ten high-dimensional benchmark problems show that the immune memetic algorithm outperforms the basic genetic algorithm-based memetic algorithm in solving most of the test problems.

关键词： memetic computation artificial immune system Lamarckian learning genetic algorithm numerical optimization

Immune secondary response and clonal selection inspired optimizers

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Progress in Natural Science:Materials International 2009年第2期19卷 237-253页

作者： Maoguo Gong a,Licheng Jiao a,Lining Zhang a,Haifeng Du b a key laboratory of intelligent Perception and image Understanding of ministry of education of china,Institute of intelligent Information processing,Xidian University,Xi’an 710071,china b School of Public Policy and Administration,Xi’an Jiaotong University,Xi’an 710049,china [a]key laboratory of intelligent Perception and image Understanding of ministry of education of china Institute of Intelligent Information Processing Xidian University Xi’an 710071 China [b]School of Public Policy and Administration Xi’an Jiaotong University Xi’an 710049 China

The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen,immune system usually calls for a more rapid and larger response to the antigen,where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response,two novel artificial immune system algorithms,secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA),are presented for solving single and multi-objective optimization problems,respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory,CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SRO copies certain antibodies to a secondary pool,whose members do not participate in CSO,but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand,decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms,such as OGA/Q,IEA,IMCPA,BGA and AEA,SRCPA is shown to be able to solve complex optimization problems,such as high-dimensional function optimizations,with better performance. On the other hand,SRCMOA combines the Pareto-strength based fitness assignment strategy,CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA,NSGA-Ⅱ,SPEA,and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto

关键词： Clonal selection Secondary response Artificial immune systems Evolutionary algorithms