Chao Yang, Bing-Qiu Chen, Lin Jia et al. · Complexity · 2020
The antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection. A large number of antibodies with high affinity make it difficult to participate in clonal selection and exist in antibody concentration for a long time. This part of inactive antibody forms a “black hole” of the antibody set, which is difficult to remove and update in a timely manner, thus affecting the speed at which the algorithm approximates the optimal solution. Inspired by the mechanism of biological forgetting, an improved clonal selection algorithm is proposed to solve this problem. It aims to use the abstract mechanism of biological forgetting to eliminate antibodies that cannot actively participate in high-frequency mutations in the antibody candidate set and to improve the problem of insufficient diversity of antibodies in the clonal selection algorithm, which is prone to fall into the local optimal. Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability.
Cited by 5Open access
Lisha Zhang, Zhengxing Sun · 2015
Zhang LS, Sun ZX. An immune clonal selection algorithm for online symbol recognition. Journal of Software,
Cited by 0
Siti †̃Aisyah Saâ€TMdan, Rajeswari Raju, Nursuriati Jamil et al. · International Journal of Engineering & Technology · 2018
Fingerprint verification has drawn a lot of attention to its approach in biometric since it is one of the most important biometric technologies nowadays and is widely used in several different applications and areas. It is applied in the forensic science area in order to identify people who were involved in criminal scenes such as the victims and the suspects. A human’s fingerprint is unique and usually has its own patterns and ridges, which differs them from other’s fingerprints. However, there are some drawbacks that can cause low accuracy and low performance of the verification. This occurs when the fingerprint images used are of low-quality and the fingerprints may be slightly incomplete (partial). Clonal Selection Algorithm (CSA) is known to be good in pattern matching and optimization of problems. Hence, this paper discusses the finding of the implementation of CSA in fingerprint verification. There were two main processes involved, which are features extraction using minutiae-based method and also the implementation of the CSA algorithm. Study shows that the FNMR result is 33.33% and the FMR is 16.67%. Further studies can be carried out by using the same algorithm, but focusing more on the feature extraction methods to improve the extraction of fingerprints. Â
Cited by 0Open access
Hongyuan Gao, Jinlong Cao, Xuemei Yu · 2010
In this paper, a novel multi-user detection based on a DNA clonal selection algorithm (DNACSA) is proposed for code-division multiple-access communications system. To design an efficient DNACSA based detector, the stochastic Hop field neural network is embedded into the DNACSA as an “immune operator” to improve further the affinity of the DNA antibodies at each generation. Such a hybridization of the DNACSA with the stochastic Hop field neural network reduces its computational complexity by providing faster convergence. In addition, the embedded Hop field neural network improves the performance of the DNACSA. Simulation results are provided to show that the proposed DNACSA detector has significant performance improvements over the conventional detectors and some detectors based on the previous intelligence algorithms in bit-error-rate and multiple access interference resistance.
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Xianjin Fang, Xue-hai MU, Ling-bing LIU et al. · Journal of Computer Applications · 2010
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Serkan Öztürk, Şaban ÖZER, Veysel Aslantaş · Kayseri University - Avesis · 2007
In this paper, a novel fragile watermarking method based on clonal selection algorithm (CSA), CLONALG, is presented. In Discrete Cosine Transform (DCT) based fragile watermarking techniques, there occurs some degree of rounding errors because of the conversion of real numbers into integers in the process of transformation of image from frequency domain to spatial domain. In this paper, the rounding errors caused by this transformation process are corrected by using CLONALG. Simulation results show that extracted watermark is obtained exactly the same as embedded watermark and optimum watermarked image transparency is achieved. In addition, the performance comparison of CLONALG and genetic algorithm (GA) based methods is realized.
Cited by 0
Shamsudin, Farah Syadiyah · UiTM Institutional Repositories (Universiti Teknologi MARA) · 2017
Fingerprint verification has drawn a lot of attention on its approach in biometric since it is one of the most important biometric technologies nowadays and it is widely used in several different applications and areas. It is applied in the forensic science area in order to identify the people that are involved in the criminal scenes such as the victims and the suspects. A human’s fingerprint is unique and usually has its own patterns and ridges, which differs them from others’ fingerprints. However, there are some drawbacks that can cause low accuracy and low performance of the verification when the fingerprint images used are of low-quality causing some of the important details to be missing or hard to trace. Therefore, the aim for this project is to develop a new approach in the fingerprint verification system by applying Clonal Selection Algorithm (CSA) that is known to be good in pattern matching and optimization of problems. There will be two processes involved, which are feature extraction using minutiae-based method and also the implementation of the proposed algorithm, CSA. The results of False Matching Ratio (FMR) was 16.67% whilst the False Non-Matching Ratio (FNMR) was 33.33%. However, different number of generations applied in CSA will give different result of the verification process. Further studies can be made by using the same algorithm, but focusing more on the image enhancement and the feature extraction methods to improve the quality of the extraction of fingerprints.
Cited by 0
Min Keng Tan, Grace Butiza Joponi, Kit Guan Lim et al. · 2023
The rising demand for renewable energy sources has fueled extensive research in photovoltaic (PV) systems. However, conventional Maximum Power Point Tracking (MPPT) algorithms often encounter challenges when tracking the global maximum power point under non-uniform irradiance conditions. To address this issue, the Clonal Selection Algorithm (CSA) is proposed as an effective approach to enhance MPPT algorithm performance. The CSA dynamically adjusts the voltage perturbation size based on instant ambient irradiance and temperature, leading to improved global maximum power point tracking and enhanced efficiency in PV systems. Experimental results demonstrate the superiority of the proposed CSA over conventional MPPT algorithms, especially in scenarios with varying solar irradiance. The CSA's adaptability allows PV systems to operate closer to their optimal efficiency, maximizing energy harvest from available solar resources. Overall, this research contributes valuable insights into sustainable and efficient energy solutions by leveraging the capabilities of the CSA. Successfully integrating the CSA in PV systems plays a critical role in establishing an eco-friendly and resilient renewable energy infrastructure, for a greener future.
Cited by 0
Regan Murugesan, K. Sivasakthi Balan, V. Navaneetha Kumar · 2010
An improved Clonal selection algorithm based Artificial Immune System (AIS) is given for solving Job Shop Scheduling Problem (JSSP). To reduce the optimal solution search space, a bio inspired positive selection principle is proposed to populate the initial antigen library. This proposed algorithm gives the better start up and faster converging towards the optimal solution for the given JSSP. The given algorithm has been simulated in Matlab tool and the results were compared with the Genetic Algorithm (GA) generated results for a well known bench marking problem. AIS produced optimal solution for more than 90% run which 15% produced by GA.
Cited by 5
Mofei Song, Zhengxing Sun · Mathematical Problems in Engineering · 2014
The collection of signature data for system development and evaluation generally requires significant time and effort. To overcome this problem, this paper proposes a detector generation based clonal selection algorithm for synthetic signature set generation. The goal of synthetic signature generation is to improve the performance of signature verification by providing more training samples. Our method uses the clonal selection algorithm to maintain the diversity of the overall set and avoid sparse feature distribution. The algorithm firstly generates detectors with a segmented r ‐continuous bits matching rule and P ‐receptor editing strategy to provide a more wider search space. Then the clonal selection algorithm is used to expand and optimize the overall signature set. We demonstrate the effectiveness of our clonal selection algorithm, and the experiments show that adding the synthetic training samples can improve the performance of signature verification.
Cited by 10Open access
Du Hai · 2005
Based on immunological antibody clonal selection theory, the general steps of Immune Clonal Selection Algorithm (ICSA) are presented in this paper. We put forward the network framework of ICSA, and the dynamic characteristics of ICSA based on the Lyapunov theory are analyzed. Then this paper introuduces a novel Artificial Immune System Algorithm, Pseudo--Grads Hybrid Immune Clonal Selection Network (GHICSN). The simulation results of some functions optimization indicate that GHlCSN improves the performance of ICSA to some extend.
Cited by 4
Hong Lü · 2014
In order to enhance the global optimal search ability of clone selection algorithm in solving complicated multi-modal function optimization problem,a new clone selection algorithm( SCSA) based on the mechanism ofStretchingtechnique is proposed in the paper. The algorithm equipped w ith this technique can narrow the range of extreme value of the objective function,and improve the optimization efficiency of the algorithm. To improve diversity of the population and global convergence performance,the chaos mutation mechanism and selection mechanism based on the density and fitness vector of the antibody are introduced in the algorithm. Simulation results indicate that the SCSA equipped w ith theStretchingtechnique can inhibit prematurity and has better convergence speed and precision compared w ith traditional artificial immune algorithms,and is an effective multi-modal function optimization algorithm.
Cited by 0
Utpal Garain, M.P. Chakraborty, Dipankar Dasgupta · Lecture notes in computer science · 2006
Cited by 29
Zheng Yuan Li, Gang Zhang, Chao Li et al. · Advanced materials research · 2014
Based on the full understanding of the current status of the reactive power optimization study, we propose an improved type of immune algorithm to solve the reactive power optimization problem by introducing the immune clonal selection algorithm (ICSA) genetic manipulation, affinity of mature, cloning and memory mechanism, and use the appropriate operator to ensure that the algorithm can quickly converge to the global optimal solution to improve the efficiency of the algorithm solving and solution accuracy, avoiding the "curse of dimensionality" and precocious problems. ICSA algorithm is proposed to improve the convergence speed simultaneously. Better maintain the diversity of the population. Effectively overcome the premature convergence of evolutionary computation itself is difficult to solve the problem. Four different examples of calculation results show that this method has superior computational efficiency and convergence capability, high quality and are solved, very suitable for solving large-scale power system reactive power optimization problem, with a strong practical value.
Cited by 0
Xiaoying Pan, Fang Liu, Licheng Jiao · Lecture notes in computer science · 2006
Cited by 5
Qiuyi Wu, Licheng Jiao, Yangyang Li · 2009
Based on the clonal selection theory of the immune system, a novel optimal algorithm, called a directional immune clonal selection algorithm (DICSA), is proposed to design RF diplexer. In DICSA, antibody is proliferated and divided into a set of subpopulation groups. In the antibody's updating, the clonal mutation is applied to avoid premature convergences. The clonal selection realizes the information communication between the subpopulation groups so as to improve the search efficiency. DICSA is applied to a practical problem, designing RF diplexer. The results show that DICSA has stable performance during the optimization process with a satisfactory result.
Cited by 1
Limin Wang, Xuming Han, Ming Li · Advanced Science Letters · 2012
Cited by 1
Heder S. Bernardino, Hélio J. C. Barbosa, Leonardo Goliatt · Lecture notes in computer science · 2010
Cited by 9
İlhan İlhan, Yunus Emre Göktepe, Cengiz Özcan et al. · ASME Press eBooks · 2011
Investigations on genetic variants associated with complex diseases are important for enhancements in diagnosis and treatments. SNPs (Single Nucleotide Polymorphisms), which comprise most of the millions of changes in human genome, are promising tools for disease-gene association studies. On the other hand, these studies are limited by cost of genotyping tremendous number of SNPs. Therefore, it is essential to identify a subset of SNPs that represents rest of the SNPs. As subset of SNPs is identified, data set should be searched as well as possible. In this study, a new method called CLONTagger was introduced, where Support Vector Machine (SVM) was used as SNP prediction method, whereas Clonal Selection Algorithm (CLONALG) was used as tag SNP selection method. The suggested method was compared with current tag SNP selection algorithms in literature using different datasets. Experimental results demonstrated that the suggested method could identify tag SNPs with better prediction accuracy than other methods from literature.
Cited by 4
Chao Chen, Yuting Wan, Ailong Ma et al. · IEEE Transactions on Geoscience and Remote Sensing · 2022
Feature selection is an effective way to handle the strong correlation of hyperspectral image data by screening the significant features, and is generally accepted to be a multiobjective optimization problem. Nevertheless, due to the randomness of the strategies and the ambiguity of the optimization directions, the existing multiobjective evolutionary optimization based feature selection methods can suffer from inefficient search and loss of search space with promising solutions when faced with the high-dimensional and multi-peak search space. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) employs a decomposition framework to provide exact guidance for the optimization directions. Unfortunately, random operators are still used, leading to inadequate local optimization. Thus, evolutionary strategies with search preference such as clonal selection may be necessary for local search. In this paper, a novel decomposition-based multiobjective clonal selection algorithm for feature selection (MOCSA/D_FS) is proposed to obtain a feature subset with a superior classification performance. In MOCSA/D_FS, the information entropy and the ratio of the relative scatter value and mutual information are utilized as two objective functions to evaluate the information amount and redundancy. A series of subproblems are then obtained by decomposing the multiobjective problem through weight vectors, with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm constraint used to balance the search space. Subsequently, a clonal selection method with search space preference performs a detailed local search on each subproblem, which can fully exploit the potential optimal space. The effectiveness and generalizability of the proposed method was confirmed by experiments on four hyperspectral remote sensing image datasets.
Cited by 20