X. Wang · 2005
Inspired by natural immune mechanisms, artificial immune optimization (AIO) methods have been successfully applied to deal with numerous challenging optimization problems with superior performances over classical optimization techniques. Clonal selection algorithm (CSA) is one of the most widely employed immune-based approaches for handling those optimization tasks. In this paper, the proposed CSA is used to search for the optimal parameters (values of inductor and capacitor) of a passive filter in the diode full-bridge rectifier. Simulation results demonstrate that the CSA-based approach can acquire the optimal LC parameters with certain given criteria for power filter design.
Cited by 27
Hongwei Dai, Yang Yu, Cunhua Li · 2010
The chaotic initialization and chaotic search are introduced into Clonal Selection Algorithm (CSA) to overcome random antibody initialization and premature convergence problems in traditional CSA. Taking full advantages of the ergodic and stochastic properties of chaotic variables, antibodies with different affinity perform chaotic search to exploit local solution space. Experimental results on test functions demonstrate that the chaotic CSA outperforms the classical clonal selection algorithm.
Cited by 1
Nur Syazwani Noor Rodi, Amelia Ritahani Ismail, Marlinda Abdul Malik · 2012
Daily rainfall prediction is important in water resources management in order to estimate
long term forecasting for future usage. This paper utilizes Artificial Immune System (AIS)
algorithm for daily rainfall prediction where rainfall is used as input nodes. AIS have the
abilities of self-organizing, memory, recognition, adaptive and learning, inspire from the
immune system. Immune system is a system in our body which consists of a complex set of
cells and molecules that protect our bodies against infection. There are three mains algorithm
in AIS which are Clonal Selection Algorithm (CSA), Immune Network Algorithm and
Negative Selection Algorithm. This study focuses on development of daily rainfall prediction
model using CSA adapted from clonal selection theory in human being and animal immune
system. The common use of Clonal Selection Algorithm is to randomly generate the
antibodies in the population. From the experiment, the results obtained more than 90%
accuracy by using 100, 500 and 1000 numbers of detector which is promising that CSA can
be a good predictor algorithm.
Cited by 0
Rafael Freesz Resende Corrêa, Stênio Sã Rosário Furtado Soares, Lorenza Leão Oliveira Moreno et al. · Anais do Simpósio Brasileiro de Pesquisa Operacional · 2024
Cited by 1
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2007
The two-signal theory of lymphocyte activation was proposed more than thirty-five years ago and since that time has been elaborated and experimentally demonstrated to be a mediation process for the activation of both B and T lymphocytes. Modern names for the theory include associative recognition theory, the ARA model, and co-stimulation for Tcells. This work proposes the integration of T-cell mediation of activated B-lymphocytes into the elaborated clonal selection algorithm (ECSA). The resultant system provides a decoupled condition-action cognitive system that appears to have much similarity with classifier systems and may be an example of the reinforcement-learning paradigm.
Cited by 3Open access
Ronghua Shang, Licheng Jiao, Maoguo Gong et al. · Lecture notes in computer science · 2005
Cited by 39
Yu Liu, Hongxia Wang · 2023
With the rapid development of steganography, researches on the adaptive steganography have made steganalysis more and more difficult. In order to improve the detection accuracy, there are plenty of works introducing complex high-dimensional features to steganalysis, among which the rich model steganalysis features are particularly classical. However, there are many redundant features in high-dimensional features, which greatly increase the time and computational cost. How to eliminate redundant features while maintaining detection accuracy is still a research hotspot. In this paper, we propose a novel feature selection method based on Clonal Selection Algorithm (FS-CSA) for image steganalysis, which separates features into multiple populations according to the idea of niche technique, and proposes a feature similarity function to calculate the affinity of features in each population, and finally uses FS-CSA to select features based on the affinity in each population in turn. The experimental results show that FS-CSA can significantly reduce the feature dimensionality while maintaining the detection accuracy, where the Spatial Rich Model (SRM) features can be reduced to about 8000 dimensions, and the Gabor Filter Residual (GFR) features can be reduced to about 7000 dimensions. The crossover experiments show that the steganalysis features selected by FS-CSA are also generalizable to any steganography algorithm.
Cited by 0
Wan Li Kang, Jing Wu, Du Cui et al. · Applied Mechanics and Materials · 2012
In this paper, we present an oriented clonal selection algorithm (O-CLONALG) for mining association rules effectively for classification. Different with the traditional evolutionary algorithms, O-CLONALG firstly scans dataset one time to find the frequent rules with one item. The items are used to generate new rules and the mutation operation is limited in it. When mutation operation takes place, each rule in the same generation was added a new item. The results have shown that it is efficient in dealing with the problem on the complexity of the rule search space. At the same time, good classification accuracy has been achieved
Cited by 0
Antariksha Bhaduri · 2009
The shuffled frog leaping algorithm (SFLA) is a recent meta-heuristic memetic algorithm used for optimization having a simple algorithm with a fast calculation time. It mimics the social behavior of a species (frogs) found in nature. Clonal selection algorithm (CSA) is an optimization algorithm developed based on the processes occurring in natural immune system. In this paper, a novel algorithm is proposed that is based on a modified CSA and SFLA. In the proposed algorithm a modified CSA is used for the best candidates in the population to progress and SFLA for the worst candidates in the population to move towards the best candidates. The power of the algorithm lies in the fact that it avoids stagnation and has a very fast convergence speed. The algorithm is tested against SFLA for five functions in which it greatly outperforms SFLA in terms of convergence rate and the optimum result obtained.
Cited by 13
Hasan Zorlu, Şaban Özer · 2009
In this work, clonal selection algorithm (CSA) has been applied to adaptive identification of nonlinear systems and compared its performance to that of genetic algorithm (GA). Nonlinear Box-Jenkins system which is frequently used as a benchmark example for testing in literature and a parametric nonlinear bilinear system have been identified using these algorithms. The simulation results have shown that nonlinear systems can be identified using CSA with low modeling error.
Cited by 1
Nadezhda Astakhova, Liliya Demidova, А. В. Кузовников et al. · IOP Conference Series Materials Science and Engineering · 2017
The multiobjective modified clonal selection algorithm based on the use of the notion "Pareto dominance", which can be applied for the development of the forecasting models on the base of the strictly binary trees has been offered. It is suggested to use the affinity indicator based on the average forecasting error rate, and the tendencies discrepancy indicator in the role of the objective functions of this algorithm. The multiobjective modified clonal selection algorithm can be applied for solving problems of individual and groups' forecasting. The experimental results which confirm the efficiency of the offered algorithm in comparison with the basic modified clonal selection algorithm have been given.
Cited by 0Open access
Yong Zhao · 2010
In this paper,an immune clonal clustering selection algorithm for solving TSP problems was proposed.Firstly,Utilizing the k_means clustering method to Divided TSPinto Some partition dynamically,afterwards,The Immune clonal selection algorithm is going onExact optimization for the TSP within subgroup.According to neighborhood K Focus connection to get connected to the global problem of large-scale TSP and get satisfactory solution.Experimental results show that the proposed algorithm is effective and feasible.
Cited by 0
Jason Brownlee · Swinburne Research Bank (Swinburne University of Technology) · 2007
Immune systems do not exist in isolation, rather the cellular and molecular interactions occur within the tissues of a host, and populations of hosts exist together in an environment. This work investigates the relationship between the acquired immune system and a pathogenic environment at the ‘hostpopulation’ scale. A minimal population-based clonal selection algorithm is proposed and extend to include the host-population immunological principles of inter-population pathogen dynamics, vaccination/inoculation, and transplanted acquired immune cells.
Cited by 3Open access
Jihee Kim, P.J. Bentley · UCL Discovery (University College London) · 2002
Cited by 27
Mingyuan Zhao, Ke Tang, Gang Lü et al. · 2008
In order to improve some fundamental problems of the clonal selection algorithm (CSA), a novel clonal selection algorithm (NCSA) is proposed. After analyzing the mechanism of the clonal selection and proposing the antibody model, the basic character of the application problem fused into the NCSA based on rearrangements of antibody molecule coding genes. Next, we analyzed synthetically the antibody-antigen affinity and the antibody-antibody affinity. Through contrast experiments between the NCSA and the CSA to solve travelling salesman problem (TSP), it is shown that the NCSA has better performances.
Cited by 7
Wenping Ma, Ronghua Shang, Licheng Jiao · Lecture notes in computer science · 2006
Cited by 4
M.Y. El-Sharkh · International Journal of Electrical Power & Energy Systems · 2013
Cited by 34
Tuba Karagül Yıldız, Hüseyin Demirci, Nilüfer Yurtay · Lecture notes on data engineering and communications technologies · 2020
Cited by 0
Ying Yu, Hou Chao-zhen · 2005
This paper proposes a new clonal selection algorithm towards the use of an artificial immune system (AIS) to build a computer virus immune system. Inspired by the learning mechanism during the clonal selection process in biology, a learning operator was introduced into the original clonal selection algorithm. This learning operator can be used to not only incarnate the effect of the learning but also to improve the detection efficiency. Preliminary simulation experiments show this algorithm is practical and simple to use.
Cited by 14
2013
Abstract. A dynamic system identification algorithm is developed using the basic mechanisms of clonal selection and an idea of a new evolutionary computing paradigm – gene expression programming. On the basis of the algorithm developed a computer based system is proposed for making decisions relevant to forecasting of a single variable and multivariate time series. The results of computing experiments achieved with the system developed show high quality of short and medium period forecasts.
Cited by 0