Immune-Inspired Quantum Genetic Optimization Algorithm and its Application

2010 ◽  
Vol 143-144 ◽  
pp. 547-551
Author(s):  
Zhe Lian
Keyword(s):  
Optimization Algorithm ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Computation Method ◽  
Clonal Selection Algorithm ◽  
Genetic Optimization ◽  
Selection Algorithm ◽  
Human Immune System ◽  
Immune Systems ◽  
Genetic Optimization Algorithm

Artificial immune systems (AIS), inspired by the natural immune systems, are an emerging kind of soft computing methods. This paper brings forward an immune-inspired quantum genetic optimization algorithm (IQGOA) based on clonal selection algorithm. The IQGOA is an evolutionary computation method inspired by the immune clonal principle of human immune system. To show the versatility and flexibility of the proposed IQGOA, some examples are given. Experimental results have shown that IQGOA is superior to clonal selection algorithm and Genetic Algorithm (GA) on performance.

scholarly journals Design of Fixed and Ladder Mutation Factor-Based Clonal Selection Algorithm for Solving Unimodal and Multimodal Functions

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
Suresh Chittineni ◽  
A. N. S. Pradeep ◽  
Dinesh Godavarthi ◽  
Suresh Chandra Satapathy ◽  
S. Mohan Krishna ◽  
...  
Keyword(s):  
Clonal Selection ◽  
Constant Factor ◽  
Clonal Selection Algorithm ◽  
Solution Stability ◽  
Selection Algorithm ◽  
Human Immune System ◽  
Solution Convergence ◽  
New Concepts ◽  
Clonal Selection Algorithms ◽  
Selection Algorithms

Clonal selection algorithms (CSAs) is a special class of immune algorithms (IA), inspired by the clonal selection principle of the human immune system. To improve the algorithm's ability to perform better, this CSA has been modified by implementing two new concepts called fixed mutation factor and ladder mutation factor. Fixed mutation factor maintains a constant factor throughout the process, where as ladder mutation factor changes adaptively based on the affinity of antibodies. This paper compared the conventional CLONALG, with the two proposed approaches and tested on several standard benchmark functions. Experimental results empirically show that the proposed methods ladder mutation-based clonal selection algorithm (LMCSA) and fixed mutation clonal selection algorithm (FMCSA) significantly outperform the existing CLONALG method in terms of quality of the solution, convergence speed, and solution stability.

scholarly journals Multithreshold Segmentation Based on Artificial Immune Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Erik Cuevas ◽  
Valentin Osuna-Enciso ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Humberto Sossa
Keyword(s):  
Initial Conditions ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Gaussian Function ◽  
Gaussian Mixture ◽  
Artificial Immune ◽  
Human Immune System ◽  
Immune Systems ◽  
Gradient Based ◽  
Low Sensitivity

Bio-inspired computing has lately demonstrated its usefulness with remarkable contributions to shape detection, optimization, and classification in pattern recognition. Similarly, multithreshold selection has become a critical step for image analysis and computer vision sparking considerable efforts to design an optimal multi-threshold estimator. This paper presents an algorithm for multi-threshold segmentation which is based on the artificial immune systems(AIS) technique, also known as theclonal selection algorithm (CSA). It follows the clonal selection principle (CSP) from the human immune system which basically generates a response according to the relationship between antigens (Ag), that is, patterns to be recognized and antibodies (Ab), that is, possible solutions. In our approach, the 1D histogram of one image is approximated through a Gaussian mixture model whose parameters are calculated through CSA. Each Gaussian function represents a pixel class and therefore a thresholding point. Unlike the expectation-maximization (EM) algorithm, the CSA-based method shows a fast convergence and a low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental evidence demonstrates a successful automatic multi-threshold selection based on CSA, comparing its performance to the aforementioned well-known algorithms.

Cost optimization of water distribution networks by using artificial immune systems

2014 ◽  
Vol 64 (1) ◽  
pp. 47-63 ◽  
Author(s):  
Miraç Eryiğit
Keyword(s):  
Water Distribution ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Immune Systems ◽  
Proposed Model ◽  
The Impact

This study aims at the development of an optimization model based on artificial immune systems (AIS) to minimize cost designs of water distribution networks (WDNs). Clonal selection algorithm (Clonalg), a class of AIS, was used as an optimization technique in the model, and its mutation operation was modified to increase the diversity (search capability). EPANET, a widely known WDN simulator, was used in conjunction with the proposed model. The model was applied to four WDNs of Two-loop, Hanoi, Go Yang, New York City, and the results obtained were compared with other heuristic and mathematical optimization models in the related literature, such as harmony search, genetic algorithm, immune algorithm, shuffled complex evolution, differential evolution, and non-linear programming-Lagrangian algorithm. Furthermore, the modified Clonalg was compared with the classic Clonalg in order to demonstrate the impact of the modification on the diversity. The proposed model appeared to be promising in terms of cost designs of WDNs.

A clonal selection algorithm model for daily rainfall data prediction

2014 ◽  
Vol 70 (10) ◽  
pp. 1641-1647
Author(s):  
N. S. Noor Rodi ◽  
M. A. Malek ◽  
Amelia Ritahani Ismail ◽  
Sie Chun Ting ◽  
Chao-Wei Tang
Keyword(s):  
Clonal Selection ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Series Data ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Immune Systems ◽  
Daily Rainfall Data ◽  
Data Prediction ◽  
Testing Stage

This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.

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