Artificial Clonal Selection Model and Its Application

2010 ◽  
pp. 100-122
Author(s):  
Shangce Gao ◽  
Zheng Tang ◽  
Hiroki Tamura
Keyword(s):  
Immune System ◽  
Computational Intelligence ◽  
Clonal Selection ◽  
Dynamical Behavior ◽  
Computational Technique ◽  
Clonal Selection Algorithm ◽  
Traveling Salesman Problems ◽  
Selection Principle ◽  
Natural Processes ◽  
Selection Principles

Artificial Immune System as a new branch in computational intelligence is the distributed computational technique inspired by immunological principles. In particular, the Clonal Selection Algorithm (CS), which tries to imitate the mechanisms in the clonal selection principle proposed by Burent to better understand its natural processes and simulate its dynamical behavior in the presence of antigens, has received a rapid increasing interest. However, the description about the mechanisms in the algorithm is rarely seen in the literature and the related operators in the algorithm are still inefficient. In addition, the comparison with other algorithms (especially the genetic algorithms) lacks of analysis. In this chapter, several new clonal selection principles and operators are introduced, aiming not only at a better understanding of the immune system, but also at solving engineering problems more efficiently. The efficiency of the proposed algorithm is verified by applying it to the famous traveling salesman problems (TSP).

scholarly journals An Immuno-Genetic Hybrid Algorithm

2009 ◽  
Vol 4 (4) ◽  
pp. 374 ◽  
Author(s):  
Emad Nabil ◽  
Amr Badr ◽  
Ibrahim Farag
Keyword(s):  
Genetic Algorithms ◽  
Immune System ◽  
Hybrid Algorithm ◽  
Clonal Selection ◽  
Fuzzy Rule ◽  
Mutation Rates ◽  
Clonal Selection Algorithm ◽  
Artificial Immune ◽  
Binary Character ◽  
Rule System

The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.

scholarly journals Monthly Rainfall Prediction Model of Peninsular Malaysia Using Clonal Selection Algorithm

2018 ◽  
Vol 7 (4.35) ◽  
pp. 182
Author(s):  
N.S.Noor Rodi ◽  
M.A. Malek ◽  
A.R. Ismail
Keyword(s):  
Immune System ◽  
Clonal Selection ◽  
Peninsular Malaysia ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Series Data ◽  
Monthly Interval ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Rainfall Prediction

Nowadays, various algorithms inspired by natural processes have been extensively applied in solving engineering problems. This study proposed Artificial Immune Systems (AIS), a computational approach inspired by the processes of human immune system, as an algorithm to predict future rainfall. This proposed algorithm is another alternative technique as compared to the commonly used Statistical, Stochastic and Artificial Neural Network techniques traditionally use in Hydrology. Rainfall prediction is pertinent in order to solve many hydrological problems. The proposed Clonal Selection Algorithm (CSA) is one of the main algorithms in AIS, which inspired on Clonal selection theory in the immune system of human body that includes selection, hyper mutation, and receptor editing processes. This study proposed algorithm is utilised to predict future monthly rainfall in Peninsular Malaysia. The collected data includes rainfall and other four (4) meteorological parameters from year 1988 to 2017 at four selected meteorological stations. The parameters used in this analysis are humidity, wind speed, temperature and pressure at monthly interval.  Four (4) meteorological stations involved are Chuping (north), Subang Jaya(west), Senai (south) and Kota Bharu (west) represented peninsular Malaysia. Based on the results at testing stage, it is found that the trend and peaks of the hydrographs from generated data are approximately similar to the actual historical data. The highest similarity percentage obtained is 91%. The high values of similarity percentage obtained between simulated and actual rainfall data in this study, reinforced the hypothesis that CSA is suitable to be used for prediction of continuous time series data such as monthly rainfall data which highly variable in nature.  As a conclusion, the results showed that the proposed Clonal Selection Algorithm is acceptable and stable at all stations.

scholarly journals Artificial Immune System Applied to Job Shop Scheduling

2021 ◽  
pp. 15-22
Author(s):  
Steven Kosasih ◽  
◽  
Cecilia E. Nugraheni ◽  
Luciana Abednego
Keyword(s):  
Immune System ◽  
Lower Bound ◽  
Upper Bound ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Clonal Selection ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Artificial Immune

Job Shop Scheduling is a problem to schedule n number of jobs in m number of machines with a different order of processing. Each machine processes exactly one job at a time. Each job will be processed in every machine once. When a machine is processing one particular job then the other machine can’t process the same job. Different schedule’s order might produce different total processing time. The result of this scheduling problem will be total processing time and schedule’s order. This paper uses clonal selection as the algorithm to solve this problem. The clonal selection algorithm comes from the concept of an artificial immune system. It's developed by copying a human’s immune system behavior. A human’s immune system can differentiate foreign objects and eliminate the objects by creating an antibody. An antibody will go to a cloning process and will mutate to further enhance itself. Clonal selection algorithm applies this cloning and mutation principle to find the most optimal solution. The goal is to find the best schedule’s order and makespan. Taillard’s benchmark is used to verify the quality of the result. To compare the result, we use two values: the upper bound and the lower bound. The upper bound is used to describe the best result of a scheduling problem that has been conducted using a certain environment. On the contrary, the lower bound shows the worst. Experiments on changing the algorithm's parameters are also conducted to measure the quality of the program. The parameters are the number of iterations, mutations, and clone numbers. According to the experiment's results, the higher the number of iteration, mutation rate, and clone number, the better solution for the problem. Clonal selection algorithm has not been able to keep up with upper bound or lower bound values from Taillard’s case. Therefore, parameters need to be increased significantly to increase the chance to produce the optimum result. The higher number of parameters used means the longer time needed to produce the result.

scholarly journals Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6351
Author(s):  
Łukasz Rokicki
Keyword(s):  
Immune System ◽  
Power Systems ◽  
Evolutionary Algorithm ◽  
Distribution System ◽  
Artificial Immune System ◽  
Low Voltage ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Clonal Selection Algorithm ◽  
Artificial Immune

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

scholarly journals A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Weiwei Zhang ◽  
Jingjing Lin ◽  
Honglei Jing ◽  
Qiuwen Zhang
Keyword(s):  
Immune System ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Clonal Selection Algorithm ◽  
Candidate Solution ◽  
Complex Optimization ◽  
Biological Immune System ◽  
Effectiveness And Efficiency ◽  
Clonal Selection Algorithms ◽  
Selection Algorithms

Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive.

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