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.

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.

Clonal Selection Algorithms for Optimal Product Line Design: A Comparative Study

2021 ◽  
Author(s):  
Michail Pantourakis ◽  
Stelios Tsafarakis ◽  
Konstantinos Zervoudakis ◽  
Efthymios Altsitsiadis ◽  
Andreas Andronikidis ◽  
...  
Keyword(s):  
Comparative Study ◽  
Clonal Selection ◽  
Product Line ◽  
Product Line Design ◽  
Clonal Selection Algorithms ◽  
Selection Algorithms ◽  
Line Design

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 A Clone Selection Based Real-Valued Negative Selection Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-20
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Negative Selection ◽  
Immune Cell ◽  
Clonal Selection ◽  
Space Distribution ◽  
Small Radius ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Immune Cell Population ◽  
Negative Selection Algorithm ◽  
Selection Algorithms

Excessive detectors, high time complexity, and loopholes are main problems which current negative selection algorithms have face and greatly limit the practical applications of negative selection algorithms. This paper proposes a real-valued negative selection algorithm based on clonal selection. Firstly, the algorithm analyzes the space distribution of the self set and gets the set of outlier selves and several classification clusters. Then, the algorithm considers centers of clusters as antigens, randomly generates initial immune cell population in the qualified range, and executes the clonal selection algorithm. Afterwards, the algorithm changes the limited range to continue the iteration until the non-self space coverage rate meets expectations. After the algorithm terminates, mature detector set and boundary self set are obtained. The main contributions lie in (1) introducing the clonal selection algorithm and randomly generating candidate detectors within the stratified limited ranges based on clustering centers of self set; generating big-radius candidate detectors first and making them cover space far from selves, which reduces the number of detectors; then generating small-radius candidate detectors and making them gradually cover boundary space between selves and non-selves, which reduces the number of holes; (2) distinguishing selves and dividing them into outlier selves, boundary selves, and internal selves, which can adapt to the interference of noise data from selves; (3) for anomaly detection, using mature detector set and boundary self set to test at the same time, which can effectively improve the detection rate and reduce the false alarm rate. Theoretical analysis and experimental results show that the algorithm has better time efficiency and detector generation quality according to classic negative selection algorithms.

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