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

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.

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A Clone Selection Based Real-Valued Negative Selection Algorithm

Complexity ◽

10.1155/2018/2520940 ◽

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

Computational Intelligence and Neuroscience ◽

10.1155/2016/6204728 ◽

2016 ◽

Vol 2016 ◽

pp. 1-14 ◽

Cited By ~ 3

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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Immune-Inspired Quantum Genetic Optimization Algorithm and its Application

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.143-144.547 ◽

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.

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