A review of clonal selection algorithm and its applications

Ulutas, Berna Haktanirlar; Kulturel-Konak, Sadan

doi:10.1007/s10462-011-9206-1

Cited by 116 publications

(21 citation statements)

References 81 publications

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“…Among them, the clonal selection algorithm is a special class of IA, and it is inspired by the clonal selection principle. Recently, clonal selection algorithm is very popular in the IA community and brings about a large number of applications, such as optimization, learning, clustering, and so on [52]. For solving optimization problems, clonal selection algorithm utilizes a collective learning process of a population of antibodies, and undergoes a cycle process of clonal proliferation, maturation, and antibody selection.…”

Section: Immune Algorithmmentioning

confidence: 99%

Immune algorithm combined with estimation of distribution for traveling salesman problem

Wang

et al. 2016

IEEJ Transactions Elec Engng

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This paper describes an artificial immune algorithm (IA) combined with estimation of distribution algorithm (EDA), named IA-EDA, for the traveling salesman problem (TSP). Two components are incorporated in IA-EDA to further improve the performance of the conventional IA. First, aiming to strengthen the information exchange during different solutions, two kinds of EDAs involving univariate marginal distribution algorithm and population-based incremental learning are altered based on the permutation representation of TSP. It is expected that new promising candidate solutions can be sampled from the constructed probabilistic model of EDA. Second, a heuristic refinement local search operator is proposed to repair the infeasible solutions sampled by EDA. Therefore, IA-EDA can alleviate the deficiencies of the conventional IA and can find better solutions for TSP by well balancing the exploitation and exploration of the search. Experiments are conducted based on a number of benchmark instances with size up to 100 000 cities. Simulation results show that IA-EDA is effective for improving the performance of the conventional IA and can produce better or competitive solutions than other hybrid algorithms. (Non-member) received the M.S. degree from the University of Toyama (UT), Toyama, Japan, in 2011. He is currently working toward the Ph.D. degree in the Faculty of Engineering, UT. His research interests include artificial immune algorithms, evolutionary algorithms, and combinatorial optimizations. Yirui Wang (Non-member) received the B.S. degree from Donghua University (DU), Shanghai, China, in 2014. He is currently working toward the M.S. degree at the College of Information Sciences and Technology, DU. His current research interests include computational intelligence, swarm intelligent algorithms, and combinatorial optimizations. Zhe Xu S153IEEJ Trans 11(S1): S142-S154 (2016) Z. XU ET AL.Sheng Li (Non-member) received the B.S. degree from Nanjing

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Section: Immune Algorithmmentioning

confidence: 99%

Immune algorithm combined with estimation of distribution for traveling salesman problem

Wang

et al. 2016

IEEJ Transactions Elec Engng

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“…Besides the natural tasks of anomaly detection and classification, they are often applied to function optimization. In this context, mostly algorithms are based on the principles of clonal selection and antibody maturation in the adaptive immune response [39].…”

Section: Learning Operators In Aismentioning

confidence: 99%

A Multi-Learning Immune Algorithm for Numerical Optimization

Wang

Gao

Aorigele

et al. 2015

IEICE Trans. Fundamentals

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SUMMARYThe emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

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“…CS is a subclass of the Artificial Immune System (AIS) algorithms, its characteristics being represented by diversity, optimization and exploration [33]. The types of problems solved with CS include: function optimization, pattern recognition, design problems, scheduling and classification [34]. The domains of these problems are varied, in the area of chemical engineering only a few applications being encountered.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

Curteanu

Drăgoi

Dafinescu

2015

Advances in Computational Intelligence

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Abstract.A modelling procedure based on hybrid configuration composed of artificial neural networks, differential evolution and clonal selection algorithms is developed and applied in this work. The neural network represents the model of the system, while the differential evolution and clonal selection algorithms perform a simultaneous topological and parametric optimization of the model. The results indicated that the combination of the two optimizers produces better results compared with each of them working separately. As case study, styrene polymerization, a complex process which is difficult to model when taking into consideration all the internal interactions, was chosen. Neural networks, designed in an optimal form, proved to be adequate tools for modelling this system.

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A review of clonal selection algorithm and its applications

Cited by 116 publications

References 81 publications

Immune algorithm combined with estimation of distribution for traveling salesman problem

Immune algorithm combined with estimation of distribution for traveling salesman problem

A Multi-Learning Immune Algorithm for Numerical Optimization

Evolutionary Hybrid Configuration Applied to a Polymerization Process Modelling

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