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Complete receptor editing operation based on quantum clonal selection algorithm for optimization problems

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第3期14卷 411-421页

作者： Yang, Yu Dai, Hongwei Gao, Shangce Wang, Yirui Jia, Dongbao Tang, Zheng Univ Toyama Fac Engn Toyama 9308555 Japan Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China

clonal selection mechanism, which is the theoretical foundation of clonal selection algorithm (CSA) and its variants, was proposed for explaining the essential features of adaptive immune responses: adequate diversity, discrimination of self and nonself, and sustaining immunologic memory. On the basis of the clonal selection theory, only the high-affinity immune cells are chosen to proliferate. Those cells with low affinity must be efficiently eliminated. However, the ability of receptor editing to salvage low-affinity immune cells from deletion by changing their receptor specificity realized the clonal selection process anew. By combining clonal selection theory and receptor editing, a quantum clonal selection algorithm based on complete receptor editing operation is proposed for the traveling salesman problem (TSP) and the holes-machining-path-planning (HMPP) problem. Two receptor editing operators (inversion and deletion) work together to improve the performance of CSA. Furthermore, in order to overcome the drawback of asexual proliferation during the immune maturation process, a quantum interference crossover based on complete receptor editing operation is used. The effectiveness of the improved algorithm is evaluated on optimization problems including TSP and HMPP problems. The experimental results are also compared with those of other methods based on clonal selection theory. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： clonal selection algorithm complete receptor editing quantum interference crossover traveling salesman problem holes-machining-path-planning problem

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A hybrid clonal selection algorithm with modified combinatorial recombination and success-history based adaptive mutation for numerical optimization

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APPLIED INTELLIGENCE 2019年第2期49卷 819-836页

作者： Zhang, Weiwei Gao, Kui Zhang, Weizheng Wang, Xiao Zhang, Qiuwen Wang, Hua Zhengzhou Univ Light Ind Zhengzhou 450000 Henan Peoples R China Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou 450000 Henan Peoples R China Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450000 Henan Peoples R China

Artificial immune system is a class of computational intelligence methods drawing inspiration from biological immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. When dealing with complex optimization problems, such as the characteristics of multimodal, high-dimension, rotational, the traditional CSA often suffers from diversity loss, poor search ability, premature convergence and stagnation. To address the problems, a modified combinatorial recombination is introduced to bring diversity to the population and avoid the premature convergence. Moreover, the success-history based adaptive mutation strategy is introduced to form a success-history based adaptive mutation based clonal selection algorithm to improve the search ability. The mutation operator is also modified and analyzed through experimental comparison. To further improve the precision and cope with the stagnation, the gene knockout strategy is proposed. The proposed algorithm is tested on CEC 2014 benchmarks and compared with state-of-the-art evolutionary algorithms. The experimental results show that MSHCSA is quite competitive.

关键词： Immune system clonal selection algorithm Optimization Mutation Adaptive

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An Efficient Binary clonal selection algorithm with Optimum Path Forest for Feature selection

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第7期11卷 259-267页

作者： Nabil, Emad Sayed, Safinaz Abdel-Fattah Hameed, Hala Abdel Cairo Univ Fac Comp & Artificial Intelligence Dept Comp Sci Giza Egypt Islamic Univ Madinah Fac Comp & Informat Syst Madinah Saudi Arabia Luxor Univ Fac Comp & Informat Dept Comp Sci Luxor Egypt Fayoum Univ Fac Comp & Informat Faiyum Egypt Taibah Univ Khaybar Community Coll Medina Saudi Arabia

Feature selection is an important step in different applications such as data mining, classification, pattern recognition, and optimization. Until now, finding the most informative set of features among a large dataset is still an open problem. In computer science, a lot of metaphors are imported from nature and biology and proved to be efficient when applying them in an artificial way to solve a lot of problems. Examples include Neural Networks, Human Genetics, Flower Pollination, and Human Immune system. clonal selection is one of the processes that happens in the human immune system while recognizing new infections. Mimicking this process in an artificial way resulted in a powerful algorithm, which is the clonal selection algorithm. In this paper, we tried to explore the power of the clonal selection algorithm in its binary form for solving the feature selection problem, we used the accuracy of the Optimum-Path Forest classifier, which is much faster than other classifiers, as a fitness function to be optimized. Experiments on three public benchmark datasets are conducted to compare the proposed Binary clonal selection algorithm in conjunction with the Optimum Path Forest classifier with other four powerful algorithms. The four algorithms are Binary Flower Pollination algorithm, Binary Bat algorithm, Binary Cuckoo Search, and Binary Differential Evolution algorithm. In terms of classification accuracy, experiments revealed that the proposed method outperformed the other four algorithms and moreover with a smaller number of features. Also, the proposed method took less average execution time in comparison with the other algorithms, except for Binary Cuckoo Search. The statistical analysis showed that our proposal has a significant difference in accuracy compared with the Binary Bat algorithm and the Binary Differential Evolution algorithm.

关键词： Feature selection artificial immune system clonal selection algorithm optimization optimum path forest

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Solving DNA Motif Discovery Problem using Improved clonal selection algorithm with Tournament selection Operator

Solving DNA Motif Discovery Problem using Improved Clonal Se...

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1st International Conference on Advanced Information Science and System (AISS)

作者： Ulker, Ezgi Deniz European Univ Lefke Dept Comp Engn Mersin 10 Karavostasi Turkey

ISBN: (纸本)9781450372916

Identifying the regulatory elements in deoxyribonucleic acid (DNA) is a challenging area of research. The order of nucleotides in a DNA sequence determines the genetic functionality. The common nucleotides called motifs which have short sizes are placed in large number of sequences. Because of the difficulty to obtain the motifs among the large number of sequences, Motif Discovery Problem (MDP) is referred as a NP-hard problem. Since this problem is considered an open area for researchers, the evolutionary algorithms can be used to bring new solutions to this field. In this paper, Improved clonal selection algorithm with Tournament selection operator (ICSAT) is adapted to solve MDP by using real DNA sequences and compared with some other algorithms which are particularly designed to solve it. The results denote that ICSAT obtains motifs between the large sequences and it produces efficient results in terms of motif length and similarity values with respect to the compared algorithms. It can be used as a good candidate for solving these kinds of problems in bioinformatics.

关键词： Biological optimization clonal selection algorithm Motif discovery Tournament selection

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clonal selection algorithm for Optimum Sizing of WT/PV/FC Hybrid Renewable Power System 1

Clonal Selection Algorithm for Optimum Sizing of WT/PV/FC Hy...

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1st International Conference on Computer Applications and Information Security (ICCAIS)

作者： Hatata, A. Y. Lafi, Alnufaie Shaqra Univ Dept Elect Engn Engn Collage Shaqraa Saudi Arabia

ISBN: (纸本)9781538644270

This paper obtains the optimum size of wind turbine/PV/fuel cell hybrid renewable power system (HRPS) using both Artificial Immune System (AIS) and HOMER software. The AIS is based on clonal selection algorithm (clonalG). The optimum size of the hybrid system will achieve two goals;satisfy the load demand with least loss of power supply probability and have to minimize the overall cost of the whole system. The cost of the optimum hybrid system includes initial, replacement, operating and maintenance cost (O&M) and salvage cost which are calculated during the whole lifetime of the project considering a discount factor. The hybrid system is determined to feed an electrical load at located at Qena AL-Gadeda City in Egypt using its predicted solar irradiation and wind data.

关键词： hybrid renewable power system PV Wind turbine fuel cell HOMER software clonal selection algorithm loss of power supply probability

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Solving DNA motif discovery problem using improved clonal selection algorithm with tournament selection operator: DNA motif discovery with clonal selection algorithm 19

Solving DNA motif discovery problem using improved Clonal se...

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Proceedings of the 1st International Conference on Advanced Information Science and System

作者： Ezgi Deniz Ülker European University of Lefke Turkey

ISBN: (纸本)9781450372916

关键词： clonal selection algorithm tournament selection biological optimization motif discovery

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Prediction of Protein Secondary Structure With clonal selection algorithm and Multilayer Perceptron

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IEEE ACCESS 2018年 6卷 45256-45261页

作者： Yavuz, Burcu Carkli Yurtay, Nlufer Ozkan, Ozhan Sakarya Univ Dept Informat Syst Engn TR-54050 Sakarya Turkey Sakarya Univ Dept Comp Engn TR-54050 Sakarya Turkey Sakarya Univ Dept Elect & Elect Engn TR-54050 Sakarya Turkey

The recent studies indicate that the protein secondary structure provides very important advantages in determining the function of a protein, treating numerous diseases and drug design. Determining the secondary structure in the laboratory environment is both costly and challenging. Therefore, the prediction of protein secondary structure has been an important study field of bioinformatics and computational biology for many years. The aim of this paper was to provide a contribution to the prediction of protein secondary structure using the nature-inspired methods. The data in the first phase were trained with clonal selection algorithm (CSA) which was modeled by being inspired by the live immune system. The classification was then performed with multilayer perceptron which is one of the deep learning methods modeled by being inspired by the biological nervous system. The results obtained indicated that training of the data with CSA prior to classification contributed positively to classification success.

关键词： clonal selection algorithm deep learning hemoglobin protein multilayer perceptron prediction of protein secondary structure

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A cluster-based clonal selection algorithm for optimization in dynamic environment

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100454-000页

作者： Zhang, Weiwei Zhang, Weizheng Yen, Gary G. Jing, HongLei Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou 45000 Henan Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Comparing with stationary optimization problems, dynamic optimization problems pose more serious challenge to traditional optimization algorithms. For solving such problem, the algorithms should track the changing optima persistently. In this paper, a cluster based clonal selection algorithm for global optimization in a dynamic fitness landscape is presented. The population is partitioned into multiple clusters according to the spatial locations at first, and then each cluster is evolved separately by using a learning based clonal selection algorithm, in which, the learning strategy within the cluster and interaction among clusters are introduced to the hypermutation operator to improve search ability. In addition, memory mechanism is presented to deposit the previous searching information and reused for optima tracking after environmental change. Experimental results demonstrate that the proposed algorithm is superior to the immune based algorithms and is competitive with respect to the state of the art designs for dynamic optimization problems.

关键词： clonal selection algorithm Dynamic optimization problem Memory artificial immune system Learning strategy

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A clonal selection algorithm for dynamic multimodal function optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100459-000页

作者： Luo, Wenjian Lin, Xin Zhu, Tao Xu, Peilan Univ Sci & Technol China Anhui Prov Key Lab Software Engn Comp & Commun Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Univ South China Sch Software Engn Hengyang Hunan Peoples R China

The objective of dynamic multimodal optimization problems (DMMOPs) is to find all global optima in a dynamic environment. Although dynamic optimization problems (DOPs) have been widely studied in the field of nature-inspired computation, DMMOPs have not yet been paid significant attention yet. It is a challenging task for the classic clonal selection algorithm (CSA) to track all the moving global optima of dynamic multimodal optimization problems. The population of the classic CSA tends to converge to a single optimum, and the shortage of population diversity prevents the classic CSA from adapting to environmental changes. To address these limitations, this paper proposes a so-called dynamic multimodal clonal selection algorithm (DMMCSA). DMMCSA incorporates a niching method called nearest-better clustering, and adapts the scale factor of the hypermutation operator according to the distances among individuals. Experiments on benchmark problems show that DMMCSA significantly outperforms CSA in terms of tracking all the global optima of DMMOPs.

关键词： clonal selection algorithm Dynamic optimization Multimodal optimization Benchmark problem

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Multi-objective Test Case Prioritization Using Improved Pareto-Optimal clonal selection algorithm

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3D RESEARCH 2018年第3期9卷 1-13页

作者： Tulasiraman, Megala Vivekanandan, Nivethitha Kalimuthu, Vivekanandan Pondicherry Engn Coll Dept Comp Sci Pondicherry India Vel Tech Inst Sci & Technol Dept Comp Sci Madras Tamil Nadu India

Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. [GRAPHICS] .

关键词： Pareto-optimal Multi-objective test case prioritization clonal selection algorithm

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