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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Luo, Wenjian Lin, Xin Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Software Engn Comp & Commun Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781538627266

clonal selection algorithms (CSAs) are a kind of Artificial Immune algorithms (AIAs). In this paper, recent advances in clonal selection algorithms are summarized and reviewed. First, the basic framework of clonal selection algorithms is given. Second, various types of applications using clonal selection algorithms are summarized, including global optimization, constrained optimization, combinatorial optimization, multiobjective optimization, dynamic optimization and other applications. Last, a brief conclusion and some remarks about clonal selection algorithms are given.

关键词： Immune Computation clonal selection algorithms Optimization

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Recent Advances in clonal selection algorithms and Applications

Recent Advances in Clonal Selection Algorithms and Applicati...

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IEEE Symposium Series on Computational Intelligence

作者： Wenjian Luo Xin Lin Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei Anhui China

关键词： Immune Computation clonal selection algorithms Optimization

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Modifying Regeneration Mutation and Hybridising clonal selection for Evolutionary algorithms Based Timetabling Tool

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-16页

作者： Thepphakorn, Thatchai Pongcharoen, Pupong Hicks, Chris Pibulsongkram Rajabhat Univ Fac Ind Technol Phitsanulok 65000 Thailand Naresuan Univ Ind Engn Dept CORIA Fac Engn Phitsanulok 65000 Thailand Newcastle Univ Sch Business Newcastle Upon Tyne NE1 7RU Tyne & Wear England

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population;(ii) included the clonal selection algorithm (CSA);and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms;(ii) identify which factors and interactions were statistically significant;(iii) identify appropriate parameters for the GA and MA;and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.

关键词： clonal selection algorithms EVOLUTIONARY algorithms TIME perspective MEMETICS FACTOR analysis

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Design and characterization of effective solar cells

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2022年第2期13卷 355-382页

作者： Ojha, Varun Jansen, Giorgio Patane, Andrea La Magna, Antonino Romano, Vittorio Nicosia, Giuseppe Univ Reading Dept Comp Sci Reading Berks England Univ Cambridge Cambridge Syst Biol Ctr Dept Biochem Cambridge England Univ Oxford Dept Comp Sci Oxford England CNR Inst Microelect & Microsyst Catania Italy Univ Catania Dept Math & Comp Sci Catania Italy Univ Cambridge Syst Biol Ctr Cambridge England Univ Catania Dept Biomed & Biotechnol Sci Catania Italy

We propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency (Q(e)) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, Q(e) of a cell with its TCO layer's doping methods and TCO and BR layer's material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

关键词： Thin-film silicon solar cell Quantum efficiency Maxwell simulation Multi-objective optimization Pareto optimality Artificial immune systems Optimization Immunological algorithms OptIA-II clonal selection algorithms

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Conformational Sampling of a Biomolecular Rugged Energy Landscape

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018年第3期15卷 732-739页

作者： Rydzewski, Jakub Jakubowski, Rafal Nicosia, Giuseppe Nowak, Wieslaw Nicolaus Copernicus Univ Inst Phys Fac Phys Astron & Informat Grudziadzka 5 PL-87100 Torun Poland Univ Catania Dept Math & Comp Sci Viale A Doria 6 I-95125 Catania Italy

The protein structure refinement using conformational sampling is important in hitherto protein studies. In this paper, we examined the protein structure refinement by means of potential energy minimization using immune computing as a method of sampling conformations. The method was tested on the x-ray structure and 30 decoys of the mutant of [Leu] Enkephalin, a paradigmatic example of the biomolecular multiple-minima problem. In order to score the refined conformations, we used a standard potential energy function with the OPLSAA force field. The effectiveness of the search was assessed using a variety of methods. The robustness of sampling was checked by the energy yield function which measures quantitatively the number of the peptide decoys residing in an energetic funnel. Furthermore, the potential energy-dependent Pareto fronts were calculated to elucidate dissimilarities between peptide conformations and the native state as observed by x-ray crystallography. Our results showed that the probed potential energy landscape of [Leu] Enkephalin is self-similar on different metric scales and that the local potential energy minima of the peptide decoys are metastable, thus they can be refined to conformations whose potential energy is decreased by approximately -250 kJ/mol.

关键词： Structure refinement configuration space self-similarity potential energy landscape clonal selection algorithms

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A Novel Hybrid clonal selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2016年第1期2016卷 1-14页

作者： Zhang, Weiwei Lin, Jingjing Jing, Honglei Zhang, Qiuwen Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou 450000 Peoples R China

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.

关键词： clonal selection algorithms IMMUNE system RECOMBINATION (Chemistry) MUTATION (Biology) GENETIC recombination

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An Information-Theoretic Approach for clonal selection algorithms

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9th International Conference on Artificial Immune Systems (ICARIS 2010)

作者： Cutello, Vincenzo Nicosia, Giuseppe Pavone, Mario Stracquadanio, Giovanni Univ Catania Dept Math & Comp Sci I-95125 Catania Italy

ISBN: (纸本)9783642145469

In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions;the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Renyi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values.

关键词： clonal selection algorithms population-based algorithms information theory relative entropy global numerical optimization

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An immune-inspired algorithm for an unrelated parallel machines' scheduling problem with sequence and machine dependent setup-times for makespan minimisation

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NEUROCOMPUTING 2015年 163卷 94-105页

作者： Marinho Diana, Rodney Oliveira de Franca Filho, Moacir Felizardo de Souza, Sergio Ricardo de Almeida Vitor, Joao Francisco Fed Ctr Technol Educ Minas Gerais CEFET MG BR-30510000 Belo Horizonte MG Brazil

This paper proposes an immune-inspired algorithm to the problem of minimising the makespan on unrelated parallel machines, with sequence dependent setup times. The initial population is generated through the construction phase of the Greedy Randomised Adaptive Search Procedure (GRASP). An evaluation function is proposed to help the algorithm escape from local optima. A Variable Neighbourhood Descent (VND) local search heuristic, which makes significant use of the characteristics of the problem, is proposed as a somatic hypermutation operator to accelerate the convergence of the algorithm. A population re-selection operator, which strategically keeps good quality solutions with a high level of dispersion in the search space, is also proposed. The experiments performed show that the proposed algorithm enables better results than those reported in recent literature studies. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Scheduling Makespan Artificial immune systems clonal selection algorithms

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Immunological Computation for Protein Function Prediction

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FUNDAMENTA INFORMATICAE 2015年第1期139卷 91-114页

作者： Zekri, Meriem Alem, Karima Souici-Meslati, Labiba Badji Mokhtar Annaba Univ Dept Comp Sci LabGED Lab LISCO Lab Annaba Algeria Badji Mokhtar Annaba Univ LBBM Lab Dept Biochem Annaba Algeria Badji Mokhtar Annaba Univ Dept Comp Sci LISCO Lab Annaba Algeria

Immunological computation is one of the largest recent bio-inspired approaches of artificial intelligence. Artificial immune systems (AIS) are inspired by the processes of the biological immune systems like the learning and memory characteristics which are used for solving complex problems. During the last two decades, AIS have been applied in various fields such as optimization, network security and data mining. In this article, we focus on the application of AIS to data mining in bioinformatics, more specifically, the classification task. For this purpose, we suggest three immune models based on clonal selection theory for the identification of G-protein coupled receptors (GPCRs) to predict their function. Our three classifiers are the artificial immune recognition system (AIRS), the clonal selection algorithm (clonalG) and the clonal selection classification algorithm (CSCA). The GPCRs represent one of the largest and most important families of multifunctional proteins and are a significant target for bioactive and drug discovery programs. It is estimated that more than half of the drugs on the market currently target GPCRs. However, although thousands of GPCRs sequences are known, many of them remain orphans, have unknown function. Our experiments show that the three immunological classifiers have provided interesting results, however, AIRS obtained the best ones. Therefore, it is, for us, the most suitable immune model for the GPCRs identification problem.

关键词： Artificial immune system Protein function prediction GPCRs Classification clonal selection algorithms Pseudo amino acid composition

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A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2014年第1期2014卷 1页

作者： Shu, Wanneng Wang, Wei Wang, Yunji South Cent Univ Nationalities Coll Comp Sci Wuhan 430074 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610064 Peoples R China Univ Texas San Antonio Elect & Comp Engn Dept San Antonio TX 78249 USA

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.

关键词： Green cloud computing Dynamic voltage and frequency scaling Resource allocation Service level agreement clonal selection algorithms

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