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immune algorithms-based approach for redundant reliability problems with multiple component choices

COMPUTERS IN INDUSTRY 2005年第2期56卷 195-205页

作者： Chen, TC You, PS Natl Formosa Univ Dept Informat Management Yunlin 632 Taiwan Natl Chiayi Univ Grad Inst Transportat & Logist Chiayi 600 Taiwan

This paper considers the series-parallel redundant reliability problems in which both the multiple component choices of each subsystem and the redundancy levels of every selected component are to be decided simultaneously so as to maximize the system reliability. The reliability design optimization problem has been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. The difficulties encountered for both methodologies are the number of constraints and the difficulty of satisfying the constraints. A penalty-guided immune algorithms-based approach is presented for solving such integer nonlinear redundant reliability design problem. The results obtained by using immune algorithms-based approach are compared with the results obtained from 33 test problems from the literature that dominate the previously mentioned solution techniques. As reported, solutions obtained by the proposed method are better than or as well as the previously best-known solutions. (C) 2004 Elsevier B.V. All rights reserved.

关键词： redundant reliability problem immune algorithms optimization

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Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms

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APPLIED MATHEMATICS AND COMPUTATION 2011年第12期217卷 5318-5327页

作者： Lin, Kuo-Ping Pai, Ping-Feng Yang, Shun-Ling Natl Chi Nan Univ Dept Informat Management Nantou Taiwan Lunghwa Univ Sci & Technol Dept Informat Management Tao Yuan 333 Taiwan Tunghai Univ Dept Ind Engn & Enterprise Informat Taichung 40704 Taiwan

The need to minimize the potential impact of air pollutants on humans has made the accurate prediction of concentrations of air pollutants a crucial subject in environmental research. Support vector regression (SVR) models have been successfully employed to solve time series problems in many fields. The use of SVR models for forecasting concentrations of air pollutants has not been widely investigated. Data preprocessing procedures and the parameter selection of SVR models can radically influence forecasting performance. This study proposes a support vector regression with logarithm preprocessing procedure and immune algorithms (SVRLIA) model which takes advantage of the structural risk minimization of SVR models, the data smoothing of preprocessing procedures, and the optimization of immune algorithms, in order to more accurately forecast concentrations of air pollutants. Three pollutants, namely particulate matter (PM10), nitrogen oxide, (NOx), and nitrogen dioxide (NO2), are collected and examined to determine the feasibility of the developed SVRLIA model. Experimental results reveal that the SVRLIA model can accurately forecast concentrations of air pollutants. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Concentrations of air pollutants Support vector regression Data preprocessing Parameter selection immune algorithms

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Emphasizing typing signature in keystroke dynamics using immune algorithms

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APPLIED SOFT COMPUTING 2015年 34卷 178-193页

作者： Pisani, Paulo Henrique Lorena, Ana Carolina Univ Fed Abc Santo Andre Brazil UNIFESP ICT Sao Jose Dos Campos Brazil

Improved authentication mechanisms are needed to cope with the increased data exposure we face nowadays. Keystroke dynamics is a cost-effective alternative, which usually only requires a standard keyboard to acquire authentication data. Here, we focus on recognizing users by keystroke dynamics using immune algorithms, considering a one-class classification approach. In such a scenario, only samples from the legitimate user are available to generate the model of the user. Throughout the paper, we emphasize the importance of proper data understanding and pre-processing. We show that keystroke samples from the same user present similarities in what we call typing signature. A proposal to take advantage of this finding is discussed: the use of rank transformation. This transformation improved performance of classification algorithms tested here and it was decisive for some immune algorithms studied in our setting. (C) 2015 Elsevier B.V. All rights reserved.

关键词： One class classification Data pre-processing immune algorithms Keystroke dynamics

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Enhancement of thermal unit commitment using immune algorithms based optimization approaches

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 1999年第4期21卷 245-252页

作者： Huang, SJ Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan

A new approach using immune algorithms to solve thermal generation scheduling problems is proposed in this article. In the proposed scheme, the objective functions and constraints were categorized as antigens. The antibodies for the immune system were determined through the calculation of affinity. For an antibody that is perfectly combined with the antigen, this antibody is deemed the solutions to the problem. With the embodiment of affinity computation, the possibility of stagnation in the iteration process was decreased. The computational performance can be also enhanced. The proposed method was tested on a practical Taiwan Power System (Taipower) through the utility data. The test results demonstrated the feasibility of this method for unit-scheduling applications. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： thermal generation scheduling immune algorithms optimization

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Near-Optimal Scheduling based on immune algorithms in Distributed Environments

Near-Optimal Scheduling based on Immune Algorithms in Distri...

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4th International Conference on Complex, Intelligent and Software Intensive Systems (CICIS)

作者： Istin, Mihai Visan, Andreea Pop, Florin Dobre, Ciprian Cristea, Valentin Univ Politehn Bucuresti Fac Automat & Comp Sci Bucharest Romania

ISBN: (纸本)9780769539676

One of the most important management aspects in Grid systems is task scheduling. This component should try to achieve two main objectives: efficient use of available resources and high performance in solving tasks given by members of virtual organizations. The scheduling problem is one of the hardest problems and it is proved that it is an NP-Complete problem. In order to have good performances heuristic algorithms are required. This paper presents a near-optimal algorithm for dependent task scheduling in distributed systems. The algorithm is based on both genetic and immune algorithms. The genetic componment is used in order to evolve a population of chromosomes representing potential solutions. In order to increase the performances of the genetic algorithm in terms of convergence times, in the initialization stage it is used an immune algorithm that produces an initial population with a good average fitness.

关键词： Scheduling Distributed Systems immune algorithms Genetic algorithms

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Determination of protein structure and dynamics combining immune algorithms and pattern search methods

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Natural Computing 2007年第1期6卷 55-72页

作者： Anile, A.M. Cutello, V. Narzisi, G. Nicosia, G. Spinella, S. Department of Mathematics and Computer Science University of Catania 95125 Catania V.le A. Doria 6 Italy Department of Linguistics University of Calabria 87036 Arcavata di Rende CS Ponte P. Bucci 17/B Italy

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multiple-minima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms. © Springer Science+Business Media, Inc. 2007.

关键词： Clonal Selection algorithms DIRECT immune algorithms Pattern search methods Protein folding Protein structure prediction Structural bioinformatics

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Testing the immune algorithms for Electrical Transport Using Mathematical Methods 8

Testing the Immune Algorithms for Electrical Transport Using...

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8th International Conference on Intelligent Technologies in Logistics and Mechatronics Systems, ITELMS 2013

作者： Mor-Yaroslavtsev, A. Levchenkov, A. Riga Tech Univ LV-1010 Riga Latvia

Objective of this paper is to test the immune algorithms for an intelligent rolling stock safety system which could provide a possibility for railway transport to avoid dangerous situations. The authors examine the algorithms used in artificial immune systems and ways how they can be used together and provide data for each one other via communication protocols, then create a simulation for further analysis with mathematical methods. The authors review data analysis methods used to detect, predict and control undesirable rolling stock travel conditions.

关键词： immune algorithms classification railway transport

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Improvement of Two-swarm Cooperative Particle Swarm Optimization Using immune algorithms and Swarm Clustering 11

Improvement of Two-swarm Cooperative Particle Swarm Optimiza...

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IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)

作者： Hayashida, Tomohiro Nishizaki, Ichiro Sekizaki, Shinya Takamori, Yuki Hiroshima Univ Grad Sch Engn Higashihiroshima Hiroshima Japan

ISBN: (纸本)9781728124292

Particle Swarm Optimization (PSO) is useful as a method for solving optimization problems with continuous value variables because the convergence speed of solution search is fast. PSO is a evolutionary computation method in which individuals (particles) with position and velocity information are placed in the search space and acts for the purpose of finding an optimal solution with sharing information with other particles. This study constructs a particle swarm optimization method introducing the immune algorithms to improve the search capability of each particle and perform solution search more efficiently. To verify the usefulness of the proposed method, some numerical experiments are performed in this study.

关键词： Particle swarm optimization evolutionary computation immune algorithms two-swarm optimization

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Stacked Autoencoder based Feature Compression for Optimal Classification of Parkinson Disease from Vocal Feature Vectors using immune algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第5期12卷 470-476页

作者： Kamalakannan, K. Anandharaj, G. Adhiparasakthi Coll Arts & Sci PG & Res Dept Comp Sci Kalavai TN India

Parkinson's disease (PD) is a neurological progressive disorder and is most common among people who are above 60 years old. It affects the brain nerve cells due to the deficiency of dopamine secretion. Dopamine acts as a neurotransmitter and helps in the movement of the body parts. Once brain cells/neurons start dying due to aging, then it will lead to a decrease in dopamine levels. The symptoms of Parkinson's are difficultly in doing regular/habitual movements, uncontrollable shaking of hands and limbs may encounter memory loss, stiff muscles, sudden temporary loss of control, etc. The severity of the disease will be worse if not diagnosed and treated at the early stages. This paper concentrates on developing Parkinson's disease diagnosing system using machine learning techniques and algorithms. Machine Learning is an integral part of artificial intelligence it takes huge data as input and train by making use of existing algorithms to understand the pattern of the data. Based on the recognized pattern, the machine will act accordingly without any human intervention. In this work, two major approaches have been employed to diagnose PD. Initially, 26 vocal data of PD affected and healthy individual datasets are obtained from the UCI Machine Learning data repository, are taken as initial raw data/features. In pre-processing, the mRMR feature selection algorithm is employed to minimize the feature count and increase the accuracy rate. The selected features will further be extracted using the Stacked Autoencoder technique to improve and increase the accuracy rate and quality of classification with reduced run time. K-fold cross-validation is used to evaluate the predictive capability of the model and the effectiveness of the extracted features. Artificial immune Recognition System - Parallel (AIRS-P), an immune inspired algorithm is employed to classify the data from the extracted features. The proposed system attained 97% accuracy, outperforms the benchmarked algorithm

关键词： immune algorithms Parkinson's disease stacked autoencoder airs-parallel machine learning

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Application of immune and genetic algorithms to the identification of a polymer based on its X-ray diffraction curve

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JOURNAL OF APPLIED CRYSTALLOGRAPHY 2013年第4期46卷 1136-1144页

作者： Rabiej, Malgorzata Univ Bielsko Biala Bielsko Biala Poland

This paper describes how a combination of two methods of artificial intelligence, an immune algorithm and a genetic algorithm, can be used to recognize a polymer by the shape of its X-ray diffraction curve. To this end, the hybrid algorithm uses a database which contains theoretical functions describing wide-angle X-ray diffraction curves of different polymers. These curves are compared by the algorithm with the experimental diffraction curve and the most similar are chosen. Such theoretical curves are kept in the immunological memory, and their parameters can be set as the starting ones in the optimization methods used for decomposition of the experimental curve into crystalline peaks and amorphous component. Using this algorithm, the preparation of the starting parameters is much easier and faster. Decomposition is the most important step in polymer crystallinity determination.

关键词： immune algorithms genetic algorithms deconvolution starting parameters diffraction curves

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