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Optimization of job shop scheduling problems using modified clonal selection algorithm

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第6期22卷 1528-1539页

作者： Atay, Yilmaz Kodaz, Halife Selcuk Univ Fac Engn Dept Comp Engn Konya Turkey

Artificial immune systems (AISs) are one of the artificial intelligence techniques studied a lot in recent years. AISs are based on the principles and mechanisms of the natural immune system. In this study, the clonal selection algorithm, which is used commonly in AISs, is modified. This algorithm is applied to job shop scheduling problems, which are one of the most difficult optimization problems. For applying application results to the optimum solution, parameter values giving the optimum solution are determined by analyzing the parameters in the algorithm. The obtained results are given in detail in the tables and figures. The best makespan values are reached in 7 out of 10 test problems (FT06, LA01, LA02, LA03, LA04, LA05, and ABZ6). Reasonable makespan values are reached for the remaining 3 problems (FT10, LA16, and ABZ5). The obtained results demonstrate that the developed system can be applied successfully to job shop scheduling problems.

关键词： Artificial immune system clonal selection algorithm job shop scheduling optimization

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Bi-direction quantum crossover-based clonal selection algorithm and its applications

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第16期41卷 7248-7258页

作者： Dai, Hongwei Yang, Yu Li, Hui Li, Cunhua Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China Univ Manchester Sch Elect & Elect Engn Control Syst Ctr Manchester M13 9PL Lancs England

In order to improve the performance of quantum interference crossover, a bi-direction quantum crossover is proposed based on the quantum jump theory. The proposed crossover is inspired by the principle of quantum mechanics. That is, when an electron drops from a higher energy level to a lower energy level, energy is released by the atom. Also, energy is absorbed when it moves from a lower energy level to a higher energy level. The bi-direction quantum crossover is combined with clonal selection algorithm (CSA) to further enhance the performance of CSA. The effectiveness of the method is tested on a class of traveling salesman problems (TSP) and engineering practical problems of holes machining path planning (HMPP). Experimental results show that the proposed algorithm achieves a good balance between exploration and exploitation, and outweighs other CSAs and heuristic algorithms in terms of convergence speed and robustness. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： clonal selection algorithm Bi-direction quantum crossover Traveling salesman problem Holes machining path planning problem Multi-objective optimization

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Modified Elite Chaotic Immune clonal selection algorithm for Sever Resource Allocation in Cloud Computing Systems

Modified Elite Chaotic Immune Clonal Selection Algorithm for...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Zhou, Jie Dutkiewicz, Eryk Liu, Ren Ping Fang, Gengfa Liu, Yuanan Macquarie Univ Dept Engn Sydney NSW Australia Beijing Univ Post & Telecommun Sch Elect Engn Beijing Peoples R China CSIRO Canberra ACT Australia

ISBN: (纸本)9789860334074

Cloud computing is a promising technology to improve computational efficiency for both IT enterprise and individuals. Resource allocation in cloud computing is very challenging as both server computing power and network bandwidth are limited. The computational efficiency of cloud computing system can be significantly improved if the resources are allocated in a balanced fashion. However, resource allocation in cloud computing is a multi-constrained nonlinear optimization problem. The computational complexity for an exhaustive search over all combinations of resource allocations is too high for practical implementation. In this paper, we develop a Modified Elite Chaotic Immune clonal selection algorithm to increase the overall efficiency of the system. An elite strategy and chaotic approaches are designed to improve population diversity and escape from local optima. Performance comparisons are made with simulated annealing algorithm (SA) and three other heuristic algorithms. Simulation results show that the Modified Elite Chaotic Immune clonal selection algorithm solves the resource allocation problem with higher system resource efficiency than all other heuristic algorithms.

关键词： cloud computing resource allocation workload balance clonal selection algorithm

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A Dual-Feedback Adaptive Clone selection algorithm With Golden Sine Search for Parameter Identification of Photovoltaic Models

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IEEE ACCESS 2024年 12卷 20341-20357页

作者： Zhang, Weiwei Yang, Jiaxin He, Qishan Liu, Zhiyang Wang, Junting Rao, Zhi Li, Meng Yu, Xiaoqiu Zhang, Weizheng Zhengzhou Univ Light Ind Coll Comp Sci & Technol Zhengzhou 450000 Peoples R China China Tobacco Henan Ind Co Ltd Anyang Cigarette Factory Anyang 455004 Peoples R China China Tobacco Hubei Ind Co Ltd Wuhan 430040 Peoples R China Hongyun Honghe Tobacco Grp Co Ltd Kunming 650202 Peoples R China Zhengzhou Univ Light Ind Coll Tobacco Sci & Engn Zhengzhou 450000 Peoples R China

Converting solar energy to electricity efficiently requires an accurate model with well-estimated parameters in a photovoltaic (PV) system. However, identifying parameters for the PV model remains challenging attributed to the nonlinearity and multimodality inherent in diode circuit models. In this paper, we propose a dual-feedback adaptive clone selection algorithm with a golden sine search. The clonal selection algorithm (CSA), known for its inherent diversity maintenance ability, offers an advantage in handling multimodal optimization problems. The critical factor is the cloning operator, particularly the determination of clone size, crucial for balancing exploration and exploitation. To address the conflict between clone size and convergence speed, we introduce a dual feedback adaptive cloning strategy. This strategy considers both the fitness difference between the hypermutated population and the original population and the harmonic mean distance of the population. Additionally, to improve accuracy, we introduce an elite learning strategy based on the golden sine mechanism. The proposed algorithm's effectiveness is assessed through comprehensive experiments on PV cells and three PV modules, considering both single diode and double diode models, respectively. The experimental results demonstrate that the accuracy of parameter identification achieved by the proposed algorithm exceeds 99.9% for all three types of PV modules. Moreover, we validate the algorithm's performance using manufacturer's datasheets with varying irradiations and temperatures. All experiments consistently indicate the stability and effectiveness of the proposed method in accurately identifying the parameters of PV models.

关键词： clonal selection algorithm double diode based model PV model single diode based model

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Neural Network Ensemble Based on clonal selection algorithm for Sneak Circuit Analysis

Neural Network Ensemble Based on Clonal Selection Algorithm ...

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International Conference on Sensors, Mechatronics and Automation (ICSMA)

作者： Liu, Bingjie Lu, Wenzhong Ji, Haiyan Navy Submarine Acad Missile Dept Qingdao 266042 Peoples R China

ISBN: (纸本)9783038350170

The paper proposed a novel neural network ensemble algrithm (NNNEA) whose individual was generated by clonal selection algorithm. NNNEA can produced individuals of ensemble with better difference than other algrithm. NNNEA was used for predicting ciruit functions and finding sneak circuit. The inputs of NNNEA are states of switches, and the outputs are states of functional components. NNNEA predicted all possible functions of circuit. The sneak circuits can be discovered by comparing the predicted with designed functions. Although there are several limitations of NNNNEA, the results revealed that NNNNEA can exactly discover sneak circuits.

关键词： sneak circuit analysis neural network ensemble clonal selection algorithm generalization performance

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Determination of Slot Parameters of Band-Notched Ultra Wideband Antenna Using clonal selection algorithm 6

Determination of Slot Parameters of Band-Notched Ultra Wideb...

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6th International Conference on Computational Intelligence and Communication Networks

作者： Devi, R. Neog, Dipak Kr Dhemaji Coll Dept Elect Dhemaji Assam India

ISBN: (纸本)9781479969296

In this paper, a clonal selection algorithm based method is presented for the optimization of slot parameters embedded on ultra-wideband antennas to notch-out frequency bands to prevent interference between ultra wideband (UWB) systems and existing wireless systems. The method is applicable for both half-wavelength and quarter-wavelength slot-resonators. The effects of substrate properties are included in the proposed work. The method is validated by comparing the computed values with experimental values reported earlier. It shows very good agreement.

关键词： Notched resonant frequency clonal selection algorithm half-wavelength slot quarter-wavelength slot ultra wideband

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Design of clonal selection algorithm based PSS for Damping Power System Oscillations 1

Design of Clonal Selection Algorithm based PSS for Damping P...

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International Conference on Smart Electric Grid (ISEG)

作者： Naresh, G. Raju, M. Ramalinga Narasimham, S. V. L. Pragati Engn Coll Elect & Elect Engn Surampalem Andhra Pradesh India JNTUK Univ Coll Engn Kakinada Elect & Elect Engn Kakinada Andhra Pradesh India JNTUK Foreign Univ Relat Kakinada Andhra Pradesh India JNTUH Sch Informat Technol Comp Sci & Engn Hyderabad Andhra Pradesh India

ISBN: (纸本)9781479941049

Power System Stabilizers (PSS) are incorporated in the excitation system to enhance the damping of low frequency oscillations. In this paper, the design of multiple PSS in a multi-machine power system (MMPS) using clonal selection algorithm (CSA) has been proposed. The design problem of the PSS has been formulated as an optimization problem and CSA has been employed to search for optimal controller parameters. It has been shown that the stability performance of the system can be improved by minimizing the eigenvalue based objective function comprising of the damping factor and the damping ratio of the poorly damped electromechanical modes. Eigenvalue analysis and nonlinear simulation results have also been presented for various loading conditions and system configurations to show the effectiveness and robustness of the proposed controllers and their ability to provide efficient damping of low frequency oscillations.

关键词： Damping clonal selection algorithm Power System Stabilizer Multi-machine Power System

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clonal selection based intelligent parameter inversion algorithm for prestack seismic data

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INFORMATION SCIENCES 2020年 517卷 86-99页

作者： Yan, Xuesong Li, Pengpeng Tang, Ke Gao, Liang Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Guangdong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Amplitude variation with offset (AVO) elastic parameter inversion is an approach of oil exploration that employs seismic information, and it is a problem of non-linear optimization. When using a quasi-linear or linear approach to solve the problem, the inversion result is unreliable or inaccurate. Metaheuristic search methods, e.g., bio-inspired optimization algorithms such as genetic algorithms, are capable of handling highly non-linear optimization problems and thus provide a promising approach for oil and gas exploration. As one of the metaheuristic search approaches, the immune clone selection algorithm exhibits the property of fast convergence and strong global search capability. In this paper, the immune clone selection algorithm is used to address the problem of AVO elastic parameter inversion. This algorithm employs the specific initialization strategy of Aki as well as the approximation equation of Rechard, which is utilized in the elastic parameter inversion process to smooth the initialization parameter curve. Additionally, the genetic operation in the algorithm is improved in accordance. The results of multiple experiments demonstrate that the approach could significantly improve the inversion accuracy, and the correlation coefficient of the elastic parameters acquired via inversion is specifically high. (C) 2020 Elsevier Inc. All rights reserved.

关键词： prestack seismic data elastic parameter inversion clonal selection algorithm intelligent algorithms correlation coefficient

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A parallel hybrid approach integrating clonal selection with artificial bee colony for logistic regression in spam email detection

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Neural Computing and Applications 2024年 1-19页

作者： Dedeturk, Bilge Kagan Akay, Bahriye Department of Software Engineering Erciyes University Kayseri Melikgazi38039 Turkey Department of Computer Engineering Erciyes University Kayseri Melikgazi38039 Turkey

Spam emails are sent to recipients for advertisement and phishing purposes. In either case, it disturbs recipients and reduces communication quality. Addressing this issue requires classifying emails on servers as either spam or ham. Numerous methods have been proposed for this classification task. Among them, logistic regression (LR) stands out for its simplicity, speed, and ease of implementation. However, LR suffers from low detection rates caused by the gradient descent algorithm used in its training phase. To overcome this limitation, we propose a novel method based on the clonal selection algorithm (CSA), renowned for its success in optimization problems due to its local and global search capabilities. Despite CSA’s effective optimization performance, it suffers from robustness and slow training time. Therefore, the CSA and artificial bee colony (ABC) algorithms are hybridized to improve CSA’s robustness and are parallelized to reduce the training time significantly. This hybrid method is employed to optimize the weights of LR by minimizing the cost at the output of LR. The empirical results denote that the proposed method, named CSA–ABC–LR, yields better classification performance compared to state-of-the-art models reported by previous studies, demonstrating an accuracy rate of 99.13% on the Enron-1 dataset, 99.22% on the CSDMC2010 dataset, and 94.49% on the Spambase dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Artificial bee colony clonal selection algorithm Email spam detection Logistic regression Spam filtering

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Effect of Text Augmentation and Adversarial Training on Fake News Detection

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第4期11卷 4775-4789页

作者： Ahmed, Hadeer Traore, Issa Saad, Sherif Mamun, Mohammad Univ Victoria Dept Elect & Comp Engn Victoria BC V8P 5C2 Canada Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada Natl Res Council Canada Fredericton NB E3B 9W4 Canada

The action of spreading false information through fake news articles presents a significant danger to society because it has the ability to shape public opinion with inaccurate facts. This can lead to negative effects, such as reduced trust in institutions and the promotion of conflict, division, and even violence. In this article, a text augmentation technique is introduced as a means of generating new data from preexisting fake news datasets. This approach has the potential to enhance classifier performance by a range of 3%-11%. It can also be utilized to launch a successful attack on trained classifiers, with up to a 90% success rate. However, the success rate of these attacks decreased to less than 28% when the model was retrained with the generated adversarial examples. These results demonstrate the effectiveness of text augmentation as a viable method for detecting fake news and increasing classifier accuracy and performance, as well as its ability to be utilized to perform adversarial machine learning (ML) and improve the resilience of ML algorithms.

关键词： Abstract meaning representation (AMR) adversarial training clonal selection algorithm cyberattack fake news graph representation text generation

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