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Evaluation and improvement of the creativity of works of art by integrating artificial immune algorithm

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Xiuming Xinxiang Univ Acad Fine Arts Xinxiang 453003 Peoples R China

The creativity of artistic works encompasses multifaceted elements such as color, shape, and texture, characterized by their complexity and subtlety. Current evaluation methods are predominantly subjective, lacking the ability to objectively and comprehensively capture and quantify these dynamic attributes. To address this limitation, this study proposes an innovative approach for evaluating artistic creativity by integrating an artificial immune algorithm (AIA). Initially, features related to color, shape, and texture are extracted from the artworks, and these feature vectors are input into the AIA as antigens. Subsequently, by defining antigen-antibody matching rules, the features of the artworks are compared with creative reference antibodies generated by the algorithm to derive creativity evaluation results. Finally, leveraging a dynamic adjustment mechanism for bidirectional crossover mutation probabilities, the diversity of the antibody population is optimized through a clonal selection strategy, enhancing the model's adaptability to diverse artistic styles. Experimental results demonstrate that the proposed improved AIA achieves significant enhancements in evaluation metrics, with average Precision and Recall values increasing by 8.45% and 11.21%, respectively, compared to the baseline AIA. This study concludes that the AIA-integrated creativity evaluation method effectively improves the objectivity and accuracy of artistic assessments, offering a novel perspective for the intelligent evaluation of artworks.

关键词： Creative evaluation artificial immune algorithm artistic creation clonal selection mechanism feedback and iteration

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artificial immune algorithm-based credit evaluation for mobile telephone customers

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2015年第9期66卷 1533-1541页

作者： Zong-Chang, Yang Hong, Kuang Ji-Sheng, Xu Hong, Sun Hunan Univ Sci & Technol Xiangtan 411201 Peoples R China Wuhan Univ Wuhan 430072 Peoples R China

The arrearage problem is a critical concern for China's mobile communication services industry. Analysis of customer credit evaluation provides this study with a potential viable solution to the arrearage problem in China. By employing an artificial immune algorithm (AIA), a measure of customer credit based on customer attributes is proposed. This method was applied to one China mobile communication services company with approximately 400 000 customers yielding satisfying results. Utilizing traditional predictive accuracy and alternative metrics, performance comparisons of the proposed AIA were made using the feed-forward back propagation artificial neural network and the logistic regression model. A debision tree analysis of anticipated benefits was performed and indicates workability of the proposed method based on customer credit evaluation.

关键词： mobile communication customer credit evaluation artificial immune algorithm analysis of expected benefit

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artificial immune algorithm Convergence Rate Estimation Based on Stochastic Process Theory 30

Artificial Immune Algorithm Convergence Rate Estimation Base...

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30th Chinese Control and Decision Conference (CCDC)

作者： Lu, Hong Yu, Chu Wen, Xiao Huaihai Inst Technol Dept Elect Engn Lianyungang 222005 Jiangsu Peoples R China Marine Resources Dev Inst Jiangsu Lianyungang 222005 Jiangsu Peoples R China Monash Univ Sch Informat Technol Caulfield Vic 3145 Australia

ISBN: (纸本)9781538612446

The stochastic process theory is utilized to replace the traditional eigenvalue estimation method of the state transition matrix so as to study the convergence rate of the real coded artificial immune algorithm. On the premise of satisfying the necessary condition for probability strong convergence of artificial immune algorithm, this method regards it as the sufficient condition for the general artificial immune algorithm and proposes a new method for estimating the exponential velocity probability strong convergence of real coded artificial immune algorithm. This method takes the final convergence of the optimum antibody in the population and avoids the deficiency of the conservative property of traditional estimation method. It can estimate the convergence property and convergence rate of a class of artificial immune algorithm. In the practical application of artificial immune algorithm, it has a certain theoretical reference value in optimizing the convergence rate.

关键词： artificial immune algorithm Convergence rate estimation Stochastic Process Probability strong convergence

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artificial immune algorithm Based Signal Reconstruction for Compressive Sensing

Artificial Immune Algorithm Based Signal Reconstruction for ...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Li, Dan Shi, Chunli Wang, Qiang Shen, Yi Wang, Yan Harbin Inst Technol Dept Control Sci & Engn Harbin 150006 Peoples R China China Elect Equipment Syst Engn Corp Shenyang Peoples R China

ISBN: (纸本)9781467363860

The core of compressive sensing, i.e., signal reconstruction, is a constraint of signal sparsity problem, which can be implemented by l(0) norm minimization. But l(0) norm minimization requires exhaustively listing all possibility of the original signals, which is an NP-hard problem to achieve difficultly by traditional algorithm. This paper proposes a signal reconstruction algorithm based on artificial immune algorithm, which can solve l(0) norm minimization directly. It has been proved through numerical simulations that performance of signal reconstruction and photo-acoustic image reconstruction based on the proposed method is superior to that of OMP algorithm.

关键词： Compressive sensing Signal reconstruction artificial immune algorithm

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artificial immune algorithm OF MULTICELLULAR GROUP AND ITS CONVERGENCE

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Journal of Pharmaceutical Analysis 2005年第2期17卷 23-27页

作者：罗印升李人厚张维玺 School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an 710049 China

Objective To find out more extrema simultaneously including global optimum and multiple local optima existed in multi-modal functions. Methods Germinal center is the generator and selector of high-affinity B cells, a multicellular group's artificial immune algorithm was proposed based on the germinal center reaction mechanism of natural immune systems. Main steps of the algorithm were given, including hyper-mutation, selection, memory, similarity suppression and recruitment of B cells and the convergence of it was proved. Results The algorithm has been tested to optimize various multi-modal functions, and the simulation results show that the artificial immune algorithm proposed here can find multiple extremum of these functions with lower computational cost. Conclusion The algorithm is valid and can converge on the satisfactory solution set D with probability 1 and approach to global solution and many local optimal solutions existed.

关键词： germinal center reaction B cell artificial immune algorithm multi-modal function

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Optimizing GPS-guidance transit route for cable crane collision avoidance using artificial immune algorithm

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GPS SOLUTIONS 2017年第2期21卷 823-834页

作者： Wu, Hao Yin, Ya Wang, Shijie Shi, Wenzhong Clarke, Keith C. Miao, Zelang Wuhan Univ Technol Sch Resources & Environm Engn Wuhan 430070 Peoples R China Hong Kong Polytec Univ Dept Land Surveying & Geoinformat Kowloon Hong Kong Peoples R China Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA

A cable crane collision accident usually causes serious damage to workers, equipment and materials. Owing to the emergence of global position system (GPS), some new collision avoidance and guidance systems have been developed and applied in many large-scale civil engineering projects. However, it is very difficult to find an optimal GPS-guidance transit route with safety and efficiency based on experience among complex three-dimensional working situations. Our objective is to make use of the artificial immune algorithm to optimize the GPS-guidance transit route for cable crane collision avoidance under two different working situations. A comprehensive evaluation function that merges the consideration of reliability, convenience and cost with respect to the GPS-guidance transit route has been established to assess the cable crane transit route. We used the engineering case of the Dagangshan hydropower station to test our algorithm. The results suggest that the artificial immune algorithm can effectively and stably determine the optimal route under a complex environment. The analysis of optimization performance indicates that the proposed optimization approach fully considers comprehensive performance. It reveals that the collision risk changes with the elevation of the cable crane within a three-dimensional space. These findings allow us to reasonably make use of the GPS-guidance function to achieve cable crane collision avoidance more efficiently.

关键词： Cable crane GPS-guidance Collision avoidance Route optimization artificial immune algorithm

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Parameter Estimation for a Capacitive Coupling Communication Channel Within a Metal Cabinet Based on a Modified artificial immune algorithm

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IEEE ACCESS 2021年 9卷 75683-75698页

作者： Lu, Liping Guo, Zhe Wang, Zhiqi Huang, Zhonghua Liu, Ming Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China

The channel model optimization algorithm plays a critical role in novel communication method research. Wireless sensor connections using capacitive coupling communication inside a metal cabinet such as spacecraft are an emerging communication technology. However, channel modeling along with optimization methods have not been systematically investigated. In this paper, a modified artificial immune algorithm (MAIA) was developed to optimize a few tens of model parameters for the capacitive coupling communication channel within a metal cabinet. The mathematical model of the communication channel was derived from the equivalent circuit model by analyzing the capacitive coupling electric field distribution. Unknown parameters in the model were optimally estimated by adopting MAIA with the objective of minimizing the root mean square error (RMSE) between the model computed data and simulation or experimental data. The proposed scheme enhanced the convergence performance by incorporating the artificial bee colony (ABC) algorithm, modifying the strategies of immune operations and introducing a similarity detection step. Validation results showed that the frequency response of the optimized model matched well with the simulation and experimental data, verifying the feasibility and robustness of the proposed MAIA. Compared with three other state-of-the-art ABC algorithms and three enhanced intelligent algorithms, it was demonstrated that the proposed algorithm performed better with respect to convergence speed and accuracy. The study provided a multiparameter channel model estimation solution for capacitive coupling communication within a metal cabinet research.

关键词： Integrated circuit modeling Couplings Metals Mathematical model Equivalent circuits Data models Computational modeling Channel model optimization parameter estimation artificial immune algorithm capacitive coupling sensor networks

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Application of an artificial immune algorithm on a statistical model of dam displacement

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2011年第10期62卷 3980-3986页

作者： Xi Guang-yong Yue Jian-ping Zhou Bao-xing Tang Pu Hohai Univ Sch Earth Sci & Engn Nanjing Peoples R China

Statistical analysis is a useful method for setting the whole string of dam displacement measures in mathematical expressions. A statistical model is usually obtained by the method of stepwise regression. The stepwise regressions based on the least square method have limitations such as the lack of stability of the set of selected variables and bias in the parameter estimates. However, the artificial immune algorithm (AIA) provides good performance as an optimization algorithm. This paper proposes an immune statistical model, which merges the statistical model and the immune algorithm together, to resolve the data analysis problems of dam horizontal crest upstream-downstream displacement. The stepwise regression model and immune statistical model have been compared, showing that the immune statistical model provide a higher degree of accuracy in predicting the future behavior of the dam. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Deformation monitoring Statistical model artificial immune algorithm immune statistical model

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A new convergence rate estimation of general artificial immune algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015年第6期28卷 2793-2800页

作者： Hong, Lu Kamruzzaman, Joarder Huaihai Inst Technol Sch Elect Engn Lianyungang 222005 Jiangsu Peoples R China Monash Univ Sch Informat Technol Clayton Vic 3800 Australia

artificial immune algorithm has been used widely and successfully in many computational optimization areas, but the theoretical research exploring the convergence rate characteristics of artificial immune algorithm is yet inadequate. In this paper, instead of the traditional eigenvalue estimation of state transition matrix, stochastic processes theory is introduced to study the convergence rate of general artificial immune algorithm. The method begins by analyzing the necessary condition for convergence of artificial immune algorithm and takes it as the sufficient condition for a class of general artificial immune algorithm. Through the definition of Markov chain convergence rate, a probability strong convergence rate estimation method of general artificial immune algorithm is proposed. This method is judged by the final convergence of the best antibody, which overcomes the conservative defect of traditional estimation methods. The simulation results show the correctness of the proposed estimation method, and the estimation method can be used to judge the convergence and convergence rate of a class of artificial immune algorithms. This research has a certain theoretical reference value to optimize the convergence rate in the practical application of artificial immune algorithm.

关键词： artificial immune algorithm clonal selection theory idiotypic immune network theory convergence rate estimation markov chain

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Enhancing network cluster synchronization capability based on artificial immune algorithm

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2019年第1期9卷 1页

作者： Chen, Tinggui Shi, Jiawen Yang, Jianjun Li, Gongfa Zhejiang Gongshang Univ Modern Business Res Ctr KRI Hangzhou Zhejiang Peoples R China Zhejiang Gongshang Univ Sch Management & E Business Hangzhou Zhejiang Peoples R China Univ North Georgia Dept Comp Sci & Informat Syst Oakwood GA USA Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China

With the deeper study on complex networks, more and more attention has been paid to the research on the cluster synchronization phenomena based on complex networks. In the real world, synchronization phenomena or cluster synchronous behaviors occur frequently, some of which may result in larger negative impacts to the society, such as cadmium rice event, while others bring significant economic benefits to the society, such as the synchronization of the propaganda for black Friday. Therefore, research on cluster synchronism has great values for theoretical study and social applications. Currently, the study of cluster synchronicity is focused on the solution of the synchronization threshold and the analysis of the synchronization phenomenon, etc. However, the optimization to enhance the synchronous evolutionary effect is rarely presented in literatures. To overcome these limitations of current work, we explore the optimization of network structure with artificial immune algorithms under the condition of a constant network scale and finally realize the promotion of synchronous evolution effect in this paper. Moreover, the relevant research results are applied to real cases. On one hand, for the positive synchronous behaviors, the network structure with good synchronization capability is created to achieve better synchronization. On the other hand, the connection between nodes and edges in the network is cut off to avoid the occurrence of negative synchronous behaviors.

关键词： Network structure Cluster synchronization Optimization and evolution artificial immune algorithm

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