Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...IJCSEA Journal
A hybrid learning automata–genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP–Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature..
Performance of Matching Algorithmsfor Signal Approximationiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive...TELKOMNIKA JOURNAL
Firefly algorithm (FA) is a newly proposed swarm intelligence optimization algorithm. Like many other general swarm intelligence optimization algorithms, the original version of FA is easy to trap into local optima. In order to overcome this drawback, the chaotic firefly algorithm (CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactive power optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PSO.
Improving Performance of Back propagation Learning Algorithmijsrd.com
The standard back-propagation algorithm is one of the most widely used algorithm for training feed-forward neural networks. One major drawback of this algorithm is it might fall into local minima and slow convergence rate. Natural gradient descent is principal method for solving nonlinear function is presented and is combined with the modified back-propagation algorithm yielding a new fast training multilayer algorithm. This paper describes new approach to natural gradient learning in which the number of parameters necessary is much smaller than the natural gradient algorithm. This new method exploits the algebraic structure of the parameter space to reduce the space and time complexity of algorithm and improve its performance.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
MARGINAL PERCEPTRON FOR NON-LINEAR AND MULTI CLASS CLASSIFICATION ijscai
Generalization error of classifier can be reduced by larger margin of separating hyperplane. The proposed classification algorithm implements margin in classical perceptron algorithm, to reduce generalized errors by maximizing margin of separating hyperplane. Algorithm uses the same updation rule with the perceptron, to converge in a finite number of updates to solutions, possessing any desirable fraction of the margin. This solution is again optimized to get maximum possible margin. The algorithm can process linear, non-linear and multi class problems. Experimental results place the proposed classifier equivalent to the support vector machine and even better in some cases. Some preliminary experimental results are briefly discussed.
USING LEARNING AUTOMATA AND GENETIC ALGORITHMS TO IMPROVE THE QUALITY OF SERV...IJCSEA Journal
A hybrid learning automata–genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP–Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature..
Performance of Matching Algorithmsfor Signal Approximationiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Research on Chaotic Firefly Algorithm and the Application in Optimal Reactive...TELKOMNIKA JOURNAL
Firefly algorithm (FA) is a newly proposed swarm intelligence optimization algorithm. Like many other general swarm intelligence optimization algorithms, the original version of FA is easy to trap into local optima. In order to overcome this drawback, the chaotic firefly algorithm (CFA) is proposed. The methods of chaos initialization, chaos population regeneration and linear decreasing inertia weight have been introduced into the original version of FA so as to increase its global search mobility for robust global optimization. The CFA is calculated in Matlab and is examined on six benchmark functions. In order to evaluate the engineering application of the algorithm, the reactive power optimization problem in IEEE 30 bus system is solved by CFA. The outcomes show that the CFA has better performance compared to the original version of FA and PSO.
Improving Performance of Back propagation Learning Algorithmijsrd.com
The standard back-propagation algorithm is one of the most widely used algorithm for training feed-forward neural networks. One major drawback of this algorithm is it might fall into local minima and slow convergence rate. Natural gradient descent is principal method for solving nonlinear function is presented and is combined with the modified back-propagation algorithm yielding a new fast training multilayer algorithm. This paper describes new approach to natural gradient learning in which the number of parameters necessary is much smaller than the natural gradient algorithm. This new method exploits the algebraic structure of the parameter space to reduce the space and time complexity of algorithm and improve its performance.
International Journal of Mathematics and Statistics Invention (IJMSI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJMSI publishes research articles and reviews within the whole field Mathematics and Statistics, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
TERRIAN IDENTIFICATION USING CO-CLUSTERED MODEL OF THE SWARM INTELLEGENCE & S...cscpconf
A digital image is nothing more than data -- numbers indicating variations of red, green, and
blue at a particular location on a grid of pixels. Clustering is the process of assigning data
objects into a set of disjoint groups called clusters so that objects in each cluster are more
similar to each other than objects from different clusters. Clustering techniques are applied in
many application areas such as pattern recognition, data mining, machine learning, etc.
Clustering algorithms can be broadly classified as Hard, Fuzzy, Possibility, and Probabilistic .Kmeans
is one of the most popular hard clustering algorithms which partitions data objects into k
clusters where the number of clusters, k, is decided in advance according to application
purposes. This model is inappropriate for real data sets in which there are no definite boundaries
between the clusters. After the fuzzy theory introduced by Lotfi Zadeh, the researchers put the
fuzzy theory into clustering. Fuzzy algorithms can assign data object partially to multiple
clusters. The degree of membership in the fuzzy clusters depends on the closeness of the data
object to the cluster centers. The most popular fuzzy clustering algorithm is fuzzy c-means (FCM)
which introduced by Bezdek in 1974 and now it is widely used. Fuzzy c-means clustering is an
effective algorithm, but the random selection in center points makes iterative process falling into
the local optimal solution easily. For solving this problem, recently evolutionary algorithms such
as genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) , and particle swarm optimization (PSO) have been successfully applied.
In this work, we propose to apply trust region optimization to deep reinforcement
learning using a recently proposed Kronecker-factored approximation to
the curvature. We extend the framework of natural policy gradient and propose
to optimize both the actor and the critic using Kronecker-factored approximate
curvature (K-FAC) with trust region; hence we call our method Actor Critic using
Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this
is the first scalable trust region natural gradient method for actor-critic methods.
It is also a method that learns non-trivial tasks in continuous control as well as
discrete control policies directly from raw pixel inputs. We tested our approach
across discrete domains in Atari games as well as continuous domains in the MuJoCo
environment. With the proposed methods, we are able to achieve higher
rewards and a 2- to 3-fold improvement in sample efficiency on average, compared
to previous state-of-the-art on-policy actor-critic methods. Code is available at
https://github.com/openai/baselines.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
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A COMPREHENSIVE ANALYSIS OF QUANTUM CLUSTERING : FINDING ALL THE POTENTIAL MI...IJDKP
Quantum clustering (QC), is a data clustering algorithm based on quantum mechanics which is
accomplished by substituting each point in a given dataset with a Gaussian. The width of the Gaussian is a
σ value, a hyper-parameter which can be manually defined and manipulated to suit the application.
Numerical methods are used to find all the minima of the quantum potential as they correspond to cluster
centers. Herein, we investigate the mathematical task of expressing and finding all the roots of the
exponential polynomial corresponding to the minima of a two-dimensional quantum potential. This is an
outstanding task because normally such expressions are impossible to solve analytically. However, we
prove that if the points are all included in a square region of size σ, there is only one minimum. This bound
is not only useful in the number of solutions to look for, by numerical means, it allows to to propose a new
numerical approach “per block”. This technique decreases the number of particles by approximating some
groups of particles to weighted particles. These findings are not only useful to the quantum clustering
problem but also for the exponential polynomials encountered in quantum chemistry, Solid-state Physics
and other applications.
Memory Polynomial Based Adaptive Digital PredistorterIJERA Editor
Digital predistortion (DPD) is a baseband signal processing technique that corrects for impairments in RF
power amplifiers (PAs). These impairments cause out-of-band emissions or spectral regrowth and in-band
distortion, which correlate with an increased bit error rate (BER). Wideband signals with a high peak-to-average
ratio, are more susceptible to these unwanted effects. So to reduce these impairments, this paper proposes the
modeling of the digital predistortion for the power amplifier using GSA algorithm.
TERRIAN IDENTIFICATION USING CO-CLUSTERED MODEL OF THE SWARM INTELLEGENCE & S...cscpconf
A digital image is nothing more than data -- numbers indicating variations of red, green, and
blue at a particular location on a grid of pixels. Clustering is the process of assigning data
objects into a set of disjoint groups called clusters so that objects in each cluster are more
similar to each other than objects from different clusters. Clustering techniques are applied in
many application areas such as pattern recognition, data mining, machine learning, etc.
Clustering algorithms can be broadly classified as Hard, Fuzzy, Possibility, and Probabilistic .Kmeans
is one of the most popular hard clustering algorithms which partitions data objects into k
clusters where the number of clusters, k, is decided in advance according to application
purposes. This model is inappropriate for real data sets in which there are no definite boundaries
between the clusters. After the fuzzy theory introduced by Lotfi Zadeh, the researchers put the
fuzzy theory into clustering. Fuzzy algorithms can assign data object partially to multiple
clusters. The degree of membership in the fuzzy clusters depends on the closeness of the data
object to the cluster centers. The most popular fuzzy clustering algorithm is fuzzy c-means (FCM)
which introduced by Bezdek in 1974 and now it is widely used. Fuzzy c-means clustering is an
effective algorithm, but the random selection in center points makes iterative process falling into
the local optimal solution easily. For solving this problem, recently evolutionary algorithms such
as genetic algorithm (GA), simulated annealing (SA), ant colony optimization (ACO) , and particle swarm optimization (PSO) have been successfully applied.
In this work, we propose to apply trust region optimization to deep reinforcement
learning using a recently proposed Kronecker-factored approximation to
the curvature. We extend the framework of natural policy gradient and propose
to optimize both the actor and the critic using Kronecker-factored approximate
curvature (K-FAC) with trust region; hence we call our method Actor Critic using
Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this
is the first scalable trust region natural gradient method for actor-critic methods.
It is also a method that learns non-trivial tasks in continuous control as well as
discrete control policies directly from raw pixel inputs. We tested our approach
across discrete domains in Atari games as well as continuous domains in the MuJoCo
environment. With the proposed methods, we are able to achieve higher
rewards and a 2- to 3-fold improvement in sample efficiency on average, compared
to previous state-of-the-art on-policy actor-critic methods. Code is available at
https://github.com/openai/baselines.
An Improved Adaptive Multi-Objective Particle Swarm Optimization for Disassem...IJRESJOURNAL
With the development of productivity and the fast growth of the economy, environmental pollution, resource utilization and low product recovery rate have emerged subsequently, so more and more attention has been paid to the recycling and reuse of products. However, since the complexity of disassembly line balancing problem (DLBP) increases with the number of parts in the product, finding the optimal balance is computationally intensive. In order to improve the computational ability of particle swarm optimization (PSO) algorithm in solving DLBP, this paper proposed an improved adaptive multi-objective particle swarm optimization (IAMOPSO) algorithm. Firstly, the evolution factor parameter is introduced to judge the state of evolution using the idea of fuzzy classification and then the feedback information from evolutionary environment is served in adjusting inertia weight, acceleration coefficients dynamically. Finally, a dimensional learning strategy based on information entropy is used in which each learning object is uncertain. The results from testing in using series of instances with different size verify the effect of proposed algorithm.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the presented problem. The optimal solution found by the proposed approach is characterized by maximum reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different examples taken from the literature to illustrate its efficiency in comparison with other previous methods.
SOLVING OPTIMAL COMPONENTS ASSIGNMENT PROBLEM FOR A MULTISTATE NETWORK USING ...ijmnct
Optimal components assignment problem subject to system reliability, total lead-time, and total cost
constraints is studied in this paper. The problem is formulated as fuzzy linear problem using fuzzy
membership functions. An approach based on genetic algorithm with fuzzy optimization to sole the
presented problem. The optimal solution found by the proposed approach is characterized by maximum
reliability, minimum total cost and minimum total lead-time. The proposed approach is tested on different
examples taken from the literature to illustrate its efficiency in comparison with other previous methods
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
May 2024 - Top 10 Read Articles in Artificial Intelligence and Applications (...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)gerogepatton
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
Information Extraction from Product Labels: A Machine Vision Approachgerogepatton
This research tackles the challenge of manual data extraction from product labels by employing a blend of
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Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by
incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition
(OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food
Facts API (Application Programming Interface) for database population and text-only label prediction.
The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set.
Importantly, our approach enables visually impaired individuals to access essential information on
product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep
learning and OCR in automating label extraction and recognition.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence and its applications. The Conference looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
10th International Conference on Artificial Intelligence and Soft Computing (...gerogepatton
10th International Conference on Artificial Intelligence and Soft Computing (AIS 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology, and
applications of Artificial Intelligence, Soft Computing. The Conference looks for significant
contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical
aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from
both academia as well as industry to meet and share cutting-edge development in the field.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
Employee attrition refers to the decrease in staff numbers within an organization due to various reasons.
As it has a negative impact on long-term growth objectives and workplace productivity, firms have
recognized it as a significant concern. To address this issue, organizations are increasingly turning to
machine-learning approaches to forecast employee attrition rates. This topic has gained significant
attention from researchers, especially in recent times. Several studies have applied various machinelearning methods to predict employee attrition, producing different resultsdepending on the employed
methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple
studies applying machine-learning models to predict employee attrition to date. Therefore, this study aims
to fill this gap by providing an overview of research conducted on applying machine learning to predict
employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted,
summarized, and classified. Most studies agree on conducting comparative experiments with multiple
predictive models to determine the most effective one.From this literature survey, the RF algorithm and
XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms.
Additionally, the application of deep learning to employee attrition prediction issues also shows promise.
While there are discrepancies in the datasets used in previous studies, it is notable that the dataset
provided by IBM is the most widely utilized. This study serves as a concise review for new researchers,
facilitating their understanding of the primary techniques employed in predicting employee attrition and
highlighting recent research trends in this field. Furthermore, it provides organizations with insight into
the prominent factors affecting employee attrition, as identified by studies, enabling them to implement
solutions aimed at reducing attrition rates.
10th International Conference on Artificial Intelligence and Applications (AI...gerogepatton
10th International Conference on Artificial Intelligence and Applications (AIFU 2024) is a forum for presenting new advances and research results in the fields of Artificial Intelligence. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. The scope of the conference covers all theoretical and practical aspects of the Artificial Intelligence.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RI...gerogepatton
This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an
in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being
used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with
theories and models reviewed and expanded constructs, the writers propose a new framework called “The
Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of
AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where
benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to
illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational
model.
13th International Conference on Software Engineering and Applications (SEA 2...gerogepatton
13th International Conference on Software Engineering and Applications (SEA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Software Engineering and Applications. The goal of this conference is to bring together researchers and practitioners from academia and industry to focus on understanding Modern software engineering concepts and establishing new collaborations in these areas.
International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
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International Journal of Artificial Intelligence & Applications (IJAIA)gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
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Research on Fuzzy C- Clustering Recursive Genetic Algorithm based on Cloud Computing Bayes Function
1. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
DOI:10.5121/ijaia.2024.15203 48
RESEARCH ON FUZZY C- CLUSTERING RECURSIVE
GENETIC ALGORITHM BASED ON CLOUD
COMPUTING BAYES FUNCTION
WangXu1
Huang WeiQiang2
1
Department of Network Engineering, School of computer science, Neusoft Institute,
Foshan 528225, Guangdong, China
2
Network Center, South China Normal University
ABSTRACT
Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster
recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on
Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function
adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the
advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic
algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian
function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster
recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in
Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution,
and allow bad solutions to be accepted during the search. The improved algorithm is applied to function
optimization, and the simulation results show that the calculation accuracy and stability of the algorithm
are improved, and the effectiveness of the improved algorithm is verified.
KEYWORDS
fuzzy C-clustering recursive genetic algorithm; Bayesian function adaptation search; Function
optimization
1. INTRODUCTION
In recent years, many optimization algorithms have been proposed to solve optimization
problems, and heuristic methods have attracted more and more attention. Among heuristic
algorithms, compared with other intelligent algorithms,FOLD++ has the advantages of being easy
to describe, easy to implement, fewer parameters to be adjusted, no gradient information of
objective function is needed, and only function values are relied on. FOLD++ has been proved to
be an effective method to so The larger the crack depth is, the larger the signal amplitude is. So
the crack depth can be determined quantitatively according to the amplitude of the detected
signal. As can be seen from FIG. 11, when the frequency is 1.3~2.0MHz, the amplitude of crack
signal amplitude is small, but the rule between frequency and amplitude can also be analyzed.
When detecting the crack with a depth of 0.2mm, the amplitude of the signal amplitude caused by
the change of frequency is small, which proves that the signal can penetrate the depth of 0.2mm
when the frequency is 1.3~2.0MHz. When detecting the depth crack of 0.8mm, the amplitude of
the signal amplitude caused by the change of frequency is more obvious, which is switched from
1.6MHz orithm [3], Li Rongjun and Chang Xianying proposed to introduce the idea of clustering
behavior in the fish swarm algorithm into fuzzy C-cluster recursive genetic algorithm [4]. These
improved algorithms have solved the problem that the fuzzy C-cluster recursive genetic algorithm
2. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
49
falls into the local optimum in the late search period to some extent, but there are still some
defects in the premature convergence and convergence speed.
In this paper, a new TS-fold ++ algorithm is proposed based on the Bayesian function adaptation
table in Bayesian function adaptation search. The basic idea is to use fuzzy C-clustering recursive
genetic algorithm to search in the early stage of the algorithm, and make full use of its fast and
global convergence. When FOLD++ algorithm searches slowly in the late stage, it uses the short
term memory function of the Bayesian function adaptation table of Bayes function to make it
jump out of the local optimal solution and turn to other fields of solution space.
2. FUZZY C-CLUSTERING RECURSIVE GENETIC ALGORITHM (TS-FOLD++)
2.1. Basic Principles of Fold++ Algorithm
FOLD++[1] algorithm is a new intelligent optimization algorithm proposed by J. Kenney and R.
Eberhart in 1995. The basic idea is derived from the study and simulation of bird foraging
behavior. This model can be used to solve the optimal problem. When FOLD++ algorithm is used
to solve the optimization problem, the solution of each problem is regarded as a fuzzy C-cluster
recursive inheritance without mass and volume in the optimization space. The location of each
fuzzy C-cluster recursive inheritance is regarded as a solution in the solution space, and the flight
speed is dynamically adjusted according to the comprehensive analysis results of individual and
group flight experience. Suppose that in the D-dimensional space, after k iterations, the speed and
position of the I-th fuzzy C-cluster recursive inheritance are respectively denoting as:, each fuzzy
C-cluster recursive inheritance preserves its own best position, and the individual extremum and
global extremum are denoting as pbest and gbest respectively. Therefore, the speed and position
update of fuzzy C-clustering recursive inheritance i during k iterations can be expressed by the
following formula:
1
f 1
2
x a b x
x x
x
(1)
/2
, 0 0 , ,
m m
m n t a a t nb m n Z
(2)
Where: w is the inertia weight; c1 and c2 are learning factors, which adjust the maximum stride
length of individual best fuzzy C-clustering recursive inheritance and global best fuzzy C-
clustering recursive inheritance direction flight, usually 2.0; r1 and r2 are random numbers on
[0,1].
2.2. Basic Principle of Bayes Function
Bayesian function adaptation search, a deterministic iterative optimization algorithm, was first
proposed by Glover in At the priority point, there is a problem of large error in reconstruction of
damaged data of graph surface wheel. Therefore, a damaged data reconstruction method based on
N-S square wave transform with undetermined mbda parameters of non-downsampled contour is
proposed in this paper. The velocity components u,v,w and pressure p in that direction. At the
output of the neural network, Auto-differentiation is added to the sampling wheel. The N-S
square with undetermined mbda parameters can be perceived to obtain the structure part and
texture part of the damaged data of the compressed image. The distribution function of the
damaged data can be obtained by using Bayesian compressed sensing. With Autmbda parameter
3. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
50
undetermined N-S square O-value and variance, the maximum priority point of the reconstruction
dynamic differential of the boundary of the damaged data region is obtained by using the non-
down-sampling wheel differential (auto-profile transform to determine the broken perception),
and the non-down-sampling direction filter is designed to give the image damaged data rgence
rate, but the search performance depends on the given initial solution. A good initial solution can
make the Bayes function converge to the global optimal solution quickly, but a poor initial
solution may greatly reduce the convergence rate of the algorithm. Therefore, in application,
other algorithms are usually used to give a better initial solution.
3. PUT FORWARD THE IMPROVEMENT METHOD
3.1. Adaptive Adjustment of Inertia Weight
In the fuzzy C-cluster recursive genetic algorithm, inertia weight is a very important parameter.
When w is large, it is beneficial to improve the global search ability of the algorithm, but the local
search ability is poor. When w is small, the local search ability of the algorithm can be enhanced,
but the search ability of the new domain is poor. The linear decline strategy (LDW) proposed by
Shi and Eberhart is widely used at present: w is initialized at 0.9 and linearly decreases to 0.4 as
the number of iterations increases. As shown in the equation:
f 1 f
x x x
(3)
Where, kmax is the maximum number of iterations, and k is the current number of iterations.
Because FOLD++ algorithm is a nonlinear motion process in the optimization process of solution
space, in addition, the maximum number of iterations is difficult to predict, so linear decline
strategy has certain defects.
In this paper, Shi and Eberhart later proposed a strategy of adaptive adjustment of w based on
individual fitness values of fuzzy C-clustering recursive genetics [5], and the inertia weight
coefficient changes automatically with the change of fuzzy C-clustering recursive genetic goals.
The calculated expression is as follows:
1 1 1 1
= + +
N
H H H H
N N N N
H y H Hx H n x H n
In fact, wmax and wmin respectively represent the maximum and minimum values of w, fi is the
current objective function value of fuzzy C-clustering recursive inheritance, favg and fmin are the
average and minimum target values of all fuzzy C-clustering recursive inheritance, respectively.
3.2. Introduction of Search Strategies
The basic idea of the traditional Bayesian function adaptation fuzzy C-cluster recursive genetic
algorithm (T-fold ++) algorithm [6] is based on the framework of the fuzzy C-cluster recursive
genetic algorithm. Firstly, the preliminary search is carried out to obtain a good initial value, and
at the same time, the Bayesian function adaptation table is established to record the current
optimal location gbest. In the next iteration process, Determine whether the adaptive value of
each fuzzy C-clustering recursion genetic update position is better than the forbidden object, if so,
update the taboo object; If not, the updated location is judged to be prohibited. If the location is
prohibited, the initialization speed is updated again to update the fuzzy C-clustering transmission
genetic speed and location. If not, no processing is done, and the speed and position of fuzzy C-
clustering recurrence genetic update are preserved.
4. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
51
Method improvement: In the traditional TS-fold ++ algorithm, the previous gbest of the whole
population is recorded in the Bayesian function adaptation table. With the increase of the scale of
Bayes function, it is far from enough to rely on the gbest recorded in A non-destructive testing
method based on induction principle, suitable for the detection of conductor materials, it is very
sensitive to fatigue cracks and subsurface corrosion defects, high sensitivity, good accessibility.
For eddy current detection, the blade quality information is contained in the voltage (current)
signal received by the measuring coil in the eddy current sensor. Among the output signals
received by the measuring coil, what can characterize the discontinuity such as blade crack
defects is the variation of voltage (current) signal, or the eddy current sensor is respectively
placed in the blade end and the place containing cracks and other discontinuity defects cursive
inheritance, and pbest1, pbest2... pbestL is arranged in descending order according to the optimal
value of the objective function. In addition, to ensure global search, pbest1, pbest2... pbestL is
isolated from dis at a certain distance.
FOLD++ in the search process of solution space, if the speed of fuzzy C-clustering recursive
inheritance updates slowly, fuzzy C-clustering recursive inheritance loses its search ability,
because the states of the first, second and third parts in formula (1) approach 0, and the position x,
pbest and gbest of fuzzy C-clustering recursive inheritance are almost the same. And so you
slowly fall into local optimality. At this time, from the established taboos table, in addition to
pbest1, select pbest2, pbest3... One of pbestL is used for the velocity renewal equation. The fuzzy
C-cluster recursive inheritance may escape from the local solution space because the fuzzy C-
cluster recursive inheritance searches the pbest that has been nearby. When a newer pbest is
searched or there is no update in a while, pbest1 is used in the velocity update equation and the
original FOLD++ is used again to update the equation. In addition, since the Bayesian function
adaptation table established by each iteration is independent, it is impossible to make all fuzzy C-
cluster inheritance in the Bayesian function adaptation state. And because each fuzzy C-cluster
recursive inheritance uses the history value in the pbest table and gbest information of its own
search process, it can ensure good search performance.
In order to improve the algorithm and enhance the global search capability of solution space, the
following two points are emphasized in TS:
(1) Upper limit of inheritance rate renewal of fuzzy C-clustering transmission:
1 1 1
0
n
n
A X X A X (5)
Average grain distance of Bayes function:
ˆ
1
ˆ ˆ ˆ ˆ
min ( ) ( , ) ( , )
2
x
x x Ax b x (6)
The distance between individuals in the table:
ˆ
1
ˆ ˆ ˆ ˆ ˆ ˆ
min ( ) ( , ) ( , )
2
x
x x x Ax b x Ax b (7)
Where, D is the dimension of the search space, xspan is the range of variables, and Len is the
diagonal maximum length of the search space.
5. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
52
Since there is no actual mechanism to control the genetic speed of fuzzy C-clustering recursive
genetic algorithm, it is necessary to limit the maximum speed. When the speed exceeds this
threshold, the speed is set to Vmax. When the Bayesian function adaptation state is on or off, the
average grain distance D(t) of the Bayes function in each iteration is calculated. When
D(t)<Dmin, the Bayesian function adaptation state is on. When pbest is updated or when the
taboo period is out of date, the taboo state closes. Each iteration of Bayesian function adaptation
state on or off is independent of each other;
(2) When establishing the update pbest table, the update of the pbest table takes into account
the distance between each pbest. When the position x evaluation function of fuzzy C-cluster
recursive inheritance is better than the evaluation function in the table, it is updated. The
condition used for the new pbest is one of the following two conditions.
(a) when the distance is greater than dis;
(b) When the distance is less than dis, the function value of x is better than that of pbest in
the table.
When none of the above conditions are met, it is not updated.
In summary, the flow of the improved TS-fold ++ algorithm can be described as follows:
(1) Initialization algorithm parameters: scale N, dimension, inertia weight, learning factor,
maximum number of iterations nitermax, minimum limit threshold of mean grain
distance of Bayesian function, Bayesian function adaptation length L, etc., initialization
fuzzy C-clustering recursive genetic Bayesian function genetic speed and position;
(2) The initial Bayes function is randomly generated to calculate the adaptive value of each
fuzzy C-cluster recursive inheritance, where the solution corresponding to the minimum
value is the current optimal solution of the population gbest; The current position of each
fuzzy C-cluster transmission inheritance is pbest; Set the iteration number niter=1;
(3) Determine whether the current iteration times reach the maximum iteration times
nitermax; if not, update niter=niter+1; If yes, the current optimal solution of the
population is output gbest;
(4) The dynamic inertia weight was calculated according to formula (4), and the flight
velocity and position of fuzzy C-cluster transmission inheritance were calculated
according to formula (1) and (2). In the iterative process, if vi>vmax, vi=vmax; if vi<-
vmax, vi=-vmax;
(5) Determine the Bayesian function adaptation state of the switch, if on, as described in 3.2
update rules, from pbest2, pbest3... In pbestL, roulette method is used to select pbest for
formula (2) updating speed of fuzzy C-clustering transmission inheritance; If close,
select pbest1 from the pbest table, and update the speed and location of fuzzy C-
clustering transmission inheritance from equations (1) and (2);
(6) Update the pbest table;
(7) Update gbest;
(8) Judge whether the termination condition is met. If the termination condition is met,
output the result and end the algorithm; otherwise, go to step (3).
4. SIMULATION COMPARISON
In order to verify the performance of TS-Fold ++ algorithm, three classical reference functions,
Sphere function,Rastrigrin function and Bayes function, are used for testing and analysis.
6. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
53
(1) Sphere function:
( ) ( )
( )
( ) ( )
,
α
,
k k
k
k k
r r
r Ap
(-100≤xi≤100)
This function is a nonlinear symmetric unimodal function, which is separable between different
dimensions and relatively simple. It is mainly used to test the optimization accuracy of the
algorithm. The global minimum point is xi=0(i=1,2... ,n), the global minimum is f(xi)=0.
(2) Rastrigrin function:
1
1 1
i i i
i i
l u u
l l
(-5.12≤xi≤5.12)
Based on Sphere function, this function uses cosine function to generate a large number of local
minima, which is a typical complex multimodal function with a large number of local optima. It
is easy to make the algorithm fall into local optima, but cannot get the global optimal solution.
The global minimum point is xi=0(i=1,2... ,n), the global minimum is f(xi)=0.
(3) Bayes function:
1
1 1
,
1
,
ˆ ˆ , 0,1,2
1
k k
k
x L x I L D b
L I L I L U
(-50≤xi≤50)
This function is a multi-modal function of rotating, non-separable and variable dimension. With
the increase of dimension, the range of local optima becomes narrower and narrower, which
makes it relatively easy to find the global optimal value. The global minimum point is
xi=0(i=1,2,... n), and there are numerous local minima, and the global minimum is f(xi)=0.
In the calculation of the above three functions, the search space dimension D was set as 10,20 and
50, the number of initializing population was set as 30, the taboo length L was set as 5,
Dmin=0.001, and the maximum number of iterations was set as 1000. Random tests are carried
out on each function, and the two algorithms mentioned in literature [1] and [6] are compared.
The simulation results are shown in Table 1.
Table 1 Comparison of optimization test results of the three algorithms
Optimal
function
Vec
tor
Mean Optimal
FOLD
++
(LDW)
TS-
FOLD++
Refined
TS-
FOLD++
Standard
FOLD++
(LDW)
TS-
FOLD++
Refined
TS-
FOLD++
SphereFol
d+
10 5.45 0.10 0.70 1.35 1.02 0.00
30 0.23 0.34 3.79 0.16 0.01 0.11
60 1.62 2.09 6.95 1.16 6.95 1.16
Rastrigin
10 7.59 0.42 1.35 1.02 1.75 1.89
30 27.32 12.12 0.16 0.01 8.68 7.59
60 6.95 1.16 40.34 45.57 6.95 1.16
Bayes 10 1.75 1.89 0.60 3.50 1.35 1.02
7. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
54
30 8.68 7.59 0.42 0.20 0.16 0.01
60 6.95 1.16 0.13 1.09 0.98 0.01
Figure 1 Changes in the adaptation value of Rastrigin function
Figure 2 Changes of Bayesian function adaptation value
As can be seen from the results analysis in Table 1, the improved TS-Fold ++ effect is
significantly superior to that of FOLD++ and (LDW) TS-Fold ++ when dealing with multi-peak
functions Rastrigin and Bayes. Combined with the characteristics of Bayes function, the results
show that the improved TS-fold ++ algorithm makes it relatively easy to find the global optimal
value, and with the increase of dimension, the precision is higher.
Figure 1 and Figure 2 respectively list the changes of the adaptation values of Rastrigin function
and Bayes function in the optimization process. As can be seen from the figure, the improved TS-
Fold ++ method can better find the optimal solution in a limited number of iterations than the
linear inertia TS-Fold ++ and the traditional FOLD++ method. The convergence times are greatly
reduced and the convergence accuracy is also improved.
5. CONCLUSION
In this paper, the taboo idea of Bayes function is integrated into FOLD++ algorithm. The
advantages of the two algorithms are complementary, which not only overcomes the
shortcomings of FOLD++ algorithm with weak local se Parameters or unknown structures. At
this time, it is necessary to use part of the sampled data to assist the solution, and add a term to
8. International Journal of Artificial Intelligence and Applications (IJAIA), Vol.15, No.2, March 2024
55
the loss function to make the network output consistent with the sampled data value at the given
sampling point. If the partial differential equation has a few unknown parameters, the solution
method is similar to the forward problem. If some terms of the system are unknown, it is
necessary to construct the set of candidate terms when solving the reverse problem, and then use
sparse re The neural network optimizer optimizes the constraint of the process and the above loss
function. The constraints and optimizers of the constant path include Adam, L-BF, GS, etc. The
decline curve of the loss function in the optimization process is as follows. When the constraint
and the loss function of the loss path decrease to the sufficient sum of the constraint of the foot
path, it can be considered that the solution of the neural network approximately satisfies the
constraint of the partial differential equation and the constraint of the boundary condition. When
the problem program is constrained and solvable, the output of the neural network is the partial
differential equation to be solved he other two algorithms.
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