At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear globa...At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear global optimization problem. In order to alleviate the risk of falling into a local optimal solution, this paper introduces a new global optimization method, the shuffle frog-leaping algorithm （SFLA）, into the Rayleigh wave dispersion-curve inversion process. SFLA is a swarm-intelligence-based algorithm that simulates a group of frogs searching for food. It uses a few parameters, achieves rapid convergence, and is capability of effective global searching. In order to test the reliability and calculation performance of SFLA, noise-free and noisy synthetic datasets were inverted. We conducted a comparative analysis with other established algorithms using the noise-free dataset, and then tested the ability of SFLA to cope with data noise. Finally, we inverted a real-world example to examine the applicability of SFLA. Results from both synthetic and field data demonstrated the effectiveness of SFLA in the interpretation of Rayleigh wave dispersion curves. We found that SFLA is superior to the established methods in terms of both reliability and computational efficiency, so it offers great potential to improve our ability to solve geophysical inversion problems.展开更多
The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-d...The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.展开更多
In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamm...In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.展开更多
Hybrid flow shop scheduling problem(HFSP)has been extensively considered,however,some reallife conditions are seldom investigated.In this study,HFsP with no precedence between some stages is solved and an adaptive shu...Hybrid flow shop scheduling problem(HFSP)has been extensively considered,however,some reallife conditions are seldom investigated.In this study,HFsP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm(ASFLA)is developed to optimize makespan.A new solution representation and a decoding procedure are presented,an adaptive memeplex search and dynamical population shuffling are implemented together.Many computational experiments are implemented.Computational results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.展开更多
In recent years,the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web.As a result,the use of techniques for extracting useful information from lar...In recent years,the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web.As a result,the use of techniques for extracting useful information from large collections of data,and particularly documents,has become more necessary and challenging.Text clustering is such a technique;it consists in dividing a set of text documents into clusters(groups),so that documents within the same cluster are closely related,whereas documents in different clusters are as different as possible.Clustering depends on measuring the content(i.e.,words)of a document in terms of relevance.Nevertheless,as documents usually contain a large number of words,some of them may be irrelevant to the topic under consideration or redundant.This can confuse and complicate the clustering process and make it less accurate.Accordingly,feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features.In this study,we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected features.The proposed approach is based on two metaheuristic algorithms:a genetic algorithm(GA)and a shuffled frog-leaping algorithm(SFLA).The GA performs feature selection,and the SFLA performs clustering.To evaluate its effectiveness,the proposed approach was tested on a well-known text document dataset:the“20Newsgroup”dataset from the University of California Irvine Machine Learning Repository.Overall,after multiple experiments were compared and analyzed,it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering,compared with classical K-means clustering.Nevertheless,this improvement requires longer computational time.展开更多
Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issu...Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.展开更多
基金supported by the National Natural Science Foundation of China(No.41374123)
文摘At present, near-surface shear wave velocities are mainly calculated through Rayleigh wave dispersion-curve inversions in engineering surface investigations, but the required calculations pose a highly nonlinear global optimization problem. In order to alleviate the risk of falling into a local optimal solution, this paper introduces a new global optimization method, the shuffle frog-leaping algorithm （SFLA）, into the Rayleigh wave dispersion-curve inversion process. SFLA is a swarm-intelligence-based algorithm that simulates a group of frogs searching for food. It uses a few parameters, achieves rapid convergence, and is capability of effective global searching. In order to test the reliability and calculation performance of SFLA, noise-free and noisy synthetic datasets were inverted. We conducted a comparative analysis with other established algorithms using the noise-free dataset, and then tested the ability of SFLA to cope with data noise. Finally, we inverted a real-world example to examine the applicability of SFLA. Results from both synthetic and field data demonstrated the effectiveness of SFLA in the interpretation of Rayleigh wave dispersion curves. We found that SFLA is superior to the established methods in terms of both reliability and computational efficiency, so it offers great potential to improve our ability to solve geophysical inversion problems.
基金supported by the National Natural Science Foundation of China(61673209,71971115)。
文摘The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.
基金the National Natural Science Foundation of China(No.42127807)Natural Science Foundation of Sichuan Province(Nos.23NSFSCC0116 and 2022NSFSC12333)the Nuclear Energy Development Project(No.[2021]-88).
文摘In airborne gamma ray spectrum processing,different analysis methods,technical requirements,analysis models,and calculation methods need to be established.To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency,an improved shuffled frog leaping algorithm-particle swarm optimization convolutional neural network(SFLA-PSO CNN)for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein.This method was used to train the weight of the neural network,optimize the structure of the network,delete redundant connections,and enable the neural network to acquire the capability of quantitative spectrum processing.In full-spectrum data processing,this method can perform the functions of energy spectrum peak searching and peak area calculations.After network training,the mean SNR and RMSE of the spectral lines were 31.27 and 2.75,respectively,satisfying the demand for noise reduction.To test the processing ability of the algorithm in large samples of airborne gamma spectra,this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis.The results show that calculation of the single-peak area takes only 0.13~0.15 ms,and the average relative errors of the peak area in the U,Th,and K spectra are 3.11,9.50,and 6.18%,indicating the high processing efficiency and accuracy of this algorithm.The performance of the model can be further improved by optimizing related parameters,but it can already meet the requirements of practical engineering measurement.This study provides a new idea for the full-spectrum processing of airborne gamma rays.
文摘Hybrid flow shop scheduling problem(HFSP)has been extensively considered,however,some reallife conditions are seldom investigated.In this study,HFsP with no precedence between some stages is solved and an adaptive shuffled frog-leaping algorithm(ASFLA)is developed to optimize makespan.A new solution representation and a decoding procedure are presented,an adaptive memeplex search and dynamical population shuffling are implemented together.Many computational experiments are implemented.Computational results prove that the new strategies of ASFLA are effective and ASFLA is very competitive in solving HFSP with no precedence between some stages.
基金This research was supported by a grant from the Research Center of the Center for Female Scientific and Medical Colleges Deanship of Scientific Research,King Saud University.
文摘In recent years,the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide Web.As a result,the use of techniques for extracting useful information from large collections of data,and particularly documents,has become more necessary and challenging.Text clustering is such a technique;it consists in dividing a set of text documents into clusters(groups),so that documents within the same cluster are closely related,whereas documents in different clusters are as different as possible.Clustering depends on measuring the content(i.e.,words)of a document in terms of relevance.Nevertheless,as documents usually contain a large number of words,some of them may be irrelevant to the topic under consideration or redundant.This can confuse and complicate the clustering process and make it less accurate.Accordingly,feature selection methods have been employed to reduce data dimensionality by selecting the most relevant features.In this study,we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected features.The proposed approach is based on two metaheuristic algorithms:a genetic algorithm(GA)and a shuffled frog-leaping algorithm(SFLA).The GA performs feature selection,and the SFLA performs clustering.To evaluate its effectiveness,the proposed approach was tested on a well-known text document dataset:the“20Newsgroup”dataset from the University of California Irvine Machine Learning Repository.Overall,after multiple experiments were compared and analyzed,it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering,compared with classical K-means clustering.Nevertheless,this improvement requires longer computational time.
基金supported by the Shenzhen Innovation Technology Program(JCYJ20160422112909302)
文摘Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.
