Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing ...Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.展开更多
This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives an...This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.展开更多
To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane...To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.展开更多
To solve the flight control problem for unmanned hypersonic vehicles,a novel intelligent optimized control method is proposed.A flight control system based on integral separated proportional-integral-derivative(PID)co...To solve the flight control problem for unmanned hypersonic vehicles,a novel intelligent optimized control method is proposed.A flight control system based on integral separated proportional-integral-derivative(PID)control is designed for hypersonic vehicle,and an improved shuffled frog leaping algorithm is presented to optimize the control parameters.A nonlinear model of hypersonic vehicle is established to examine the dynamic characteristics achieved by the flight control system.Simulation results demonstrate that the proposed optimized controller can effectively achieve better flight control performance than the traditional controller.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 the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem...In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.展开更多
This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler form...This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.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
Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subject...Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.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
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
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
As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that a...As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that at least one machine is not eligible for at least one job.PBPMSP and scheduling problems with machine eligibility are frequently considered;however,PBPMSP with machine eligibility is seldom explored.This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition(CSFLA)to minimize makespan.In CSFLA,the initial population is produced in a heuristic and random way,and the competitive search of memeplexes comprises two phases.Competition between any two memeplexes is done in the first phase,then iteration times are adjusted based on competition,and search strategies are adjusted adaptively based on the evolution quality of memeplexes in the second phase.An adaptive population shuffling is given.Computational experiments are conducted on 100 instances.The computational results showed that the new strategies of CSFLA are effective and that CSFLA has promising advantages in solving the considered PBPMSP.展开更多
DNA shuffling是蛋白质定向进化的一种常用策略,其优点是可以快速积累多突变效果,但同时由于突变数较多,其中真正发挥作用的突变及其结构基础往往不清楚。β-葡萄糖苷酶是纤维素高效降解的限速酶,良好的热稳定性是影响其实际催化效率的...DNA shuffling是蛋白质定向进化的一种常用策略,其优点是可以快速积累多突变效果,但同时由于突变数较多,其中真正发挥作用的突变及其结构基础往往不清楚。β-葡萄糖苷酶是纤维素高效降解的限速酶,良好的热稳定性是影响其实际催化效率的关键因素。该研究以DNA shuffling策略产生的热稳定性β-葡萄糖苷酶突变体Bgl3-6511(含60个突变,T_(50)值比野生型提高4.6℃)为研究对象,通过序列比对、定点突变和热稳定性测定,对其中的有益突变进行鉴定。结果显示,6个单点突变Y50F、R52H、R56K、V65I、T67A和P143A分别将该酶的T_(50)值提高2.9、4.2、1.5、2.8、3.2、1.2℃。同时,鉴定到5个有害突变将该酶的T_(50)值降低1.0~3.4℃。将获得单点有益突变进行组合,获得T_(50)值提高13.4℃的M6(Y50F/R52H/R56K/V65I/T67A/P143A),说明DNA shuffling策略积累的有害突变确实损害了性能优化。结构分析和分子动力学模拟显示,有益突变主要是通过增强分子内氢键、π-π键和稳定二级结构发挥作用。该研究对Bgl3-6511中的单点有益突变进行鉴定,对其结构基础进行了分析,并获得了热稳定性更加优良的突变酶,相关信息可为其他酶的分子改造提供有益借鉴。展开更多
Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was impro...Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing（SA）,Immune Vaccination（Iv）,Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis（LDA） Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM（SFLA-SVM）method,Particle Swarm Optimization algorithm optimization-SVM（PSo-SVM） method,basic SVM,Gaussian Mixture Model（GMM）method and Back Propagation（BP）neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.展开更多
文摘Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.
基金supported by the National Key Research and Development Program of China(Grant No.2016YFC0401702)the Fundamental Research Funds for the Central Universities(Grant No.2018B11214)the National Natural Science Foundation of China(Grants No.51379059 and 51579002)
文摘This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.
基金supported by the National Natural Science Foundation of China (61102106,61102105)the Fundamental Research Funds for the Central Universities (HEUCF100801,HEUCFZ1129)
文摘To solve discrete optimization difficulty of the spectrum allocation problem,a membrane-inspired quantum shuffled frog leaping（MQSFL） algorithm is proposed.The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm,which is an effective discrete optimization algorithm.Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems.By hybridizing the quantum frog colony optimization and membrane computing,the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure.The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time.Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.
基金supported in part by the National Natural Science Foundation of China(No.61304223)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20123218120015)the Fundamental Research Funds for the Central Universities(No.NZ2015206)
文摘To solve the flight control problem for unmanned hypersonic vehicles,a novel intelligent optimized control method is proposed.A flight control system based on integral separated proportional-integral-derivative(PID)control is designed for hypersonic vehicle,and an improved shuffled frog leaping algorithm is presented to optimize the control parameters.A nonlinear model of hypersonic vehicle is established to examine the dynamic characteristics achieved by the flight control system.Simulation results demonstrate that the proposed optimized controller can effectively achieve better flight control performance than the traditional controller.
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.
文摘In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.
文摘This work presents a memetic Shuffled Frog Leaping Algorithm(SFLA)based tuning approach of an Integral Sliding Mode Controller(ISMC)for a quadrotor type of Unmanned Aerial Vehicles(UAV).Based on the Newton–Euler formalism,a nonlinear dynamic model of the studied quadrotor is firstly established for control design purposes.Since the main parameters of the ISMC design are the gains of the sliding surfaces and signum functions of the switching control law,which are usually selected by repetitive and time-consuming trials-errors based procedures,a constrained optimization problem is formulated for the systematically tuning of these unknown variables.Under time-domain operating constraints,such an optimization-based tuning problem is effectively solved using the proposed SFLA metaheuristic with an empirical comparison to other evolutionary computation-and swarm intelligence-based algorithms such as the Crow Search Algorithm(CSA),Fractional Particle Swarm Optimization Memetic Algorithm(FPSOMA),Ant Bee Colony(ABC)and Harmony Search Algorithm(HSA).Numerical experiments are carried out for various sets of algorithms’parameters to achieve optimal gains of the sliding mode controllers for the altitude and attitude dynamics stabilization.Comparative studies revealed that the SFLA is a competitive and easily implemented algorithm with high performance in terms of robustness and non-premature convergence.Demonstrative results verified that the proposed metaheuristicsbased approach is a promising alternative for the systematic tuning of the effective design parameters in the integral sliding mode control framework.
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Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.
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.
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.
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supported by the National Natural Science Foundation of China(Grant Number 61573264).
文摘As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that at least one machine is not eligible for at least one job.PBPMSP and scheduling problems with machine eligibility are frequently considered;however,PBPMSP with machine eligibility is seldom explored.This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition(CSFLA)to minimize makespan.In CSFLA,the initial population is produced in a heuristic and random way,and the competitive search of memeplexes comprises two phases.Competition between any two memeplexes is done in the first phase,then iteration times are adjusted based on competition,and search strategies are adjusted adaptively based on the evolution quality of memeplexes in the second phase.An adaptive population shuffling is given.Computational experiments are conducted on 100 instances.The computational results showed that the new strategies of CSFLA are effective and that CSFLA has promising advantages in solving the considered PBPMSP.
基金supported by the National Nature Science Foundation(61231002,61273266,51075068)the Doctoral Fund of Ministry of Education of China(20110092130004)+1 种基金the Postdoctoral Fund of Ministry of Education of China(2012M520973)the Open Research Foundation of Key Laboratory(B) of Underwater Acoustic Signal Processing of Ministry of Education of Southeast University under Grant(UASP1202)
文摘Due to the drawbacks in Support Vector Machine（SVM）parameter optimization,an Improved Shuffled Frog Leaping Algorithm（Im-SFLA）was proposed,and the learning ability in practical speech emotion recognition was improved.Firstly,we introduced Simulated Annealing（SA）,Immune Vaccination（Iv）,Gaussian mutation and chaotic disturbance into the basic SFLA,which bManced the search efficiency and population diversity effectively.Secondly,Im-SFLA Was applied to the optimization of SVM parameters,and an Im-SFLA-SVM method Was proposed.Thirdly,the acoustic features of practical speech emotion,such aS ridgetiness,were analyzed.The pitch frequency,short-term energy,formant frequency and chaotic characteristics were analyzed corresponding to different emotion categories,and we constructed a 144-dimensional emotion feature vector for recognition and reduced to 4-dimension by adopting Linear Discriminant Analysis（LDA） Finally,the Im-SFLA-SVM method Was tested on the practical speech emotion database,and the recognition results were compared with Shuffled Frog Leaping Algorithm optimization-SVM（SFLA-SVM）method,Particle Swarm Optimization algorithm optimization-SVM（PSo-SVM） method,basic SVM,Gaussian Mixture Model（GMM）method and Back Propagation（BP）neural network method.The experimentM resuits showed that the average recognition rate of Im-SFLA-SVM method was 77.8%,which had improved 1.7%,2.7%,3.4%,4.7%and 7.8%respectively,compared with the other methods.The recognition of fidgetiness was significantly improve,thus verifying that Im-SFLA was an effective SVM parameter selection method,and the Im-SFLA-SVM method may significantly improve the practical speech emotion recognition.
