Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes...Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.展开更多
In order to improve the tracking performance in this paper following TBD(Track before Detection) framework multi-level crossover and matching operator is presented.In data association stage the greedy principle is ado...In order to improve the tracking performance in this paper following TBD(Track before Detection) framework multi-level crossover and matching operator is presented.In data association stage the greedy principle is adopted to handle time complexity in DPA and at the same time crossover mathing operator is given to construct candidate trajectory.In addition the corresponding strategy is introduced in preprocessing and postprocessing to remove clutter and suppress false alarm rate.By the experimental comparison and analysis it can be found that the method is more perfer to strengthen the tracking performance of targets with SNR < 2.0 dB.展开更多
Aiming at the problems of inaccurate edge segmentation,the hole phenomenon of segmenting large-scale targets,and the slow segmentation speed of printed circuit boards(PCB)in the image segmentation process,a PCB image ...Aiming at the problems of inaccurate edge segmentation,the hole phenomenon of segmenting large-scale targets,and the slow segmentation speed of printed circuit boards(PCB)in the image segmentation process,a PCB image segmentation model Mobile-Deep based on DeepLabv3+semantic segmentation framework is proposed.Firstly,the DeepLabv3+feature extraction network is replaced by the lightweight model MobileNetv2,which effectively reduces the number of model parameters;secondly,for the problem of positive and negative sample imbalance,a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve the model discriminative ability;in addition,a more efficient atrous spatial pyramid pooling(E-ASPP)module is proposed.In addition,a more efficient E-ASPP module is proposed,and the Roberts crossover operator is chosen to sharpen the image edges to improve the model accuracy;finally,the network structure is redesigned to further improve the model accuracy by drawing on the multi-scale feature fusion approach.The experimental results show that the proposed segmentation algorithm achieves an average intersection ratio of 93.45%,a precision of 94.87%,a recall of 93.65%,and a balance score of 93.64%on the PCB test set,which is more accurate than the common segmentation algorithms Hrnetv2,UNet,PSPNet,and PCBSegClassNet,and the segmentation speed is faster.展开更多
This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are...This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are objects based on domain ontologies. We define some genetic operators for objects, and discuss how to apply these genetic operators on objects to get new objects, which can satisfy new requirements.展开更多
The flowshop scheduling problem is NP complete. To solve it by genetic algorithm, an efficient crossover operator is designed. Compared with another crossover operator, this one often finds a better solution within th...The flowshop scheduling problem is NP complete. To solve it by genetic algorithm, an efficient crossover operator is designed. Compared with another crossover operator, this one often finds a better solution within the same time.展开更多
We have employed a recent implementation of genetic algorithms to study a range of standard benchmark functions for global optimization. It turns out that some of them are not very useful as challenging test functions...We have employed a recent implementation of genetic algorithms to study a range of standard benchmark functions for global optimization. It turns out that some of them are not very useful as challenging test functions, since they neither allow for a discrimination between different variants of genetic operators nor exhibit a dimensionality scaling resembling that of real-world problems, for example that of global structure optimization of atomic and molecular clusters. The latter properties seem to be simulated better by two other types of benchmark functions. One type is designed to be deceptive, exemplified here by Lunacek’s function. The other type offers additional advantages of markedly increased complexity and of broad tunability in search space characteristics. For the latter type, we use an implementation based on randomly distributed Gaussians. We advocate the use of the latter types of test functions for algorithm development and benchmarking.展开更多
The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancin...The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.展开更多
基金the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(Grant Number IMSIU-RP23030).
文摘Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.
基金Sponsored by the Young Talent Program of Fujian Province (Grant No.2007F3097)
文摘In order to improve the tracking performance in this paper following TBD(Track before Detection) framework multi-level crossover and matching operator is presented.In data association stage the greedy principle is adopted to handle time complexity in DPA and at the same time crossover mathing operator is given to construct candidate trajectory.In addition the corresponding strategy is introduced in preprocessing and postprocessing to remove clutter and suppress false alarm rate.By the experimental comparison and analysis it can be found that the method is more perfer to strengthen the tracking performance of targets with SNR < 2.0 dB.
基金funded by the University-Industry Cooperation Project“Research and Application of Intelligent Traveling Technology for Steel Logistics Based on Industrial Internet”,Grant Number 2022H6005Natural Science Foundation of Fujian Provincial Science and Technology Department,Grant Number 2022J01952Research Start-Up Projects,Grant Number GY-Z12079.
文摘Aiming at the problems of inaccurate edge segmentation,the hole phenomenon of segmenting large-scale targets,and the slow segmentation speed of printed circuit boards(PCB)in the image segmentation process,a PCB image segmentation model Mobile-Deep based on DeepLabv3+semantic segmentation framework is proposed.Firstly,the DeepLabv3+feature extraction network is replaced by the lightweight model MobileNetv2,which effectively reduces the number of model parameters;secondly,for the problem of positive and negative sample imbalance,a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve the model discriminative ability;in addition,a more efficient atrous spatial pyramid pooling(E-ASPP)module is proposed.In addition,a more efficient E-ASPP module is proposed,and the Roberts crossover operator is chosen to sharpen the image edges to improve the model accuracy;finally,the network structure is redesigned to further improve the model accuracy by drawing on the multi-scale feature fusion approach.The experimental results show that the proposed segmentation algorithm achieves an average intersection ratio of 93.45%,a precision of 94.87%,a recall of 93.65%,and a balance score of 93.64%on the PCB test set,which is more accurate than the common segmentation algorithms Hrnetv2,UNet,PSPNet,and PCBSegClassNet,and the segmentation speed is faster.
文摘This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are objects based on domain ontologies. We define some genetic operators for objects, and discuss how to apply these genetic operators on objects to get new objects, which can satisfy new requirements.
文摘The flowshop scheduling problem is NP complete. To solve it by genetic algorithm, an efficient crossover operator is designed. Compared with another crossover operator, this one often finds a better solution within the same time.
文摘We have employed a recent implementation of genetic algorithms to study a range of standard benchmark functions for global optimization. It turns out that some of them are not very useful as challenging test functions, since they neither allow for a discrimination between different variants of genetic operators nor exhibit a dimensionality scaling resembling that of real-world problems, for example that of global structure optimization of atomic and molecular clusters. The latter properties seem to be simulated better by two other types of benchmark functions. One type is designed to be deceptive, exemplified here by Lunacek’s function. The other type offers additional advantages of markedly increased complexity and of broad tunability in search space characteristics. For the latter type, we use an implementation based on randomly distributed Gaussians. We advocate the use of the latter types of test functions for algorithm development and benchmarking.
文摘The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.