A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency ...A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.展开更多
The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding t...The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.展开更多
The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The ...The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The key to the information acquisition of a bridge monitoring system is to obtain data that meets the health monitoring requirements of the bridge with a limited number of measurement points.To address this,a hybrid method based on multiple optimization criteria is proposed for optimal sensor placement(OSP).First,the minimum number of modes required for bridge monitoring is determined using the information entropy criterion(IE).Then,the number of measurement points is determined using a sequence method combined with the modal assurance criterion(MAC).Finally,the sensor placement is optimized using the generalized genetic algorithm(GGA)combined with double-structure encoding,and the optimization results are validated through finite element model analysis.The research results show that the hybrid method based on multiple optimization criteria can effectively determine the number of measurement points for bridge structures and optimize sensor placement,with a significant improvement in computational speed.展开更多
In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed init...In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.展开更多
The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is h...The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is highly expensive,we will develop genetic algorithms(GAs)to obtain heuristic solutions to the problem.In GAs,as the crossover is a very important process,the crossovermethods proposed for the traditional TSP could be adapted for the GTSP.The sequential constructive crossover(SCX)and three other operators are adapted to use in GAs to solve the GTSP.The effectiveness of GA using SCX is verified on some GTSP Library(GTSPLIB)instances first and then compared against GAs using the other crossover methods.The computational results show the success of the GA using SCX for this problem.Our proposed GA using SCX,and swap mutation could find average solutions whose average percentage of excesses fromthe best-known solutions is between 0.00 and 14.07 for our investigated instances.展开更多
This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal ...This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results.展开更多
This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity fa...This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.展开更多
To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit t...To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.展开更多
GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. B...GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. By use of coding betterment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for optimizing combination of units in thermal power plants is proposed. And through taking examples, test result are analyzed and compared with results of some different algorithms. Numerical results show available value for the unit commitment problem with examples.展开更多
This research proposes a synergistic meta-heuristic algorithm for solving the extreme operational complications of combined heat and power economic dispatch problem towards the advantageous economic outcomes on the co...This research proposes a synergistic meta-heuristic algorithm for solving the extreme operational complications of combined heat and power economic dispatch problem towards the advantageous economic outcomes on the cost of generation. The combined heat and power (CHP) is a system that provides electricity and thermal energy concurrently. For its extraordinary efficiency and significant emission reduction, it is considered a promising energy prospect. The broad application of combined heat and power units requires the joint dispatch of power and heating systems, in which the modelling of combined heat and power units plays a vital role. The present research employs the genetic optimization algorithm to evaluate the cost function, heat and power dispatch values encountered in a system with simple cycle cogeneration unit and quadratic cost function. The system was first modeled to determine the various parameters of combined heat and power units towards solving its economic dispatch problem directly. In order for modelling to be done, a general structure of combined heat and power must be defined. The test system considered consists of four units: two conventional power units, one combined heat and power unit and one heat-only unit. The algorithm was applied to test system while taking into account the power and heat units, bounds of the units and feasible operation region of cogeneration unit. Output decision variables of 4-unit test systems plus cost function from Genetic Algorithm (GA), was determined using appropriate codes. The proposed algorithm produced a well spread and diverse optimal solution and also converged reasonably to the actual optimal solution in 51 iterations. The result obtained compared favourably with that obtained with the direct solution algorithm discussed in a previous paper. We conclude that the genetic algorithm is quite efficient in dealing with non-convex and constrained combined heat and power economic dispatch problem.展开更多
The study on the application of Genetic Algorithms(GA) to numerical simulation has been carried out. The simulation with GA is aimed at to realize the operation optimization of the coal fired MHD generator channel. Th...The study on the application of Genetic Algorithms(GA) to numerical simulation has been carried out. The simulation with GA is aimed at to realize the operation optimization of the coal fired MHD generator channel. The computer program for this purpose has been developed. By simulating numerically the operation optimization of IEE’s 25MWt coal fired experimental MHD generator, the feasibility of the application of GA procedure to the MHD power generation field has been verified.展开更多
This paper investigates the effects of various parameters on the terminal voltage and frequency of self excited induction generator using genetic algorithm. The parameters considered are speed, capacitance, leakage re...This paper investigates the effects of various parameters on the terminal voltage and frequency of self excited induction generator using genetic algorithm. The parameters considered are speed, capacitance, leakage reactance, stator and rotor resistances. Simulated results obtained using genetic algorithm facilitates in exploring the performance of self-excited induction generator. The paper henceforth establishes the application of user friendly genetic algorithm for studying the behaviour of self-excited induction.展开更多
Random numbers generated by pseudo-random and true random number generators (TRNG) are used in a wide variety of important applications. A TRNG relies on a non-deterministic source to sample random numbers. In this pa...Random numbers generated by pseudo-random and true random number generators (TRNG) are used in a wide variety of important applications. A TRNG relies on a non-deterministic source to sample random numbers. In this paper, we improve the post-processing stage of TRNGs using a heuristic evolutionary algorithm. Our post-processing algorithm decomposes the problem of improving the quality of random numbers into two phases: (i) Exact Histogram Equalization: it modifies the random numbers distribution with a specified output distribution;(ii) Stationarity Enforcement: using genetic algorithms, the output of (ii) is permuted until the random numbers meet wide-sense stationarity. We ensure that the quality of the numbers generated from the genetic algorithm is within a specified level of error defined by the user. We parallelize the genetic algorithm for improved performance. The post-processing is based on the power spectral density of the generated numbers used as a metric. We propose guideline parameters for the evolutionary algorithm to ensure fast convergence, within the first 100 generations, with a standard deviation over the specified quality level of less than 0.45. We also include a TestU01 evaluation over the random numbers generated.展开更多
To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering th...To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering the safety distance of pipes and the directional constraints at terminals, the definition of a double coding technique, the collision detection method, the concept of energy and the definition of fitness functions. The similarity detection is introduced to prevent close breeding in the crossover operator, the selection pressure is controlled according to the evolution situation and a heuristic mutation method is used to boost the evolution. Simulation case shows that this approach is more practical and can satisfy different design requirements by changing algorithm parameters.展开更多
This document elaborates on the generic implementation one of the main heuristics algorithms verified through its quick application to a biology problem requiring to find out an optimal sequences tree topology. In ord...This document elaborates on the generic implementation one of the main heuristics algorithms verified through its quick application to a biology problem requiring to find out an optimal sequences tree topology. In order to solve this problem, categorized as Non-Polynomial Hard (NP-Hard), “to minimize differences between given (leaf) and/or derived (parent) sequences”, many popular methods are used. “The higher the number of given sequences is, the more advisable and efficient it would be to go towards heuristics as they would provide a close-enough solution faster, as for instance genetic algorithms amongst others do. Thus, as part of a larger research in Heuristics and phylogenies, this paper aims to suggest a generic advanced flexible implementation of the Genetic Algorithm verified by a “general way to encode the problem into instances of different heuristic algorithms” as mentioned in our first reference below. The proposed algorithm will also present a chronology traceability feature for further analysis and potential improvements.展开更多
This paper describes an Inductive method with gnnetic search which learns attribute based phraserllle of natural laguage from set of preclassified examples. Every example is described with some attributes/values. This...This paper describes an Inductive method with gnnetic search which learns attribute based phraserllle of natural laguage from set of preclassified examples. Every example is described with some attributes/values. This algorithm takes an example as a seed, generalizes it by genetic process, and makes it cover as many examples as possible. We use genetic operator in population to perform a probabilistic parallel search in rule space and it will reduce greatly possibe rule search space compared with many other inductive methods. In this paper, we give description of attribute, word, dictionary and rule at first. then we describe learning algoritm and genetic search Proctess, and at last, we give a computing method abour quility of roule C(r).展开更多
This paper deals with the target-fault-oriented test generation of sequential circuits using genetic algorithms. We adopted the concept of multiple phases and proposed four sub-procedures which consist of activation, ...This paper deals with the target-fault-oriented test generation of sequential circuits using genetic algorithms. We adopted the concept of multiple phases and proposed four sub-procedures which consist of activation, propagation and justification phases. The paper focuses on the design of genetic operators and construction of fitness functions which are based on the structure information of circuits. Using ISCAS89 benchmarks, the experiment results of GA were given.展开更多
A novel interoperability test sequences optimization scheme is proposed in which the genetic algorithm （GA） is used to obtain the minimal-length interoperability test sequences. During our work, the basic interopera...A novel interoperability test sequences optimization scheme is proposed in which the genetic algorithm （GA） is used to obtain the minimal-length interoperability test sequences. During our work, the basic interoperability test sequences are generated based on the minimal-complete-coverage criterion, which removes the redundancy from conformance test sequences. Then interoperability sequences minimization problem can be considered as an instance of the set covering problem, and the GA is applied to remove redundancy in interoperability transitions. The results show that compared to conventional algorithm, the proposed algorithm is more practical to avoid the state space explosion problem, for it can reduce the length of the test sequences and maintain the same transition coverage.展开更多
Genome assembly is a prerequisite step for analyzing next generation sequencing data and also far from being solved. Many assembly tools have been proposed and used extensively. Majority of them aim to assemble sequen...Genome assembly is a prerequisite step for analyzing next generation sequencing data and also far from being solved. Many assembly tools have been proposed and used extensively. Majority of them aim to assemble sequencing reads into contigs; however, we focus on the assembly of contigs into scaffolds in this paper. This is called scaffolding, which estimates the relative order of the contigs as well as the size of the gaps between these contigs. Pheromone trail-based genetic algorithm （PGA） was previously proposed and had decent performance according to their paper. From our previous study, we found that family competition mechanism in genetic algorithm is able to further improve the results. Therefore, we propose family competition pheromone genetic algorithm （FCPGA） and demonstrate the improvement over PGA.展开更多
This work proposes a scheme which helps digitizing hand printed and electronic planar objects or vectorizing the generic shapes. An evolutionary optimization technique namely Genetic Algorithm (GA) is used to solve th...This work proposes a scheme which helps digitizing hand printed and electronic planar objects or vectorizing the generic shapes. An evolutionary optimization technique namely Genetic Algorithm (GA) is used to solve the problem of curve fitting with a cubic spline function. GA works well for finding the optimal values of shape parameters in the description of the proposed cubic spline. The underlying scheme comprises of various phases including data of the image outlines, detection of corner points, using GA for optimal values of shape parameters, and fitting curve using cubic spline to the detected corner points.展开更多
基金Supported by the Natural Science Foundation of Jiangsu Province (No.BK2004016).
文摘A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on gen- eralized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA com- posed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables. Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is sim- pler than conventional algorithms when it comes to hardware implementation. Moreover, it proc- esses only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.
文摘The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era,and actively responding to climate change policy.Through the analysis of the application of the generalized regression neural network(GRNN)in prediction,this paper improved the prediction method of GRNN.Genetic algorithm(GA)was adopted to search the optimal smooth factor as the only factor of GRNN,which was then used for prediction in GRNN.During the prediction of carbon dioxide emissions using the improved method,the increments of data were taken into account.The target values were obtained after the calculation of the predicted results.Finally,compared with the results of GRNN,the improved method realized higher prediction accuracy.It thus offers a new way of predicting total carbon dioxide emissions,and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.
文摘The increase in bridge structure span and the complex stress characteristics directly affect the optimization of sensor placement,which in turn influences the data acquisition performance of the monitoring system.The key to the information acquisition of a bridge monitoring system is to obtain data that meets the health monitoring requirements of the bridge with a limited number of measurement points.To address this,a hybrid method based on multiple optimization criteria is proposed for optimal sensor placement(OSP).First,the minimum number of modes required for bridge monitoring is determined using the information entropy criterion(IE).Then,the number of measurement points is determined using a sequence method combined with the modal assurance criterion(MAC).Finally,the sensor placement is optimized using the generalized genetic algorithm(GGA)combined with double-structure encoding,and the optimization results are validated through finite element model analysis.The research results show that the hybrid method based on multiple optimization criteria can effectively determine the number of measurement points for bridge structures and optimize sensor placement,with a significant improvement in computational speed.
文摘In order to solve the problem between searching performance and convergence of genetic algorithms, a fast genetic algorithm generalized self-adaptive genetic algorithm (GSAGA) is presented. (1) Evenly distributed initial population is generated. (2) Superior individuals are not broken because of crossover and mutation operation for they are sent to subgeneration directly. (3) High quality im- migrants are introduced according to the condition of the population schema. (4) Crossover and mutation are operated on self-adaptation. Therefore, GSAGA solves the coordination problem between convergence and searching performance. In GSAGA, the searching per- formance and global convergence are greatly improved compared with many existing genetic algorithms. Through simulation, the val- idity of this modified genetic algorithm is proved.
基金the Deanship of Scientific Research,Imam Mohammad Ibn Saud Islamic University(IMSIU),Saudi Arabia,for funding this research work through Grant No.(221412020).
文摘The generalized travelling salesman problem(GTSP),a generalization of the well-known travelling salesman problem(TSP),is considered for our study.Since the GTSP is NP-hard and very complex,finding exact solutions is highly expensive,we will develop genetic algorithms(GAs)to obtain heuristic solutions to the problem.In GAs,as the crossover is a very important process,the crossovermethods proposed for the traditional TSP could be adapted for the GTSP.The sequential constructive crossover(SCX)and three other operators are adapted to use in GAs to solve the GTSP.The effectiveness of GA using SCX is verified on some GTSP Library(GTSPLIB)instances first and then compared against GAs using the other crossover methods.The computational results show the success of the GA using SCX for this problem.Our proposed GA using SCX,and swap mutation could find average solutions whose average percentage of excesses fromthe best-known solutions is between 0.00 and 14.07 for our investigated instances.
文摘This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results.
文摘This paper introduces an optimized planning approach for integrating photovoltaic as distributed generation (PV-DG) into the radial distribution power systems, utilizing exhaustive load flow (ELF), loss sensitivity factor (LSF), genetic algorithms (GA) methods, and numerical method based on LSF. The methodology aims to determine the optimal allocation and sizing of multiple PV-DG to minimize power loss through time series power flow analysis. An approach utilizing continuous sensitivity analysis is developed and inherently leverages power flow and loss equations to compute LSF of all buses in the system towards employing a dynamic PV-DG model for more accurate results. The algorithm uses a numerical grid search method to optimize PV-DG placement in a power distribution system, focusing on minimizing system losses. It combines iterative analysis, sensitivity assessment, and comprehensive visualization to identify and present the optimal PV-DG configurations. The present-ed algorithms are verified through co-simulation framework combining MATLAB and OpenDSS to carry out analysis for 12-bus radial distribution test system. The proposed numerical method is compared with other algorithms, such as ELF, LSF methods, and Genetic Algorithms (GA). Results show that the proposed numerical method performs well in comparison with LSF and ELF solutions.
基金This work was supported by National Natural Science Foundation of China (NSFC) under the grant !No. 69873030
文摘To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.
基金Supported by the Natural Science Foundation of Hubei Province (2006ABA222)
文摘GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. By use of coding betterment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for optimizing combination of units in thermal power plants is proposed. And through taking examples, test result are analyzed and compared with results of some different algorithms. Numerical results show available value for the unit commitment problem with examples.
文摘This research proposes a synergistic meta-heuristic algorithm for solving the extreme operational complications of combined heat and power economic dispatch problem towards the advantageous economic outcomes on the cost of generation. The combined heat and power (CHP) is a system that provides electricity and thermal energy concurrently. For its extraordinary efficiency and significant emission reduction, it is considered a promising energy prospect. The broad application of combined heat and power units requires the joint dispatch of power and heating systems, in which the modelling of combined heat and power units plays a vital role. The present research employs the genetic optimization algorithm to evaluate the cost function, heat and power dispatch values encountered in a system with simple cycle cogeneration unit and quadratic cost function. The system was first modeled to determine the various parameters of combined heat and power units towards solving its economic dispatch problem directly. In order for modelling to be done, a general structure of combined heat and power must be defined. The test system considered consists of four units: two conventional power units, one combined heat and power unit and one heat-only unit. The algorithm was applied to test system while taking into account the power and heat units, bounds of the units and feasible operation region of cogeneration unit. Output decision variables of 4-unit test systems plus cost function from Genetic Algorithm (GA), was determined using appropriate codes. The proposed algorithm produced a well spread and diverse optimal solution and also converged reasonably to the actual optimal solution in 51 iterations. The result obtained compared favourably with that obtained with the direct solution algorithm discussed in a previous paper. We conclude that the genetic algorithm is quite efficient in dealing with non-convex and constrained combined heat and power economic dispatch problem.
文摘The study on the application of Genetic Algorithms(GA) to numerical simulation has been carried out. The simulation with GA is aimed at to realize the operation optimization of the coal fired MHD generator channel. The computer program for this purpose has been developed. By simulating numerically the operation optimization of IEE’s 25MWt coal fired experimental MHD generator, the feasibility of the application of GA procedure to the MHD power generation field has been verified.
文摘This paper investigates the effects of various parameters on the terminal voltage and frequency of self excited induction generator using genetic algorithm. The parameters considered are speed, capacitance, leakage reactance, stator and rotor resistances. Simulated results obtained using genetic algorithm facilitates in exploring the performance of self-excited induction generator. The paper henceforth establishes the application of user friendly genetic algorithm for studying the behaviour of self-excited induction.
文摘Random numbers generated by pseudo-random and true random number generators (TRNG) are used in a wide variety of important applications. A TRNG relies on a non-deterministic source to sample random numbers. In this paper, we improve the post-processing stage of TRNGs using a heuristic evolutionary algorithm. Our post-processing algorithm decomposes the problem of improving the quality of random numbers into two phases: (i) Exact Histogram Equalization: it modifies the random numbers distribution with a specified output distribution;(ii) Stationarity Enforcement: using genetic algorithms, the output of (ii) is permuted until the random numbers meet wide-sense stationarity. We ensure that the quality of the numbers generated from the genetic algorithm is within a specified level of error defined by the user. We parallelize the genetic algorithm for improved performance. The post-processing is based on the power spectral density of the generated numbers used as a metric. We propose guideline parameters for the evolutionary algorithm to ensure fast convergence, within the first 100 generations, with a standard deviation over the specified quality level of less than 0.45. We also include a TestU01 evaluation over the random numbers generated.
基金Supported by National "863" Project of China (2006AA09A104)
文摘To solve the problem of low efficiency in pipe routing design, an improved genetic algorithm based approach is proposed. To present this approach, the paper mainly describes a generation method of nodes considering the safety distance of pipes and the directional constraints at terminals, the definition of a double coding technique, the collision detection method, the concept of energy and the definition of fitness functions. The similarity detection is introduced to prevent close breeding in the crossover operator, the selection pressure is controlled according to the evolution situation and a heuristic mutation method is used to boost the evolution. Simulation case shows that this approach is more practical and can satisfy different design requirements by changing algorithm parameters.
文摘This document elaborates on the generic implementation one of the main heuristics algorithms verified through its quick application to a biology problem requiring to find out an optimal sequences tree topology. In order to solve this problem, categorized as Non-Polynomial Hard (NP-Hard), “to minimize differences between given (leaf) and/or derived (parent) sequences”, many popular methods are used. “The higher the number of given sequences is, the more advisable and efficient it would be to go towards heuristics as they would provide a close-enough solution faster, as for instance genetic algorithms amongst others do. Thus, as part of a larger research in Heuristics and phylogenies, this paper aims to suggest a generic advanced flexible implementation of the Genetic Algorithm verified by a “general way to encode the problem into instances of different heuristic algorithms” as mentioned in our first reference below. The proposed algorithm will also present a chronology traceability feature for further analysis and potential improvements.
文摘This paper describes an Inductive method with gnnetic search which learns attribute based phraserllle of natural laguage from set of preclassified examples. Every example is described with some attributes/values. This algorithm takes an example as a seed, generalizes it by genetic process, and makes it cover as many examples as possible. We use genetic operator in population to perform a probabilistic parallel search in rule space and it will reduce greatly possibe rule search space compared with many other inductive methods. In this paper, we give description of attribute, word, dictionary and rule at first. then we describe learning algoritm and genetic search Proctess, and at last, we give a computing method abour quility of roule C(r).
文摘This paper deals with the target-fault-oriented test generation of sequential circuits using genetic algorithms. We adopted the concept of multiple phases and proposed four sub-procedures which consist of activation, propagation and justification phases. The paper focuses on the design of genetic operators and construction of fitness functions which are based on the structure information of circuits. Using ISCAS89 benchmarks, the experiment results of GA were given.
文摘A novel interoperability test sequences optimization scheme is proposed in which the genetic algorithm （GA） is used to obtain the minimal-length interoperability test sequences. During our work, the basic interoperability test sequences are generated based on the minimal-complete-coverage criterion, which removes the redundancy from conformance test sequences. Then interoperability sequences minimization problem can be considered as an instance of the set covering problem, and the GA is applied to remove redundancy in interoperability transitions. The results show that compared to conventional algorithm, the proposed algorithm is more practical to avoid the state space explosion problem, for it can reduce the length of the test sequences and maintain the same transition coverage.
文摘Genome assembly is a prerequisite step for analyzing next generation sequencing data and also far from being solved. Many assembly tools have been proposed and used extensively. Majority of them aim to assemble sequencing reads into contigs; however, we focus on the assembly of contigs into scaffolds in this paper. This is called scaffolding, which estimates the relative order of the contigs as well as the size of the gaps between these contigs. Pheromone trail-based genetic algorithm （PGA） was previously proposed and had decent performance according to their paper. From our previous study, we found that family competition mechanism in genetic algorithm is able to further improve the results. Therefore, we propose family competition pheromone genetic algorithm （FCPGA） and demonstrate the improvement over PGA.
文摘This work proposes a scheme which helps digitizing hand printed and electronic planar objects or vectorizing the generic shapes. An evolutionary optimization technique namely Genetic Algorithm (GA) is used to solve the problem of curve fitting with a cubic spline function. GA works well for finding the optimal values of shape parameters in the description of the proposed cubic spline. The underlying scheme comprises of various phases including data of the image outlines, detection of corner points, using GA for optimal values of shape parameters, and fitting curve using cubic spline to the detected corner points.
