A Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-06-24
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
Identiﬁcation of bilinear systems using differential evolution algorithm
Saban Ozer; Hasan Zorlu
2011-06-01
In this work, a novel identiﬁcation method based on differential evolution algorithm has been applied to bilinear systems and its performance has been compared to that of genetic algorithm. Box–Jenkins system and different type bilinear systems have been identiﬁed using differential evolution and genetic algorithms. The simulation results have shown that bilinear systems can be successfully and efﬁciently identiﬁed using these algorithms.
Solving Partial Differential Equations Using a New Differential Evolution Algorithm
Natee Panagant
2014-01-01
Full Text Available This paper proposes an alternative meshless approach to solve partial differential equations (PDEs. With a global approximate function being defined, a partial differential equation problem is converted into an optimisation problem with equality constraints from PDE boundary conditions. An evolutionary algorithm (EA is employed to search for the optimum solution. For this approach, the most difficult task is the low convergence rate of EA which consequently results in poor PDE solution approximation. However, its attractiveness remains due to the nature of a soft computing technique in EA. The algorithm can be used to tackle almost any kind of optimisation problem with simple evolutionary operation, which means it is mathematically simpler to use. A new efficient differential evolution (DE is presented and used to solve a number of the partial differential equations. The results obtained are illustrated and compared with exact solutions. It is shown that the proposed method has a potential to be a future meshless tool provided that the search performance of EA is greatly enhanced.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Novel Feature Selection by Differential Evolution Algorithm
Ali Ghareaghaji
2013-11-01
Full Text Available Iris scan biometrics employs the unique characteristic and features of the human iris in order to verify the identity of in individual. In today's world, where terrorist attacks are on the rise employment of infallible security systems is a must. This makes Iris recognition systems unavoidable in emerging security. Authentication the objective function is minimized using Differential Evolutionary (DE Algorithm where the population vector is encoded using Binary Encoded Decimal to avoid the float number optimization problem. An automatic clustering of the possible values of the Lagrangian multiplier provides a detailed insight of the selected features during the proposed DE based optimization process. The classification accuracy of Support Vector Machine (SVM is used to measure the performance of the selected features. The proposed algorithm outperforms the existing DE based approaches when tested on IRIS, Wine, Wisconsin Breast Cancer, Sonar and Ionosphere datasets. The same algorithm when applied on gait based people identification, using skeleton data points obtained from Microsoft Kinect sensor, exceeds the previously reported accuracies.
Differential evolution algorithm for global optimizations in nuclear physics
Qi, Chong
2017-04-01
We explore the applicability of the differential evolution algorithm in finding the global minima of three typical nuclear structure physics problems: the global deformation minimum in the nuclear potential energy surface, the optimization of mass model parameters and the lowest eigenvalue of a nuclear Hamiltonian. The algorithm works very effectively and efficiently in identifying the minima in all problems we have tested. We also show that the algorithm can be parallelized in a straightforward way.
Improving Results of Differential Evolution Algorithm
2015-01-01
Optimisation problems are of prime importance in scientific and engineering communities. Many day-to-day tasks in these fields can be classified as optimisation problems. Due to their enormous solution spaces, optimisation problems frequently lie in class NP. In such cases, engineers and researchers have to rely on algorithms and techniques that can find sub-optimal solutions to these problems. One of the most dependable algorithms for numerical optimisation problems is Diff...
A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution
Lijin Wang
2015-01-01
Full Text Available The backtracking search optimization algorithm (BSA is a new nature-inspired method which possesses a memory to take advantage of experiences gained from previous generation to guide the population to the global optimum. BSA is capable of solving multimodal problems, but it slowly converges and poorly exploits solution. The differential evolution (DE algorithm is a robust evolutionary algorithm and has a fast convergence speed in the case of exploitive mutation strategies that utilize the information of the best solution found so far. In this paper, we propose a hybrid backtracking search optimization algorithm with differential evolution, called HBD. In HBD, DE with exploitive strategy is used to accelerate the convergence by optimizing one worse individual according to its probability at each iteration process. A suit of 28 benchmark functions are employed to verify the performance of HBD, and the results show the improvement in effectiveness and efficiency of hybridization of BSA and DE.
Real parameter optimization by an effective differential evolution algorithm
Ali Wagdy Mohamed
2013-03-01
Full Text Available This paper introduces an Effective Differential Evolution (EDE algorithm for solving real parameter optimization problems over continuous domain. The proposed algorithm proposes a new mutation rule based on the best and the worst individuals among the entire population of a particular generation. The mutation rule is combined with the basic mutation strategy through a linear decreasing probability rule. The proposed mutation rule is shown to promote local search capability of the basic DE and to make it faster. Furthermore, a random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme are merged to avoid stagnation and/or premature convergence. Additionally, the scaling factor and crossover of DE are introduced as uniform random numbers to enrich the search behavior and to enhance the diversity of the population. The effectiveness and benefits of the proposed modifications used in EDE has been experimentally investigated. Numerical experiments on a set of bound-constrained problems have shown that the new approach is efficient, effective and robust. The comparison results between the EDE and several classical differential evolution methods and state-of-the-art parameter adaptive differential evolution variants indicate that the proposed EDE algorithm is competitive with , and in some cases superior to, other algorithms in terms of final solution quality, efficiency, convergence rate, and robustness.
A DIFFERENTIAL EVOLUTION ALGORITHM DEVELOPED FOR A NURSE SCHEDULING PROBLEM
Shahnazari-Shahrezaei, P.
2012-11-01
Full Text Available Nurse scheduling is a type of manpower allocation problem that tries to satisfy hospital managers objectives and nurses preferences as much as possible by generating fair shift schedules. This paper presents a nurse scheduling problem based on a real case study, and proposes two meta-heuristics a differential evolution algorithm (DE and a greedy randomised adaptive search procedure (GRASP to solve it. To investigate the efficiency of the proposed algorithms, two problems are solved. Furthermore, some comparison metrics are applied to examine the reliability of the proposed algorithms. The computational results in this paper show that the proposed DE outperforms the GRASP.
Parameters Identification of Photovoltaic Cells Based on Differential Evolution Algorithm
Liao Hui
2016-01-01
Full Text Available For the complex nonlinear model of photovoltaic cells, traditional evolution strategy is easy to fall into the local optimal and its identification time is too long when taking parameters identification, then the difference algorithm is proposed in this study, which is to solve the problems of parameter identification in photovoltaic cell model, where it is very difficult to achieve with other identification algorithms. In this method, the random data is selected as the initial generation; the successful evolution to the next generation is done through a certain strategy of difference algorithm, which can achieve the effective identification of control parameters. It is proved that the method has a good global optimization and the fast convergence ability, and the simulation results are shown that the differential evolution has high identification ability and it is an effective method to identify the parameters of photovoltaic cells, where the photovoltaic cells can be widely used in other places with these parameters.
Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm
Saad Mohd Sazli
2016-01-01
Full Text Available This paper present the parameter identification of damped compound pendulum using differential evolution algorithm. The procedure used to achieve the parameter identification of the experimental system consisted of input output data collection, ARX model order selection and parameter estimation using conventional method least square (LS and differential evolution (DE algorithm. PRBS signal is used to be input signal to regulate the motor speed. Whereas, the output signal is taken from position sensor. Both, input and output data is used to estimate the parameter of the ARX model. The residual error between the actual and predicted output responses of the models is validated using mean squares error (MSE. Analysis showed that, MSE value for LS is 0.0026 and MSE value for DE is 3.6601×10-5. Based results obtained, it was found that DE have lower MSE than the LS method.
Differential Evolution algorithm applied to FSW model calibration
Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.
2014-03-01
Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.
Fan, Qinqin; Yan, Xuefeng
2016-01-01
The performance of the differential evolution (DE) algorithm is significantly affected by the choice of mutation strategies and control parameters. Maintaining the search capability of various control parameter combinations throughout the entire evolution process is also a key issue. A self-adaptive DE algorithm with zoning evolution of control parameters and adaptive mutation strategies is proposed in this paper. In the proposed algorithm, the mutation strategies are automatically adjusted with population evolution, and the control parameters evolve in their own zoning to self-adapt and discover near optimal values autonomously. The proposed algorithm is compared with five state-of-the-art DE algorithm variants according to a set of benchmark test functions. Furthermore, seven nonparametric statistical tests are implemented to analyze the experimental results. The results indicate that the overall performance of the proposed algorithm is better than those of the five existing improved algorithms.
An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies
Wan-li Xiang
2015-01-01
Full Text Available Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.
Enhanced differential evolution algorithm for solving reactive power problem
K. Lenin
2016-09-01
Full Text Available Differential evolution (DE is one of the efficient evolutionary computing techniques that seem to be effective to handle optimization problems in many practical applications. Conversely, the performance of DE is not always flawless to guarantee fast convergence to the global optimum. It can certainly get inaction resulting in low accuracy of acquired results. An enhanced differential evolution (EDE algorithm by integrating excited arbitrary confined search (EACS to augment the performance of a basic DE algorithm have been proposed in this paper. EACS is a local search method that is excited to swap the present solution by a superior candidate in the neighbourhood. Only a small subset of arbitrarily selected variables is used in each step of the local exploration for randomly deciding the subsequent provisional solution. The proposed EDE has been tested in standard IEEE 30 bus test system. The simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.
Application of differential evolution algorithm on self-potential data.
Xiangtao Li
Full Text Available Differential evolution (DE is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods.
Application of differential evolution algorithm on self-potential data.
Li, Xiangtao; Yin, Minghao
2012-01-01
Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods.
Optimal Reactive Power Dispatch using Improved Differential Evolution Algorithm
Hamid Falaghi
2014-12-01
Full Text Available Reactive power dispatch plays a key role in secure and economic operation of power systems. Optimal reactive power dispatch (ORPD is a non-linear optimization problem which includes both continues and discrete variables. Due to complex characteristics, heuristic and evolutionary based optimization approaches have become effective tools to solve the ORPD problem. In this paper, a new optimization approach based on improved differential evolution (IDE has been proposed to solve the ORPD problem. IDE is an improved version of differential evolution optimization algorithm in which new solutions are produced in respect to global best solution. In the proposed approach, IDE determines the optimal combination of control variables including generator voltages, transformer taps and setting of VAR compensation devices to obtain minimum real power losses. In order to demonstrate the applicability and efficiency of the proposed IDE based approach, it has been tested on the IEEE 14 and 57-bus test systems and obtained results are compared with those obtained using other existing methods. Simulation results show that the proposed approach is superior to the other existing methods.
Differential evolution and simulated annealing algorithms for mechanical systems design
H. Saruhan
2014-09-01
Full Text Available In this study, nature inspired algorithms – the Differential Evolution (DE and the Simulated Annealing (SA – are utilized to seek a global optimum solution for ball bearings link system assembly weight with constraints and mixed design variables. The Genetic Algorithm (GA and the Evolution Strategy (ES will be a reference for the examination and validation of the DE and the SA. The main purpose is to minimize the weight of an assembly system composed of a shaft and two ball bearings. Ball bearings link system is used extensively in many machinery applications. Among mechanical systems, designers pay great attention to the ball bearings link system because of its significant industrial importance. The problem is complex and a time consuming process due to mixed design variables and inequality constraints imposed on the objective function. The results showed that the DE and the SA performed and obtained convergence reliability on the global optimum solution. So the contribution of the DE and the SA application to the mechanical system design can be very useful in many real-world mechanical system design problems. Beside, the comparison confirms the effectiveness and the superiority of the DE over the others algorithms – the SA, the GA, and the ES – in terms of solution quality. The ball bearings link system assembly weight of 634,099 gr was obtained using the DE while 671,616 gr, 728213.8 gr, and 729445.5 gr were obtained using the SA, the ES, and the GA respectively.
Differential Evolution Algorithm for Route Optimization Problems of Engineering Networks
O. G. Monahov
2015-01-01
Full Text Available The paper considers problems of structure optimization of engineering networks to provide a minimum total cost of engineering networks in construction and operation. The mathematical statement of the problem in terms of the hyper-network theory takes into account the interdependence of indicators of hyper-network elements, a layout area and a projected network. A digital model of terrain presents the placement area of engineering networks (a territory. In our case, it will be a weighted mesh (graph of primary network of dedicated vertices-consumers and a vertex-source for the utilities. The edges weights will be determined by the costs of construction and operation of the route between the given vertices of the network. The initial solution of the problem of minimizing the total cost will be using the minimum spanning tree, obtained on a weighted complete graph the vertices of which are defined by vertices-consumers and the vertexsource for the utilities, and the weights of edges are the distance between the vertices on the given weighted graph of the primary network. The work offers a method of differential evolution to solve the problem in hyper-network formulation that improves the initial solution by the mapping the edges of the secondary network in the primary network using additional Steiner points. As numerical experiments have shown, a differential evolution algorithm allows us to reduce the average total cost for a given engineering network compared to the initial solution by 5% - 15%, depending on the configuration, parameters, and layout area.
Estimating Traffic Accidents in Turkey Using Differential Evolution Algorithm
Akgüngör, Ali Payıdar; Korkmaz, Ersin
2017-06-01
Estimating traffic accidents play a vital role to apply road safety procedures. This study proposes Differential Evolution Algorithm (DEA) models to estimate the number of accidents in Turkey. In the model development, population (P) and the number of vehicles (N) are selected as model parameters. Three model forms, linear, exponential and semi-quadratic models, are developed using DEA with the data covering from 2000 to 2014. Developed models are statistically compared to select the best fit model. The results of the DE models show that the linear model form is suitable to estimate the number of accidents. The statistics of this form is better than other forms in terms of performance criteria which are the Mean Absolute Percentage Errors (MAPE) and the Root Mean Square Errors (RMSE). To investigate the performance of linear DE model for future estimations, a ten-year period from 2015 to 2024 is considered. The results obtained from future estimations reveal the suitability of DE method for road safety applications.
Yongquan Zhou
2013-01-01
Full Text Available In view of the traditional numerical method to solve the nonlinear equations exist is sensitive to initial value and the higher accuracy of defects. This paper presents an invasive weed optimization (IWO algorithm which has population diversity with the heuristic global search of differential evolution (DE algorithm. In the iterative process, the global exploration ability of invasive weed optimization algorithm provides effective search area for differential evolution; at the same time, the heuristic search ability of differential evolution algorithm provides a reliable guide for invasive weed optimization. Based on the test of several typical nonlinear equations and a circle packing problem, the results show that the differential evolution invasive weed optimization (DEIWO algorithm has a higher accuracy and speed of convergence, which is an efficient and feasible algorithm for solving nonlinear systems of equations.
Hybridizing Differential Evolution with a Genetic Algorithm for Color Image Segmentation
R. V. V. Krishna
2016-10-01
Full Text Available This paper proposes a hybrid of differential evolution and genetic algorithms to solve the color image segmentation problem. Clustering based color image segmentation algorithms segment an image by clustering the features of color and texture, thereby obtaining accurate prototype cluster centers. In the proposed algorithm, the color features are obtained using the homogeneity model. A new texture feature named Power Law Descriptor (PLD which is a modification of Weber Local Descriptor (WLD is proposed and further used as a texture feature for clustering. Genetic algorithms are competent in handling binary variables, while differential evolution on the other hand is more efficient in handling real parameters. The obtained texture feature is binary in nature and the color feature is a real value, which suits very well the hybrid cluster center optimization problem in image segmentation. Thus in the proposed algorithm, the optimum texture feature centers are evolved using genetic algorithms, whereas the optimum color feature centers are evolved using differential evolution.
A hybrid differential evolution algorithm for meta-task scheduling in grids
Kang Qinma; Jiang Changjun; He Hong; Huang Qiangsheng
2009-01-01
Task scheduling is one of the core steps to effectively exploit the capabilities of heterogeneous resources in the grid. This paper presents a new hybrid differential evolution (HDE) algorithm for finding an optimal or near-optimal schedule within reasonable time. The encoding scheme and the adaptation of classical differential evolution algorithm for dealing with discrete variables are discussed. A simple but effective local search is incorporated into differential evolution to stress exploitation. The performance of the proposed HDE algorithm is showed by being compared with a genetic algorithm (GA) on a known static benchmark for the problem. Experimental results indicate that the proposed algorithm has better performance than GA in terms of both solution quality and computational time, and thus it can be used to design efficient dynamic schedulers in batch mode for real grid systems.
Özgür Başkan
2014-09-01
Full Text Available Differential Evolution algorithm has effectively been used to solve engineering optimization problems recently. The Differential Evolution algorithm, which uses similar principles with Genetic Algorithms, is more robust on obtaining optimal solution than many other heuristic algorithms with its simpler structure. In this study, Differential Evolution algorithm is applied to the transportation network design problems and its effectiveness on the solution is investigated. In this context, Differential Evolution based models are developed using bi-level programming approach for the solution of the transportation network design problem and determination of the on-street parking places in urban road networks. In these models, optimal investment and parking strategies are investigated on the upper level. On the lower level, deterministic traffic assignment problem, which represents drivers' responses, is solved using Frank-Wolfe algorithm and VISUM traffic modeling software. In order to determine the effectiveness of the proposed models, numerical applications are carried out on Sioux-Falls test network. Results showed that the Differential Evolution algorithm may effectively been used for the solution of transportation network design problems.
WANG Shundin; ZHANG Hua
2008-01-01
Using functional derivative technique In quantum field theory,the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations.The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by Introducing the time translation operator.The functional partial differential evolution equations were solved by algebraic dynam-ics.The algebraic dynamics solutions are analytical In Taylor series In terms of both initial functions and time.Based on the exact analytical solutions,a new nu-merical algorithm-algebraic dynamics algorithm was proposed for partial differ-ential evolution equations.The difficulty of and the way out for the algorithm were discussed.The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Self-adaptive learning based discrete differential evolution algorithm for solving CJWTA problem
Yu Xue; Yi Zhuang; Tianquan Ni; Siru Ni; Xuezhi Wen
2014-01-01
Cooperative jamming weapon-target assignment (CJWTA) problem is a key issue in electronic countermeasures (ECM). Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution (SLDDE) algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.
Zhu, Jun; Yan, Xuefeng; Zhao, Weixiang
2013-10-01
To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process. The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem.
2008-01-01
Using functional derivative technique in quantum field theory, the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations. The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by introducing the time translation operator. The functional partial differential evolution equations were solved by algebraic dynam-ics. The algebraic dynamics solutions are analytical in Taylor series in terms of both initial functions and time. Based on the exact analytical solutions, a new nu-merical algorithm—algebraic dynamics algorithm was proposed for partial differ-ential evolution equations. The difficulty of and the way out for the algorithm were discussed. The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Chuii Khim Chong
2012-06-01
Full Text Available This paper introduces an improved Differential Evolution algorithm (IDE which aims at improving its performance in estimating the relevant parameters for metabolic pathway data to simulate glycolysis pathway for yeast. Metabolic pathway data are expected to be of significant help in the development of efficient tools in kinetic modeling and parameter estimation platforms. Many computation algorithms face obstacles due to the noisy data and difficulty of the system in estimating myriad of parameters, and require longer computational time to estimate the relevant parameters. The proposed algorithm (IDE in this paper is a hybrid of a Differential Evolution algorithm (DE and a Kalman Filter (KF. The outcome of IDE is proven to be superior than Genetic Algorithm (GA and DE. The results of IDE from experiments show estimated optimal kinetic parameters values, shorter computation time and increased accuracy for simulated results compared with other estimation algorithms
Reconstruction of strain distribution in fiber Bragg grat-ings with differential evolution algorithm
WEN Xiao-yan; YU Qoan
2008-01-01
Differential evolution algorithm is used to solve the inverse problem of strain distribution in tibet Bragg grating (FBG).Linear and nonlinear strain profiles are reconstructed based on the reflection spectra. An approximate solution could beobtained within only 50 rounds of evolutions. Numerical examples show good agreements between target strain profilesand reconstructed ones. Online performance analysis illuminates the efficiency and practicality of differential evolutionalgorithm in solving the inverse problem of FBG.
XU Zhi-gao; GUAN Zheng-xi; MA Jing
2005-01-01
The differential evolution (DE) algorithm is applied to solving the models' equations of a whole missile power system, and the steady fault characteristics of the whole system are analyzed. The DE algorithm is robust, requires few control variables, is easy to use and lends itself very well to parallel computation. Calculation results indicate that the DE algorithm simulates faults of a missile power system very well.
Efficient differential evolution algorithms for multimodal optmal control problems
Lopez Cruz, I.L.; Willigenburg, van L.G.; Straten, van G.
2003-01-01
Many methods for solving optimal control problems, whether direct or indirect, rely upon gradient information and therefore may converge to a local optimum. Global optimisation methods like Evolutionary algorithms, overcome this problem. In this work it is investigated how well novel and easy to und
A hybrid differential evolution algorithm to vehicle routing problem with fuzzy demands
Erbao, Cao; Mingyong, Lai
2009-09-01
In this paper, the vehicle routing problem with fuzzy demands (VRPFD) is considered, and a fuzzy chance constrained program model is designed, based on fuzzy credibility theory. Then stochastic simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy chance constrained program model. Moreover, the influence of the dispatcher preference index on the final objective of the problem is discussed using stochastic simulation, and the best value of the dispatcher preference index is obtained.
Design of Short-Circuited Microstrip Antenna Using Differential Evolution Algorithm
Arindam Deb
2012-08-01
Full Text Available Differential evolution (DE algorithm is used to design a microstrip antenna, loaded with a shorting pin. The position of probe and the position of shorting pin are optimized using DE. The fitness function for DE is obtained using multiport network modelling technique. Antenna is fabricated and measured results are compared with the theoretical results.
Braak, ter C.J.F.
2004-01-01
Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likeli
Ali Wagdy Mohamed
2014-11-01
Full Text Available In this paper, a novel version of Differential Evolution (DE algorithm based on a couple of local search mutation and a restart mechanism for solving global numerical optimization problems over continuous space is presented. The proposed algorithm is named as Restart Differential Evolution algorithm with Local Search Mutation (RDEL. In RDEL, inspired by Particle Swarm Optimization (PSO, a novel local mutation rule based on the position of the best and the worst individuals among the entire population of a particular generation is introduced. The novel local mutation scheme is joined with the basic mutation rule through a linear decreasing function. The proposed local mutation scheme is proven to enhance local search tendency of the basic DE and speed up the convergence. Furthermore, a restart mechanism based on random mutation scheme and a modified Breeder Genetic Algorithm (BGA mutation scheme is combined to avoid stagnation and/or premature convergence. Additionally, an exponent increased crossover probability rule and a uniform scaling factors of DE are introduced to promote the diversity of the population and to improve the search process, respectively. The performance of RDEL is investigated and compared with basic differential evolution, and state-of-the-art parameter adaptive differential evolution variants. It is discovered that the proposed modifications significantly improve the performance of DE in terms of quality of solution, efficiency and robustness.
Mingolo, Nusharin; Sarakorn, Weerachai
2016-04-01
In this research, the Modified Differential Evolution (DE) algorithm is proposed and applied to the Magnetotelluric (MT) and Vertical Electrical sounding (VES) data to reveal the reasonable resistivity structure. The common processes of DE algorithm, including initialization, mutation and crossover, are modified by introducing both new control parameters and some constraints to obtain the fitting-reasonable resistivity model. The validity and efficiency of our developed modified DE algorithm is tested on both synthetic and real observed data. Our developed DE algorithm is also compared to the well-known OCCAM's algorithm for real case of MT data. For the synthetic case, our modified DE algorithm with appropriate control parameters can reveal the reasonable-fitting models when compared to the original synthetic models. For the real data case, the resistivity structures revealed by our algorithm are closed to those obtained by OCCAM's inversion, but our obtained structures reveal layers more apparently.
Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms
Xu Chen; Wenli Du; Feng Qian
2016-01-01
Dynamic optimization problems (DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are in-valid. In this article, a technology named ranking-based mutation operator (RMO) is presented to enhance the previous differential evolution (DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
Sukanta Nama
2016-04-01
Full Text Available Differential evolution (DE is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA is a new evolutionary algorithm (EA for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy.
Xinli Xu
2013-01-01
Full Text Available A two-level batch chromosome coding scheme is proposed to solve the lot splitting problem with equipment capacity constraints in flexible job shop scheduling, which includes a lot splitting chromosome and a lot scheduling chromosome. To balance global search and local exploration of the differential evolution algorithm, a hybrid discrete differential evolution algorithm (HDDE is presented, in which the local strategy with dynamic random searching based on the critical path and a random mutation operator is developed. The performance of HDDE was experimented with 14 benchmark problems and the practical dye vat scheduling problem. The simulation results showed that the proposed algorithm has the strong global search capability and can effectively solve the practical lot splitting problems with equipment capacity constraints.
Chong, Chuii Khim; Mohamad, Mohd Saberi; Deris, Safaai; Shamsir, Mohd Shahir; Abdullah, Afnizanfaizal
2014-01-01
This paper presents an Improved Differential Evolution (IDE) algorithm to improve the kinetic parameter estimation in simulating the glycolysis pathway and the threonine biosynthesis pathway. Experimentally derived time series kinetic data are noisy and possess many unknown parameters. These characteristics of kinetic data cause lengthy computational time to compute the optimum value of the kinetic parameters. To solve this problem, this study had been conducted to develop a hybrid method that combined the Differential Evolution algorithm (DE) and the Kalman Filter (KF) to produce IDE. Results have shown that lesser computation time (6% and 18.5% faster) and more robust to noisy data with significant reduced error rates (93% and 79% reduced error rates) compared with the Genetic Algorithm (GA) and DE, respectively, in glycolysis and threonine biosynthesis pathway simulations. IDE is reliable as it demonstrated consistent standard deviation values which were close to mean values. We foresee the applicability of IDE into other metabolic pathway simulations.
Reservoir Flood Control Operation Based on Adaptive Immune Differential Evolution Algorithm
Zou, Qiang; Lu, Jun; Yu, Shan
2017-05-01
Reservoir flood control operation (RFCO) is a high dimensional complex problem with multi-stages, multi-variables and multi-constraints, and its optimal solution is not easy to get. Differential evolution algorithm (DE) can be applied in RFCO, but its species diversity may sharply decline at the last evolution and lead into local optimal. Therefore, based on the adaptively controlling for mutation factor and crossover factor in each generation and immune clonal selection for better individuals, then adaptive immune differential evolution algorithm (AIDE) was proposed. And test function simulation verified the feasibility and efficiency of AIDE. Finally, AIDE was employed for RFCO and case study showed that AIDE could get better flood control benefit with fast convergence and high accuracy, moreover the outcomes of this research provided an effective way for RFCO.
Iwan Solihin, Mahmud; Fauzi Zanil, Mohd
2016-11-01
Cuckoo Search (CS) and Differential Evolution (DE) algorithms are considerably robust meta-heuristic algorithms to solve constrained optimization problems. In this study, the performance of CS and DE are compared in solving the constrained optimization problem from selected benchmark functions. Selection of the benchmark functions are based on active or inactive constraints and dimensionality of variables (i.e. number of solution variable). In addition, a specific constraint handling and stopping criterion technique are adopted in the optimization algorithm. The results show, CS approach outperforms DE in term of repeatability and the quality of the optimum solutions.
An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration
Wenping Ma
2014-01-01
Full Text Available We introduce an area-based method for remote sensing image registration. We use orthogonal learning differential evolution algorithm to optimize the similarity metric between the reference image and the target image. Many local and global methods have been used to achieve the optimal similarity metric in the last few years. Because remote sensing images are usually influenced by large distortions and high noise, local methods will fail in some cases. For this reason, global methods are often required. The orthogonal learning (OL strategy is efficient when searching in complex problem spaces. In addition, it can discover more useful information via orthogonal experimental design (OED. Differential evolution (DE is a heuristic algorithm. It has shown to be efficient in solving the remote sensing image registration problem. So orthogonal learning differential evolution algorithm (OLDE is efficient for many optimization problems. The OLDE method uses the OL strategy to guide the DE algorithm to discover more useful information. Experiments show that the OLDE method is more robust and efficient for registering remote sensing images.
Zhongbo Hu
2014-01-01
Full Text Available Many improved differential Evolution (DE algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithm in theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS operators. The parameter self-adaptation and uniform mutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011.
Umesh Kumar Rout
2013-09-01
Full Text Available This paper presents the design and performance analysis of Differential Evolution (DE algorithm based Proportional-Integral (PI controller for Automatic Generation Control (AGC of an interconnected power system. A two area non-reheat thermal system equipped with PI controllers which is widely used in literature is considered for the design and analysis purpose. The design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions using Integral Time multiply Absolute Error (ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients are derived in order to increase the performance of the controller. The superiority of the proposed DE optimized PI controller has been shown by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA and Genetic Algorithm (GA based PI controller for the same interconnected power system.
Yalin Wang
2013-01-01
Full Text Available The grinding-classification is the prerequisite process for full recovery of the nonrenewable minerals with both production quality and quantity objectives concerned. Its natural formulation is a constrained multiobjective optimization problem of complex expression since the process is composed of one grinding machine and two classification machines. In this paper, a hybrid differential evolution (DE algorithm with multi-population is proposed. Some infeasible solutions with better performance are allowed to be saved, and they participate randomly in the evolution. In order to exploit the meaningful infeasible solutions, a functionally partitioned multi-population mechanism is designed to find an optimal solution from all possible directions. Meanwhile, a simplex method for local search is inserted into the evolution process to enhance the searching strategy in the optimization process. Simulation results from the test of some benchmark problems indicate that the proposed algorithm tends to converge quickly and effectively to the Pareto frontier with better distribution. Finally, the proposed algorithm is applied to solve a multiobjective optimization model of a grinding and classification process. Based on the technique for order performance by similarity to ideal solution (TOPSIS, the satisfactory solution is obtained by using a decision-making method for multiple attributes.
Jian Wang
2014-01-01
Full Text Available A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality.
Maryjane TREMAYNE; Samantha Y. CHONG; Duncan BELL
2009-01-01
Evolutionary search and optimisation algorithms have been used successfully in many areas of materials science and chemistry. In recent years, these techniques have been applied to, and revolutionised the study of crystal structures from powder diffraction data. In this paper we present the application of a hybrid global optimisation technique,cultural differential evolution (CDE), to crystal structure determination from powder diffraction data. The combination of the principles of social evolution and biological evolution,through the pruning of the parameter search space shows significant improvement in the efficiency of the calculations over traditional dictates of biological evolution alone. Resuits are presented in which a range of algorithm control parameters, i.e., population size, mutation and recombination rates, extent of culture-based pruning are used to assess the performance of this hybrid technique. The effects of these control parameters on the speed and efficiency of the optimisation calculations are discussed, and the potential advantages of the CDE approach demonstrated through an average 40% improvement in terms of speed of convergence of the calculations presented, and a maximum gain of 68% with larger population size.
Design of PID controller with incomplete derivation based on differential evolution algorithm
Wu Lianghong; Wang Yaonan; Zhou Shaowu; Tan Wen
2008-01-01
To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution (DE) algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.
Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm
Seyed Abbas Taher
2012-01-01
Full Text Available Differential evolution (DE algorithm is used to determine optimal location of unified power quality conditioner (UPQC considering its size in the radial distribution systems. The problem is formulated to find the optimum location of UPQC based on an objective function (OF defined for improving of voltage and current profiles, reducing power loss and minimizing the investment costs considering the OF's weighting factors. Hence, a steady-state model of UPQC is derived to set in forward/backward sweep load flow. Studies are performed on two IEEE 33-bus and 69-bus standard distribution networks. Accuracy was evaluated by reapplying the procedures using both genetic (GA and immune algorithms (IA. Comparative results indicate that DE is capable of offering a nearer global optimal in minimizing the OF and reaching all the desired conditions than GA and IA.
Huseyin Ceylan
2013-01-01
Full Text Available This study proposes a traffic congestion minimization model in which the traffic signal setting optimization is performed through a combined simulation-optimization model. In this model, the TRANSYT traffic simulation software is combined with Differential Evolution (DE optimization algorithm, which is based on the natural selection paradigm. In this context, the EQuilibrium Network Design (EQND problem is formulated as a bilevel programming problem in which the upper level is the minimization of the total network performance index. In the lower level, the traffic assignment problem, which represents the route choice behavior of the road users, is solved using the Path Flow Estimator (PFE as a stochastic user equilibrium assessment. The solution of the bilevel EQND problem is carried out by the proposed Differential Evolution and TRANSYT with PFE, the so-called DETRANSPFE model, on a well-known signal controlled test network. Performance of the proposed model is compared to that of two previous works where the EQND problem has been solved by Genetic-Algorithms- (GAs- and Harmony-Search- (HS- based models. Results show that the DETRANSPFE model outperforms the GA- and HS-based models in terms of the network performance index and the computational time required.
Ramin Mansouri
2014-06-01
Full Text Available Iran, has caused most of the water used and as much as possible to avoid losses. One of the important parameters in agriculture is water distribution uniformity coefficient (CU in sprinkler irrigation. CU amount of water sprinkler operating depends on different pressure heads (P, riser height (RH, distance between sprinklers on lateral pipes (Sl and the distance between lateral pipes (Sm. The best combination of the above parameters for maximum CU, is still unknown for applicators. In this research, CU quantities of zb model sprinkler (made in Iran were measured at Hashemabad cotton research station of Gorgan under 3 different pressure heads (2.5, 3 and 3.5 atm, 2 riser heads (60 and 100 cm and 7 sprinkler (Sl×Sm including 9×12, 9×15, 12×12, 15×12, 12×18, 15×15, 15×18m arrangements. By using differential evolution algorithm (DE, CU equation was optimized and the best optimized coefficients obtained. In this algorithm, the coefficients F and CR equal to 2 and 0.5, respectively, with a population of 100 members and 1000 number of generations (iterations, provides the best results. Absolute error between the results of this algorithm with the measured results is 2.2%. As well as values Wilmot (d and the root-mean square error (RMSE, equal to 0.919 and 2.126, respectively. This results show that this algorithm has high accuracy to estimate water distribution uniformity.
Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.
2011-08-01
This paper proposes a novel optimization approach for the least cost design of looped water distribution systems (WDSs). Three distinct steps are involved in the proposed optimization approach. In the first step, the shortest-distance tree within the looped network is identified using the Dijkstra graph theory algorithm, for which an extension is proposed to find the shortest-distance tree for multisource WDSs. In the second step, a nonlinear programming (NLP) solver is employed to optimize the pipe diameters for the shortest-distance tree (chords of the shortest-distance tree are allocated the minimum allowable pipe sizes). Finally, in the third step, the original looped water network is optimized using a differential evolution (DE) algorithm seeded with diameters in the proximity of the continuous pipe sizes obtained in step two. As such, the proposed optimization approach combines the traditional deterministic optimization technique of NLP with the emerging evolutionary algorithm DE via the proposed network decomposition. The proposed methodology has been tested on four looped WDSs with the number of decision variables ranging from 21 to 454. Results obtained show the proposed approach is able to find optimal solutions with significantly less computational effort than other optimization techniques.
Zhu, Wu; Fang, Jian-an; Tang, Yang; Zhang, Wenbing; Du, Wei
2012-01-01
Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.
Fan, Li; Faryad, Muhammad; Barber, Greg D.; Mallouk, Thomas E.; Monk, Peter B.; Lakhtakia, Akhlesh
2015-01-01
A spectrum splitter can be used to spatially multiplex different solar cells that have high efficiency in mutually exclusive parts of the solar spectrum. We investigated the use of a grating, comprising an array of dielectric cylinders embedded in a dielectric slab, for specularly transmitting one part of the solar spectrum while the other part is transmitted nonspecularly and the total reflectance is very low. A combination of (1) the rigorous coupled-wave approach for computing the reflection and transmission coefficients of the grating and (2) the differential evolution algorithm for optimizing the grating geometry and the refractive indices of dielectric materials was devised as a design tool. We used this tool to optimize two candidate gratings and obtained definite improvements to the initial guesses for the structural and constitutive parameters. Significant spectrum splitting can be achieved if the angle of incidence does not exceed 15 deg.
WANG Congzhe; FANG Yuefa; GUO Sheng
2015-01-01
Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.
Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao
2014-06-16
Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.
Chang Liu
2014-06-01
Full Text Available Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM and a differential evolution (DE algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.
Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.
2016-11-01
The Least Squares (LS), Least Median Squares (LMdS), Reweighted Least Squares (RLS) and Trimmed Least Squares (TLS) estimators are used to obtain parameter estimates of AR models using DE algorithm. The empirical study indicated that, the RLS estimator seems to be very reasonable because of having smaller root mean square error (RMSE), particularly for the Gaussian AR(1) process with unknown drift and additive outliers. Moreover, while LS performs well on shorter processes with less percentage and smaller magnitude of additive outliers (AOS); RLS and TLS compare favorably with respect to LS for longer AR processes. Thus, this study recommends the Reweighted Least Squares estimator as an alternative to the LS estimator in the case of autoregressive processes with additive outliers. The experiment also demonstrates that Differential Evolution (DE) algorithm obtains optimal solutions for fitting first-order autoregressive processes with outliers using the estimators. At the request of all authors of the paper, and with the agreement of the Proceedings Editor, an updated version of this article was published on 15 December 2016. The original version supplied to AIP Publishing contained errors in some of the mathematical equations and in Table 2. The errors have been corrected in the updated and re-published article.
Le-Duc, Thang; Ho-Huu, Vinh; Nguyen-Thoi, Trung; Nguyen-Quoc, Hung
2016-12-01
In recent years, various types of magnetorheological brakes (MRBs) have been proposed and optimized by different optimization algorithms that are integrated in commercial software such as ANSYS and Comsol Multiphysics. However, many of these optimization algorithms often possess some noteworthy shortcomings such as the trap of solutions at local extremes, or the limited number of design variables or the difficulty of dealing with discrete design variables. Thus, to overcome these limitations and develop an efficient computation tool for optimal design of the MRBs, an optimization procedure that combines differential evolution (DE), a gradient-free global optimization method with finite element analysis (FEA) is proposed in this paper. The proposed approach is then applied to the optimal design of MRBs with different configurations including conventional MRBs and MRBs with coils placed on the side housings. Moreover, to approach a real-life design, some necessary design variables of MRBs are considered as discrete variables in the optimization process. The obtained optimal design results are compared with those of available optimal designs in the literature. The results reveal that the proposed method outperforms some traditional approaches.
Jin-Yu Zhang
2014-01-01
Full Text Available This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method.
Zhang, Yanjun; Yu, Chunjuan; Fu, Xinghu; Liu, Wenzhe; Bi, Weihong
2015-12-01
In the distributed optical fiber sensing system based on Brillouin scattering, strain and temperature are the main measuring parameters which can be obtained by analyzing the Brillouin center frequency shift. The novel algorithm which combines the cuckoo search algorithm (CS) with the improved differential evolution (IDE) algorithm is proposed for the Brillouin scattering parameter estimation. The CS-IDE algorithm is compared with CS algorithm and analyzed in different situation. The results show that both the CS and CS-IDE algorithm have very good convergence. The analysis reveals that the CS-IDE algorithm can extract the scattering spectrum features with different linear weight ratio, linewidth combination and SNR. Moreover, the BOTDR temperature measuring system based on electron optical frequency shift is set up to verify the effectiveness of the CS-IDE algorithm. Experimental results show that there is a good linear relationship between the Brillouin center frequency shift and temperature changes.
Koshak, William; Solakiewicz, Richard
2012-01-01
The ability to estimate the fraction of ground flashes in a set of flashes observed by a satellite lightning imager, such as the future GOES-R Geostationary Lightning Mapper (GLM), would likely improve operational and scientific applications (e.g., severe weather warnings, lightning nitrogen oxides studies, and global electric circuit analyses). A Bayesian inversion method, called the Ground Flash Fraction Retrieval Algorithm (GoFFRA), was recently developed for estimating the ground flash fraction. The method uses a constrained mixed exponential distribution model to describe a particular lightning optical measurement called the Maximum Group Area (MGA). To obtain the optimum model parameters (one of which is the desired ground flash fraction), a scalar function must be minimized. This minimization is difficult because of two problems: (1) Label Switching (LS), and (2) Parameter Identity Theft (PIT). The LS problem is well known in the literature on mixed exponential distributions, and the PIT problem was discovered in this study. Each problem occurs when one allows the numerical minimizer to freely roam through the parameter search space; this allows certain solution parameters to interchange roles which leads to fundamental ambiguities, and solution error. A major accomplishment of this study is that we have employed a state-of-the-art genetic-based global optimization algorithm called Differential Evolution (DE) that constrains the parameter search in such a way as to remove both the LS and PIT problems. To test the performance of the GoFFRA when DE is employed, we applied it to analyze simulated MGA datasets that we generated from known mixed exponential distributions. Moreover, we evaluated the GoFFRA/DE method by applying it to analyze actual MGAs derived from low-Earth orbiting lightning imaging sensor data; the actual MGA data were classified as either ground or cloud flash MGAs using National Lightning Detection Network[TM] (NLDN) data. Solution error
Banaja Mohanty
2014-09-01
Full Text Available This paper presents the design and performance analysis of Differential Evolution (DE algorithm based Proportional–Integral (PI and Proportional–Integral–Derivative (PID controllers for Automatic Generation Control (AGC of an interconnected power system. Initially, a two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. In the proposed approach, the design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions are used for the design purpose. The superiority of the proposed approach has been shown by comparing the results with a recently published Craziness based Particle Swarm Optimization (CPSO technique for the same interconnected power system. It is noticed that, the dynamic performance of DE optimized PI controller is better than CPSO optimized PI controllers. Additionally, controller parameters are tuned at different loading conditions so that an adaptive gain scheduling control strategy can be employed. The study is further extended to a more realistic network of two-area six unit system with different power generating units such as thermal, hydro, wind and diesel generating units considering boiler dynamics for thermal plants, Generation Rate Constraint (GRC and Governor Dead Band (GDB non-linearity.
I THAMARAI; S MURUGAVALLI
2017-01-01
Software effort estimation is the process of calculating the effort required to develop a software product based on the input parameters that are usually partial in nature. It is an important task but the most difficult and complicated step in the software product development. Estimation requires detailed information about project scope, process requirements and resources available. Inaccurate estimation leads to financial lossand delay in the projects. Due to the intangible nature of software, most of the software estimation process unreliable. But there is a strong relationship between effort estimation and project management activities.Various methodologies have been employed to improve the procedure of software estimation. This paper reviews journal articles on software development to get the direction in the future estimation research. Several methods for software effort estimation are discussed in this paper, including the data sets widely used and metrics used for evaluation. The use of evolutionary computational tools in the estimation is dealt with in detail.A new model for estimation using differential evolution algorithm called DEAPS is proposed and its advantagesare discussed.
苏国韶; 张小飞; 陈光强; 符兴义
2008-01-01
To determine structure and parameters of a rheological constitutive model for rocks,a new method based on differential evolution(DE) algorithm combined with FLAC3D(a numerical code for geotechnical engineering) was proposed for identification of the global optimum coupled of model structure and its parameters.At first,stochastic coupled mode was initialized,the difference in displacement between the numerical value and in-situ measurements was regarded as fitness value to evaluate quality of the coupled mode.Then the coupled-mode was updated continually using DE rule until the optimal parameters were found.Thus,coupled-mode was identified adaptively during back analysis process.The results of applications to Jinping tunnels in China show that the method is feasible and efficient for identifying the coupled-mode of constitutive structure and its parameters.The method overcomes the limitation of the traditional method and improves significantly precision and speed of displacement back analysis process.
Tarek Bouktir
2012-06-01
Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via Differential Evolution (DE algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Var Compensators (SVC. To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.
Optimal Path Design of Geared 5-bar mechanism using Differential Evolution Algorithm
Ali Aliniay Saghalaksari
2016-06-01
Full Text Available Five-bar linkage mechanisms with two degrees of freedom (DOF are more capable in generating coupler path than four-bar mechanisms with one DOF. The DOF of these mechanisms is reduced to one and they will have constant ratio of binary input when they are equipped by gear. Therefore, besides keeping the simple structure, it is possible to employ them to generate a more accurate path than that generated by four-bar mechanisms using only one input. In this study, using such mechanism for the considered paths, which are used for the comparison purpose, a singleobjective design is performed to optimize the length of mechanism links and revolution ratio of gears by considering the necessary constraints. The error function of square deviation of positions is considered as the objective function and the differential evolution algorithm is utilized in order to solve the considered optimization problems, which are Triangle Curve with 22 Discrete Points and Asteroid Curve with 41 Discrete Points. Compared with the main reference [9], the final results revealed a significant improvement.
DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm.
Chaves-González, José M; Vega-Rodríguez, Miguel A
2014-02-01
In this paper, we use an adapted multi-objective version of the differential evolution (DE) metaheuristics for the design and generation of reliable DNA libraries that can be used for computation. DNA sequence design is a very relevant task in many recent research fields, e.g. nanotechnology or DNA computing. Specifically, DNA computing is a new computational model which uses DNA molecules as information storage and their possible biological interactions as processing operators. Therefore, the possible reactions and interactions among molecules must be strictly controlled to prevent incorrect computations. The design of reliable DNA libraries for bio-molecular computing is an NP-hard combinatorial problem which involves many heterogeneous and conflicting design criteria. For this reason, we modelled DNA sequence design as a multiobjective optimization problem and we solved it by using an adapted multi-objective version of DE metaheuristics. Seven different bio-chemical design criteria have been simultaneously considered to obtain high quality DNA sequences which are suitable for molecular computing. Furthermore, we have developed the multiobjective standard fast non-dominated sorting genetic algorithm (NSGA-II) in order to perform a formal comparative study by using multi-objective indicators. Additionally, we have also compared our results with other relevant results published in the literature. We conclude that our proposal is a promising approach which is able to generate reliable real-world DNA sequences that significantly improve other DNA libraries previously published in the literature.
NIAN Xiaoyu; WANG Zhenlei; QIAN Feng
2013-01-01
To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online,a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed,which is based on the differential evolution (DE) and the group search optimization (GSO).The DEGSO combines the advantages of the two algorithms:the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum.A cooperative method is also proposed for switching between these two algorithms.If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold,it is considered to fall into the local optimization and the other algorithm is chosen.Experiments on benchmark functions show that.the hybrid algorithm outperforms GSO in accuracy,global searching ability and efficiency.The optimization of ethylene and propylene yields is illustrated as a case by DEGSO.After optimization,the yield of ethylene and propylene is increased remarkably,which provides the proper operational condition of the ethylene cracking furnace.
Xiao-lei DONG; Sui-qing LIU; Tao TAO; Shu-ping LI; Kun-lun XIN
2012-01-01
The differential evolution (DE) algorithm has been received increasing attention in terms of optimizing the design for the water distribution systems (WDSs).This paper aims to carry out a comprehensive performance comparison between the new emerged DE algorithm and the most popular algorithm-the genetic algorithm (GA).A total of six benchmark WDS case studies were used with the number of decision variables ranging from 8 to 454.A preliminary sensitivity analysis was performed to select the most effective parameter values for both algorithms to enable the fair comparison.It is observed from the results that the DE algorithm consistently outperforms the GA in terms of both efficiency and the solution quality for each case study.Additionally,the DE algorithm was also compared with the previously published optimization algorithms based on the results for those six case studies,indicating that the DE exhibits comparable performance with other algorithms.It can be concluded that the DE is a newly promising optimization algorithm in the design of WDSs.
Xuemin, Wang; Anqiang, Li; Rui, Zhang
2017-05-01
Due to the wide construction of wind power and the difficulty for it to join the power grid, a short-term hydro-wind economic dispatch (WHED) problem is proposed. WHED system contains several wind power units and hydropower plants, which are renewable and clean. Combined with hydropower plants, the wind power units can join the power grid stably. Then, a WHED system with four cascaded hydropower plants and two wind units is established, and a modified differential evolution (DE) algorithm with chaotic perturbation is proposed for optimizing. Finally, two cases are simulated and analysed, the dispatch results show that the presented model and algorithm are feasible and effective.
A Novel Discrete Differential Evolution Algorithm for the Vehicle Routing Problem in B2C E-Commerce
Xia, Chao; Sheng, Ying; Jiang, Zhong-Zhong; Tan, Chunqiao; Huang, Min; He, Yuanjian
2015-12-01
In this paper, a novel discrete differential evolution (DDE) algorithm is proposed to solve the vehicle routing problems (VRP) in B2C e-commerce, in which VRP is modeled by the incomplete graph based on the actual urban road system. First, a variant of classical VRP is described and a mathematical programming model for the variant is given. Second, the DDE is presented, where individuals are represented as the sequential encoding scheme, and a novel reparation operator is employed to repair the infeasible solutions. Furthermore, a FLOYD operator for dealing with the shortest route is embedded in the proposed DDE. Finally, an extensive computational study is carried out in comparison with the predatory search algorithm and genetic algorithm, and the results show that the proposed DDE is an effective algorithm for VRP in B2C e-commerce.
Ting Hou; Liping Zhang; Yuchen Chen
2014-01-01
.... In this paper, a kind of fuzzy self-optimizing control based on differential evolution algorithm is proposed, which applied in the power plant boiler system, the boiler combustion efficiency has been...
S. U. Khan
2014-01-01
Full Text Available Three issues regarding sensor failure at any position in the antenna array are discussed. We assume that sensor position is known. The issues include raise in sidelobe levels, displacement of nulls from their original positions, and diminishing of null depth. The required null depth is achieved by making the weight of symmetrical complement sensor passive. A hybrid method based on memetic computing algorithm is proposed. The hybrid method combines the cultural algorithm with differential evolution (CADE which is used for the reduction of sidelobe levels and placement of nulls at their original positions. Fitness function is used to minimize the error between the desired and estimated beam patterns along with null constraints. Simulation results for various scenarios have been given to exhibit the validity and performance of the proposed algorithm.
M. Setak
2013-01-01
Full Text Available The hub location problem involves a network of origins and destinations over which transportation takes place. There are many studies associated with finding the location of hub nodes and the allocation of demand nodes to these located hub nodes to transfer the only one kind of commodity under one level of service. However, in this study, carrying different commodity types from origin to destination under various levels of services (e.g. price, punctuality, reliability or transit time is studied. Quality of services experienced by users such as speed, convenience, comfort and security of transportation facilities and services is considered as the level of service. In each system, different kinds of commodities with various levels of services can be transmitted. The appropriate level of service that a commodity can be transmitted through is chosen by customer preferences and the specification of the commodity. So, a mixed integer programming formulation for single allocation hub covering location problem, which is based on the idea of transferring multi commodity flows under multi levels of service is presented. These two are applied concepts, multi-commodity and multi-level of service, which make the model's assumptions closer to the real world problems. In addition, a differential evolution algorithm is designed to find near-optimal solutions. The obtained solutions using differential evolution (DE algorithm (upper bound, where its parameters are tuned by response surface methodology, are compared with exact solutions and computed lower bounds by linear relaxation technique to prove the efficiency of proposed DE algorithm.
SUN Fan; ZHONG Weimin; CHENG Hui; QIAN Feng
2013-01-01
Two general approaches are adopted in solving dynamic optimization problems in chemical processes,namely,the analytical and numerical methods.The numerical method,which is based on heuristic algorithms,has been widely used.An approach that combines differential evolution (DE) algorithm and control vector parameterization (CVP) is proposed in this paper.In the proposed CVP,control variables are approximated with polynomials based on state variables and time in the entire time interval.Region reduction strategy is used in DE to reduce the width of the search region,which improves the computing efficiency.The results of the case studies demonstrate the feasibility and efficiency of the proposed methods.
Abhijit Chandra
2012-04-01
Full Text Available Reduction of computational complexity of digital hardware has drawn the special attention of researchers in recent past. Proper emphasis is needed in this regard towards the settlement of computationally efficient as well as functionally competent design of digital systems. In this communication, we have made one novel attempt for designing multiplier-free Finite duration Impulse Response (FIR digital filter using one robust evolutionary optimization technique, called Differential Evolution (DE. The search has been directed through two sequentially opposite paths which include quantization and optimization as fundamental operations. Besides performing a detailed comparative analysis between these two proposed approaches; the performance evaluation of the designed filter with other existing discrete coefficient FIR models has also been carried out. Finally, the optimum search method for realizing the required set of specifications has been suggested.
Chen Kaiyan; Si Junhong; Zhou Fubao; Zhang Renwei; Shao He; Zhao Hongmei
2015-01-01
In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method (CPM) based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu-tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor-hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve large-scale generalized ventilation networks optimization problem in the future.
Li, Hong; Zhang, Li; Jiao, Yong-Chang
2016-07-01
This paper presents an interactive approach based on a discrete differential evolution algorithm to solve a class of integer bilevel programming problems, in which integer decision variables are controlled by an upper-level decision maker and real-value or continuous decision variables are controlled by a lower-level decision maker. Using the Karush--Kuhn-Tucker optimality conditions in the lower-level programming, the original discrete bilevel formulation can be converted into a discrete single-level nonlinear programming problem with the complementarity constraints, and then the smoothing technique is applied to deal with the complementarity constraints. Finally, a discrete single-level nonlinear programming problem is obtained, and solved by an interactive approach. In each iteration, for each given upper-level discrete variable, a system of nonlinear equations including the lower-level variables and Lagrange multipliers is solved first, and then a discrete nonlinear programming problem only with inequality constraints is handled by using a discrete differential evolution algorithm. Simulation results show the effectiveness of the proposed approach.
无
2010-01-01
We give an equivalent construction of the infinitesimal time translation operator for partial differential evolution equation in the algebraic dynamics algorithm proposed by Shun-Jin Wang and his students. Our construction involves only simple partial differentials and avoids the derivative terms of δ function which appear in the course of computation by means of Wang-Zhang operator. We prove Wang’s equivalent theorem which says that our construction and Wang-Zhang’s are equivalent. We use our construction to deal with several typical equations such as nonlinear advection equation, Burgers equation, nonlinear Schrodinger equation, KdV equation and sine-Gordon equation, and obtain at least second order approximate solutions to them. These equations include the cases of real and complex field variables and the cases of the first and the second order time derivatives.
Liu, Chengshi
2010-08-01
We give an equivalent construction of the infinitesimal time translation operator for partial differential evolution equation in the algebraic dynamics algorithm proposed by Shun-Jin Wang and his students. Our construction involves only simple partial differentials and avoids the derivative terms of δ function which appear in the course of computation by means of Wang-Zhang operator. We prove Wang’s equivalent theorem which says that our construction and Wang-Zhang’s are equivalent. We use our construction to deal with several typical equations such as nonlinear advection equation, Burgers equation, nonlinear Schrodinger equation, KdV equation and sine-Gordon equation, and obtain at least second order approximate solutions to them. These equations include the cases of real and complex field variables and the cases of the first and the second order time derivatives.
Nguyen Ngoc Son
2016-12-01
Full Text Available This article proposes a novel advanced differential evolution method which combines the differential evolution with the modified back-propagation algorithm. This new proposed approach is applied to train an adaptive enhanced neural model for approximating the inverse model of the industrial robot arm. Experimental results demonstrate that the proposed modeling procedure using the new identification approach obtains better convergence and more precision than the traditional back-propagation method or the lonely differential evolution approach. Furthermore, the inverse model of the industrial robot arm using the adaptive enhanced neural model performs outstanding results.
Optimization of a mirror-based neutron source using differential evolution algorithm
Yurov, D. V.; Prikhodko, V. V.
2016-12-01
This study is dedicated to the assessment of capabilities of gas-dynamic trap (GDT) and gas-dynamic multiple-mirror trap (GDMT) as potential neutron sources for subcritical hybrids. In mathematical terms the problem of the study has been formulated as determining the global maximum of fusion gain (Q pl), the latter represented as a function of trap parameters. A differential evolution method has been applied to perform the search. Considered in all calculations has been a configuration of the neutron source with 20 m long distance between the mirrors and 100 MW heating power. It is important to mention that the numerical study has also taken into account a number of constraints on plasma characteristics so as to provide physical credibility of searched-for trap configurations. According to the results obtained the traps considered have demonstrated fusion gain up to 0.2, depending on the constraints applied. This enables them to be used either as neutron sources within subcritical reactors for minor actinides incineration or as material-testing facilities.
Islam, Sk Minhazul; Das, Swagatam; Ghosh, Saurav; Roy, Subhrajit; Suganthan, Ponnuthurai Nagaratnam
2012-04-01
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.
Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.
2016-10-01
Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.
Ning Dong
2014-01-01
functions are executed, and comparisons with five state-of-the-art algorithms are made. The results illustrate that the proposed algorithm is competitive with and in some cases superior to the compared ones in terms of the quality, efficiency, and the robustness of the obtained results.
Ekinci, Yunus Levent; Balkaya, Çağlayan; Göktürkler, Gökhan; Turan, Seçil
2016-06-01
An efficient approach to estimate model parameters from residual gravity data based on differential evolution (DE), a stochastic vector-based metaheuristic algorithm, has been presented. We have showed the applicability and effectiveness of this algorithm on both synthetic and field anomalies. According to our knowledge, this is a first attempt of applying DE for the parameter estimations of residual gravity anomalies due to isolated causative sources embedded in the subsurface. The model parameters dealt with here are the amplitude coefficient (A), the depth and exact origin of causative source (zo and xo, respectively) and the shape factors (q and ƞ). The error energy maps generated for some parameter pairs have successfully revealed the nature of the parameter estimation problem under consideration. Noise-free and noisy synthetic single gravity anomalies have been evaluated with success via DE/best/1/bin, which is a widely used strategy in DE. Additionally some complicated gravity anomalies caused by multiple source bodies have been considered, and the results obtained have showed the efficiency of the algorithm. Then using the strategy applied in synthetic examples some field anomalies observed for various mineral explorations such as a chromite deposit (Camaguey district, Cuba), a manganese deposit (Nagpur, India) and a base metal sulphide deposit (Quebec, Canada) have been considered to estimate the model parameters of the ore bodies. Applications have exhibited that the obtained results such as the depths and shapes of the ore bodies are quite consistent with those published in the literature. Uncertainty in the solutions obtained from DE algorithm has been also investigated by Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing without cooling schedule. Based on the resulting histogram reconstructions of both synthetic and field data examples the algorithm has provided reliable parameter estimations being within the sampling limits of
Topology Optimization of Structure Using Differential Evolution
Chun-Yin Wu
2008-02-01
Full Text Available The population-based evolutionary algorithms have emerged as powerful mechanism for finding optimum solutions of complex optimization problems. A promising new evolutionary algorithm, differential evolution, has garnered significant attention in the engineering optimization research. Differential evolution has the advantage of incorporating a relatively simple and efficient form of mutation and crossover. This paper aims at introducing differential evolution as an alternative approach for topology optimization of truss and continuous structure with stress and displacement constraints. In comparison the results with other studies, it shows that differential evolution algorithms are very effective and efficient in solving topology optimization problem of structure.
Balkaya, Çağlayan; Ekinci, Yunus Levent; Göktürkler, Gökhan; Turan, Seçil
2017-01-01
3D non-linear inversion of total field magnetic anomalies caused by vertical-sided prismatic bodies has been achieved by differential evolution (DE), which is one of the population-based evolutionary algorithms. We have demonstrated the efficiency of the algorithm on both synthetic and field magnetic anomalies by estimating horizontal distances from the origin in both north and east directions, depths to the top and bottom of the bodies, inclination and declination angles of the magnetization, and intensity of magnetization of the causative bodies. In the synthetic anomaly case, we have considered both noise-free and noisy data sets due to two vertical-sided prismatic bodies in a non-magnetic medium. For the field case, airborne magnetic anomalies originated from intrusive granitoids at the eastern part of the Biga Peninsula (NW Turkey) which is composed of various kinds of sedimentary, metamorphic and igneous rocks, have been inverted and interpreted. Since the granitoids are the outcropped rocks in the field, the estimations for the top depths of two prisms representing the magnetic bodies were excluded during inversion studies. Estimated bottom depths are in good agreement with the ones obtained by a different approach based on 3D modelling of pseudogravity anomalies. Accuracy of the estimated parameters from both cases has been also investigated via probability density functions. Based on the tests in the present study, it can be concluded that DE is a useful tool for the parameter estimation of source bodies using magnetic anomalies.
An improved differential evolution algorithm for TSP%旅行商问题的改进差分进化方法
梅觅; 薛惠锋; 谷雨
2011-01-01
TSP(Traveling Salesman Problem)旅行商问题是一类典型的NP完全问题,目前大多采用遗传算法求解.差分进化算法(Differential Evolution Algorithm, DE)作为一种新型的进化算法,与遗传算法有很多相似之处.提出用改进的差分进化算法解决TSP问题.采用基于整数序规范的辅助算子解决变异问题,并引入刘海交叉算子.实验结果表明该方法有效地提高了算法的收敛速度与寻优质量,表现出了良好的特性.%TSP ( Traveling Salesman Problem) is a kind of typical NP problems, and currently is solved by genetic algorithm ( GA) generally. As a new kind of evolution algorithm, differential evolution algorithm ( DE) shares many common performances with GA. In order to solve TSP more conveniently,an improved differential evolution algorithm was proposed. The new method added an auxiliary operator for regulating integer sequence in the mutation process and used Liuhai crossover operator to replace the original crossover operator. Through several experiments, it could be concluded that this method can significantly improve the speed of convergence and the quality of optimal results, features well characteristic in TSP.
多目标优化差分进化算法%Differential Evolution Algorithm for Multi-Objective Optimization
敖友云; 迟洪钦
2011-01-01
Fitness assignment of individuals and diversity maintenance of population are two key techniques of evolutionary algorithms. First, on the one hand, this paper introduces some related concepts of Pareto e~dom-inance which can determine the strength Pareto values of the individuals of population, according to the strength Pareto values of individuals, some better individuals are selected into the offspring population by the technique of Pareto ranking; on the other hand, in order to maintain the diversity of population, a crowded-density method is introduced to remove some individuals that are located in the crowed regions. Then, according to some characteristics of differential evolution (DE), through using the appropriate DE strategies and control parameters, this paper proposes a differential evolution algorithm for multi-objective optimization, which is called DEAMO. Finally, numerical experiments show that DEAMO can perform well when tested on several benchmark multi-objective optimization problems.%个体的适应度赋值和群体的多样性维护是进化算法的两个关键问题.首先,一方面,定义了Paretoε-支配关系的相关概念,通过Paretoε-支配关系确定个体的强度Pareto值,根据个体的强度Pareto值对群体进行Pareto分级排序,实现优胜劣汰；另一方面,使用拥挤距离估算个体的拥挤密度,淘汰位于拥挤区的一些个体,维持群体的多样性.然后,根据差分进化算法的特点,使用适当的进化策略和控制参数,给出了一种用于求解多目标优化问题的差分进化算法DEAMO.最后,数值实验表明,DEAMO在求解标准的多目标优化问题时性能表现优良.
Abhijit Chandra
2012-10-01
Full Text Available In recent times, system designers are becoming very much apprehensive in reducing the structural complexity of digital systems with which they deal in practice. However, the uncontrolled minimization of any digital hardware always leads to significant deterioration of system performance making it incompatible for use in any practical system. As proper trade-off is inevitably essential between achievable performance and required hardware, researchers have sought a number of artificially intelligent optimization techniques to solve it out. Since such a technique generally involves variety of constructional alternatives, appropriate use of correct option demands justified attention. Numerous evolutionary computation techniques, being a branch of biologically inspired optimization process, are being increasingly used for a number of signal processing applications of late. This paper throws enough light to select the most suitable mutation strategy of Differential Evolution (DE algorithm for efficient design of multiplier-less low-pass finite duration impulse response (FIR filter. Computationally efficient mutation scheme has been identified by observing convergence behavior and error histogram plot for different alternatives. Performance of the designed filter has been compared in terms of its magnitude response and the requirement of various hardware blocks for four different lengths of the filter. Consequently the name of the most favorable mutation rule has been suggested upon analyzing all the factors. Finally the supremacy of our proposed design has been established by comparing its performance with that of other state-of-the-art multiplier-less low-pass FIR filters.
Clearance of Flight Control Law Based on Cultural Differential Evolution Algorithm%基于差分进化算法的飞行控制律评估
李爱军; 王景; 李佳; 王长青
2014-01-01
针对传统文化算法进化后期收敛速度慢和差分进化算法在进化过程中缺乏对知识有效利用的问题，提出一种新的文化差分进化算法。该算法以文化算法为框架，将差分进化算法的变异、交叉和选择作为种群空间的进化操作，并通过信念空间的知识指导种群进化。根据飞行品质规范选取迎角响应限制准则，以飞机模型ADMIRE为研究对象，利用该算法对存在不确定条件下的飞行控制律进行非线性评估，克服传统网格评估方法在工程应用中的不足。仿真结果表明，与改进差分进化算法相比，文化差分进化算法在全飞行包线范围内找出最坏的不确定参数组合，具有更高的可靠性和效率。%Aiming at the slow converge rate in traditional cultural algorithm and lower use efficiency of knowledge about evolutionary information in differential evolution algorithm, a new cultural differential evolution algorithm is proposed. The cultural algorithm is utilized as the framework of the proposed algorithm, in which the evolution in population space consists of mutation, crossover and selection of the differential evolution. In addition, the population space evolution is guided by the belief space knowledge. According to the flying quality specifications, a nonlinear criterion is presented. The proposed algorithm is then applied to evaluate angle of attack limit exceedance criterion, which is current widely used in the aerospace industry. The full authority flight control law of the Aero-Data Model in Research Environment ( ADMIRE) is evaluated with uncertainties by the proposed algorithm, which overcomes the limitations of traditional grid-based ones. The simulation results validate that the reliability, computational complexity and efficiency of the proposed algorithm outperform those of the modified differential evolution algorithm, especially in searching for the worst uncertain parameter combinations
Rasim M. Alguliev
2011-01-01
Full Text Available Extractive multidocument summarization is modeled as a modified p-median problem. The problem is formulated with taking into account four basic requirements, namely, relevance, information coverage, diversity, and length limit that should satisfy summaries. To solve the optimization problem a self-adaptive differential evolution algorithm is created. Differential evolution has been proven to be an efficient and robust algorithm for many real optimization problems. However, it still may converge toward local optimum solutions, need to manually adjust the parameters, and finding the best values for the control parameters is a consuming task. In the paper is proposed a self-adaptive scaling factor in original DE to increase the exploration and exploitation ability. This paper has found that self-adaptive differential evolution can efficiently find the best solution in comparison with the canonical differential evolution. We implemented our model on multi-document summarization task. Experiments have shown that the proposed model is competitive on the DUC2006 dataset.
Kumar Deepak
2015-12-01
Full Text Available Groundwater contamination due to leakage of gasoline is one of the several causes which affect the groundwater environment by polluting it. In the past few years, In-situ bioremediation has attracted researchers because of its ability to remediate the contaminant at its site with low cost of remediation. This paper proposed the use of a new hybrid algorithm to optimize a multi-objective function which includes the cost of remediation as the first objective and residual contaminant at the end of the remediation period as the second objective. The hybrid algorithm was formed by combining the methods of Differential Evolution, Genetic Algorithms and Simulated Annealing. Support Vector Machines (SVM was used as a virtual simulator for biodegradation of contaminants in the groundwater flow. The results obtained from the hybrid algorithm were compared with Differential Evolution (DE, Non Dominated Sorting Genetic Algorithm (NSGA II and Simulated Annealing (SA. It was found that the proposed hybrid algorithm was capable of providing the best solution. Fuzzy logic was used to find the best compromising solution and finally a pumping rate strategy for groundwater remediation was presented for the best compromising solution. The results show that the cost incurred for the best compromising solution is intermediate between the highest and lowest cost incurred for other non-dominated solutions.
一种求解MAX-k-SAT问题的新方法%Solving MAX-k-SAT problems by improved differential evolution algorithm
宋建民; 弓小影
2014-01-01
An improved binary differential evolution algorithm (denoted as IBDE) to solving the maximum satisfiability problem (MAX-k-SAT) was put forward,and the improved algorithm was verified by solving a series of random mass MAX-k-SAT instances in this paper.The computational results of IBDE through practicing series of random large-scale instances of MAX-k-SAT showed that IBDE was a new effective algorithm for MAX-k-SAT.%基于差分演化算法提出了一种求解最大可满足问题（MAX-k-SAT）的改进算法,记为IBDE,并通过对一系列随机大规模MAX-k-SAT实例的求解进行验证。实验结果表明：IBDE是一种求解MAX-k-SAT问题非常有效的新方法。
Wang, Lin; Qu, Hui; Chen, Tao; Yan, Fang-Ping
2013-01-01
The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP). The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs), the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP's difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE) is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA) and hybrid DE (HDE) are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem.
Lin Wang
2013-01-01
Full Text Available The integration with different decisions in the supply chain is a trend, since it can avoid the suboptimal decisions. In this paper, we provide an effective intelligent algorithm for a modified joint replenishment and location-inventory problem (JR-LIP. The problem of the JR-LIP is to determine the reasonable number and location of distribution centers (DCs, the assignment policy of customers, and the replenishment policy of DCs such that the overall cost is minimized. However, due to the JR-LIP’s difficult mathematical properties, simple and effective solutions for this NP-hard problem have eluded researchers. To find an effective approach for the JR-LIP, a hybrid self-adapting differential evolution algorithm (HSDE is designed. To verify the effectiveness of the HSDE, two intelligent algorithms that have been proven to be effective algorithms for the similar problems named genetic algorithm (GA and hybrid DE (HDE are chosen to compare with it. Comparative results of benchmark functions and randomly generated JR-LIPs show that HSDE outperforms GA and HDE. Moreover, a sensitive analysis of cost parameters reveals the useful managerial insight. All comparative results show that HSDE is more stable and robust in handling this complex problem especially for the large-scale problem.
El-Qulity, Said Ali; Mohamed, Ali Wagdy
2016-01-01
This paper proposes a nonlinear integer goal programming model (NIGPM) for solving the general problem of admission capacity planning in a country as a whole. The work aims to satisfy most of the required key objectives of a country related to the enrollment problem for higher education. The system general outlines are developed along with the solution methodology for application to the time horizon in a given plan. The up-to-date data for Saudi Arabia is used as a case study and a novel evolutionary algorithm based on modified differential evolution (DE) algorithm is used to solve the complexity of the NIGPM generated for different goal priorities. The experimental results presented in this paper show their effectiveness in solving the admission capacity for higher education in terms of final solution quality and robustness.
Shaheen, Husam I.; Rashed, Ghamgeen I.; Cheng, S.J. [Electric Power Security and High Efficiency Lab, Department of Electrical Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei (China)
2011-01-15
This paper presents a new approach based on Differential Evolution (DE) technique to find out the optimal placement and parameter setting of Unified Power Flow Controller (UPFC) for enhancing power system security under single line contingencies. Firstly, we perform a contingency analysis and ranking process to determine the most severe line outage contingencies considering line overloads and bus voltage limit violations as a Performance Index. Secondly, we apply DE technique to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach, we perform simulations on an IEEE 14-bus and an IEEE 30-bus power systems. The results we have obtained indicate that installing UPFC in the location optimized by DE can significantly enhance the security of power system by eliminating or minimizing the overloaded lines and the bus voltage limit violations. (author)
Application of differential evolution algorithm in wind power integrated system%差分进化法在风电并网系统中的应用
匡洪海; 吴政球; 李圣清; 李军军
2013-01-01
为解决多机风电并网系统的稳定性问题,提出在风电并网系统的同步发电机(SG)中安装电力系统稳定器(PSS),利用差分进化算法解决SG中自动电压调节器(AVR)和PSS参数的最优调节问题.在有、无PSS以及是否使用差分进化法的各种情况下对风电并网系统稳定性进行了研究分析,研究表明通过差分进化法的协同调节使含AVR和PSS的风电并网系统有良好的阻尼作用,能减少发电机转子角差振荡,提高电压稳定性,通过仿真结果对比可知差分进化法可使并网系统稳定性明显提高.%In order to solve stability problem of multi-machine wind power integrated system, Power System Stabilizers(PSSs) are installed for Synchronous Generator(SG), using Differential Evolution(DE) algorithm to solve the optimal regulation problem of Automatic Voltage Regulator( AVR) and PSS parameters in SG. Under the situation of with or without PSS and whether using differential evolution algorithm, the stability of wind power integrated system is studied. The research shows that wind power integrated system has good damping effect by AVR-PSS coordinated tuning based on DE algorithm, can reduce oscillation of rotor angle difference and improve voltage stability. Simulation results show that the stability of wind power integrated system can be greatly improved through using the DE algorithm.
徐进权; 王宏志; 胡黄水
2016-01-01
To optimize the periodic polling table in Multifunction Vehicle Bus (M VB) ,a new design based on differential evolution algorithm is proposed .According to the relevant provisions of international standard IEC61375 ,we set the periodic polling table generation rules and constraints , and establish the mathematical model .With the evenness degree as objective function ,periodic polling table is built and optimized .The differential evolution algorithm is compared with the step fill method in the IEC61375‐1 to show that the former is with better performance .%针对多功能车辆总线（Multifunction Vehicle Bus ，MVB）周期扫描表提出了一种利用差分进化算法的优化设计方法。根据 IEC61375国际标准相关规定，明确了周期扫描表生成规则和约束条件，建立了相应的数学模型，以均匀度为目标函数，对周期扫描表进行建立和优化。最后，通过与国际标准中的逐步填空法进行均匀度对比，显示出差分进化算法的优势。
一种基于精英云变异的差分演化算法%A Novel Differential Evolution Algorithm Based on Elite-Cloudy Mutation
郭肇禄; 吴志健; 汪靖; 汪慎文; 谢承旺
2013-01-01
针对传统差分演化算法在演化过程中存在少数个体出现停滞的现象,提出一种基于精英云变异的差分演化算法.该算法在演化过程中统计出每个个体的停滞代数,当一个个体的停滞代数达到指定的阈值时,对该个体执行精英云变异操作,使其向最优个体靠近,从而加快收敛速度；同时以一定的概率对所有个体执行一般反向学习操作,以增加种群的多样性.对比实验结果表明该算法在收敛速度和求解精度上均具有一定的优势.%Aiming at the disadvantage of traditional differential evolution, namely, existing some stagnating individuals in the evolutionary process, a novel differential evolution algorithm based on elite-cloudy mutation (ECMDE) is proposed in this study. In the proposed algorithm, stagnation generation of each individual is counted in the evolutionary process. Moreover, an individual is executed by the elite-cloudy mutation to approach the best individual when the stagnation generation of the individual is more than a pre-defined threshold value. Thus, it can accelerate the convergence speed. Additionally, in order to increase the population diversity, it executes the opposition-based learning operator with a certain probability. Experimental results indicate that the proposed algorithm obtains promising performance in both solution precision and convergence speed.
龙文
2012-01-01
提出一种新的多目标优化差分进化算法用于求解约束优化问题.该算法利用佳点集方法初始化个体以维持种群的多样性.将约束优化问题转化为两个目标的多目标优化问题.基于Pareto支配关系,将种群分为Pareto子集和Non-Pareto子集,结合差分进化算法两种不同变异策略的特点,对Non-Pareto子集和Pareto子集分别采用DE/best/1变异策略和DE/rand/1变异策略.数值实验结果表明该算法具有较好的寻优效果.%A novel multi-objective optimization differential evolution algorithm is proposed for solving constrained optimization problems. In the process of population evolution, the individuals generation based on good-point-set method is introduced into the evolutionary algorithm initial step. The constrained optimization problem is converted into a multi-objective optimization problem. The population is divided into Non-Pareto set and Pareto set based on multi-objective optimization technique. In order to improve global convergence of the proposed algorithm, DE/best/1 mutation scheme and DE/rand/1 mutation scheme are used to the Non-Pareto set and the Pareto set respectively. The experimental results show that the proposed algorithm can get high performance while dealing with various complex problems.
Dynamic differential evolution algorithm for swarm robots search path planning%复杂环境移动群机器人最优路径规划方法
徐雪松; 杨胜杰; 陈荣元
2016-01-01
研究了一类复杂环境下移动群机器人的建模与控制策略.采用栅格法对机器人工作环境进行建模,基于个体的有限感知能力和局部的交互机制设计了响应概率函数,解决群机器人任务分配与信息共享难题.通过施加螺旋控制于早期信号搜索,并将该搜索信息作为启发因子改进动态差分进化算法,对群机器人进行路径优化.仿真结果表明,当响应概率函数中距离变量调节因子β=0.006时,任务分配控制算法达到最好效果.同时,移动群机器人路径规划的平均路径长度ˉS,平均移动时间Tˉ以及平均收敛代数Mˉ,相比扩展PSO算法分别提高了16%、57%及230%.最后,将该算法应用于AS-UⅢ型轮式移动群机器人物理实验,并设计了协同控制平台,具有较好的工程应用价值.%A novel optimization algorithm based on differential evolution is proposed in this paper .The modeling and the control strategies of swarming robots for search planning in a complex environment are discussed .Grid method is used for robot working environment modeling .The response probability function is designed based on in-dividual's limited cognitive ability and local interaction mechanism , which can solve the problem of the swarm robot task allocation and information sharing.Robots moving spirally to search cues can offer evidence for using dynamic differential evolution algorithm to search target optimally.The simulation results show that when the response proba-bility function distance variable regulating factorβ=0.006, task allocation control algorithm can achieve the best effect .At the same time , the mobile robot path planning group of average path length , average moving time and av-erage convergence algebraic extension compared to PSO algorithm is enhanced by 16%, 57% and 230% respec-tively.This algorithm is introduced to AS-UⅢ wheel mobile robots real experiments and illustrated its engineering application value.
A Novel and Robust Evolution Algorithm for Optimizing Complicated Functions
Gao, Yifeng; Zhao, Ge
2011-01-01
In this paper, a novel mutation operator of differential evolution algorithm is proposed. A new algorithm called divergence differential evolution algorithm (DDEA) is developed by combining the new mutation operator with divergence operator and assimilation operator (divergence operator divides population, and, assimilation operator combines population), which can detect multiple solutions and robustness in noisy environment. The new algorithm is applied to optimize Michalewicz Function and to track changing of rain-induced-attenuation process. The results based on DDEA are compared with those based on Differential Evolution Algorithm (DEA). It shows that DDEA algorithm gets better results than DEA does in the same premise. The new algorithm is significant for optimizing and tracking the characteristics of MIMO (Multiple Input Multiple Output) channel at millimeter waves.
Hammoudi Abderazek
2015-09-01
Full Text Available Profile shift has an immense effect on the sliding, load capacity, and stability of involute cylindrical gears. Available standards such as ISO/DIS 6336 and BS 436 DIN/3990 currently give the recommendation for the selection of profile shift coefficients. It is, however, very approximate and usually given in the form of implicit graphs or charts. In this article, the optimal selection values of profile shift coefficients for cylindrical involute spur and helical gears are described, using a differential evolution algorithm. The optimization procedure is developed specifically for exact balancing specific sliding coefficients at extremes of contact path and account for gear design constraints. The obtained results are compared with those of standards and research of other authors. They demonstrate the effectiveness and robustness of the applied method. A substantial improvement in balancing specific sliding coefficients is found in this work.
Polarization WSF Algorithm Based on Differential Evolution%基于微分进化的极化WSF信号参数估计算法
刘扬; 吴瑛
2011-01-01
Compared with traditional antenna array, the polarization sensitive array can receive spatial information and more complete electromagnetic information. It has higher receive gain due to less sensitivity to the variation of signal polarization. The polarization weighted subspace fitting (WSF) algorithm is obviously better in accuracy and resolution than the general subspace algorithm and can process coherent signals. The algorithm has good robustness. But the number of parameters needed to be estimated is twice more than traditional WSF, so computation problem appears more prominent. To deal with this problem, the genetic algorithm is used to polarization WSF. But poor performance is expressed, which is different from traditional WSF. Differential evolution algorithm, features as simplicity, fast convergence, high accuracy, search performance, and stability, is suitable for solving multi-dimensional functions of maximum solution, this paper applies the algorithm to the polarization WSF and compares it with the WSF based on genetic algorithm. Experimental comparison simulation shows the efficiency of the method.%极化敏感阵列与传统的天线阵列相比,可以同时接收到信号的空间信息和更加完整的电磁信息,由于受信号极化变化的干扰较小,接收增益更高,估计出的极化状态参数可以用于检测、多址等领域,因此具有更加广阔的开发价值.极化加权信号子空间( WSF)算法的精度、分辨率明显优于一般子空间类算法,并且可以处理相干信号,鲁棒性较好,与传统空间谱WSF相比,需要估计的参数多了一倍,计算量问题显得更加突出.针对该问题,首先将遗传算法应用于联合谱WSF,与传统测向不同,性能不佳.微分进化算法简单,收敛速度快,搜索精度高,性能稳定,将该算法应用于极化加权信号子空间算法的多维函数求解,并将它与基于遗传算法的极化WSF进行比较,证明文中算法的有效性.
张军丽; 周永权
2011-01-01
人工萤火虫优化算法在寻找函数全局最优值时存在着收敛速度慢、易陷入局部最优、收敛成功率和计算精度低等缺点,为此,文中将人工鱼群算法的觅食行为嵌入到人工萤火虫算法,并与差分进化算法融合,提出一种基于人工萤火虫与差分进化的混合优化算法.最后,通过4个典型测试函数和1个应用实例进行测试,结果表明所提出的混合算法收敛速度快,计算精度高,其整体逼近性能比基本人工萤火虫和差分进化算法更优.%When searching for the globally optimal solution of function, there exist some shortcomings in artificial glowworm swam optimization (GSO), such as the slow convergence speed, easily falling into the local optimum value, the low success rate of convergence and computational accuracy. This paper embeds predatory behavior of artificial fish swarm algorithm (AFSA) into GSO and proposes a hybrid optimization algorithm which combines the GSO with differential evolution (DE). Finally, the algorithm is put through four typical test functions and an application example. The results show that the hybrid algorithm has better convergence efficiency and higher computational precision, and its overall approximation performance is superior to basic artificial GSO and DE.
范泽华; 白铁成
2016-01-01
差分演化算法的实现简单有效，但其搜索能力较弱，对此提出一种基于贝塔分布的控制参数动态设置策略以提高差分演化的优化效果，并将其应用于图像分割问题。首先，将图像的直方图按强度分为两类，并按类内方差、类间方差与总方差总结为待优化的目标函数；然后，使用改进的差分演化算法搜索图像分割目标函数的最优解，其中在每轮迭代中使用贝塔分布动态的设置控制参数。仿真实验表明，该方法获得了较好的优化结果，并获得了较好的图像分割效果。%The differential evolution algorithm is effective and easy to realize,but it has poor search ability,so a control parameter dynamic setting strategy based on beta distribution is proposed to improve the optimization effect of the differential evo⁃lution,and applied to the image segmentation. In the scheme,the image histograms are divided into two classes according their intensity,and summarized to the waiting optimization target function according to the inner⁃class variance,inter⁃class variance and total variance. And then,the improved differential evolution algorithm is used to search the optimal solution of the image segmentation target function,in which the beta distribution is used to set the control parameters dynamically in each iteration. The simulation experiment results show that the proposed method can obtain better optimal result and good image segmentation effect.
A Hybrid Differential Invasive Weed Algorithm for Congestion Management
Basak, Aniruddha; Pal, Siddharth; Pandi, V. Ravikumar; Panigrahi, B. K.; Das, Swagatam
This work is dedicated to solve the problem of congestion management in restructured power systems. Nowadays we have open access market which pushes the power system operation to their limits for maximum economic benefits but at the same time making the system more susceptible to congestion. In this regard congestion management is absolutely vital. In this paper we try to remove congestion by generation rescheduling where the cost involved in the rescheduling process is minimized. The proposed algorithm is a hybrid of Invasive Weed Optimization (IWO) and Differential Evolution (DE). The resultant hybrid algorithm was applied on standard IEEE 30 bus system and observed to beat existing algorithms like Simple Bacterial foraging (SBF), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), Differential Evolution (DE) and hybrid algorithms like Hybrid Bacterial Foraging and Differential Evolution (HBFDE) and Adaptive Bacterial Foraging with Nelder Mead (ABFNM).
Research on community detection based on differential evolution algorithm%基于差分演化的复杂网络社区挖掘算法研究
柴丹炜; 张若昕; 刘建生
2016-01-01
Community detection has been an important research direction of the structure of complex network, whose mining algorithm is a crucial core issue. To improve the accuracy of community detection, a community detection algorithm based on the theory of differential evolution for complex network (Differential Evolution Community Detection Algorithm, DECD) is presented. In the study, the algorithm proposed a creative encoding mode and chose the modularity density function as the optimization objective for differential evolution algorithm to detect the structure of complex networks. Experimental results demonstrate that not only the encoding speed is optimized and the repetition encoding problems is solved by the creative encoding mode, but also the accuracy of community detection in complex networks is improved by DECD algorithm.%社区结构的挖掘问题已经成为复杂网络中重要的研究方向，其挖掘算法是关键的核心问题。为了提高对社区结构进行挖掘的准确度，提出一种基于差分演化思想的复杂网络社区挖掘算法（Differential Evolution Community Detection Algorithm, DECD ）。 DECD算法设计了一种新的编码方式，以模块密度函数作为优化目标，通过差分演化算法对复杂网络实施有效划分。实验结果表明，新的编码方式提高了编码速度并解决了社区重复编码问题，同时DECD算法能够提高复杂网络中的社区结构挖掘的准确度。
Primorac, E.; Kuhlenbeck, H.; Freund, H.-J.
2016-07-01
The structure of a thin MoO3 layer on Au(111) with a c(4 × 2) superstructure was studied with LEED I/V analysis. As proposed previously (Quek et al., Surf. Sci. 577 (2005) L71), the atomic structure of the layer is similar to that of a MoO3 single layer as found in regular α-MoO3. The layer on Au(111) has a glide plane parallel to the short unit vector of the c(4 × 2) unit cell and the molybdenum atoms are bridge-bonded to two surface gold atoms with the structure of the gold surface being slightly distorted. The structural refinement of the structure was performed with the CMA-ES evolutionary strategy algorithm which could reach a Pendry R-factor of ∼ 0.044. In the second part the performance of CMA-ES is compared with that of the differential evolution method, a genetic algorithm and the Powell optimization algorithm employing I/V curves calculated with tensor LEED.
马小华; 李济民
2011-01-01
For the shortcomings of being slow in convergence in the late and easy to fall into local minima, a differential evolution algorithm merging logarithm crossing probability and random migration was presented. This algorithm enhanced the convergence speed and precision, and also improved the ability of global optimization. Numerical results show that the proposed algorithm LMDE was better than the basic differential evolution algorithm DE and the chaotic differential evolution algorithm CDE in terms of convergence and robustness as well as the ability of global optimization.%针对差分进化算法在后期收敛缓慢和易陷入局部极值缺点,提出了一种带有对数递增交叉概率因子和随机迁移算子的差分进化算法.这个算法增强了收敛速度和精度,同时也提高了全局寻优能力.数值实验结果表明,所提出的算法LMDE比基本DE和带混沌差分进化算法CDE在收敛性和稳健性以及全局寻优能力方面更好.
K. Asan Mohideen
2014-07-01
Full Text Available Improving the transient performance of the MRAC has been a point of research for a long time. The main objective of the paper is to design an MRAC with improved transient and steady state performance. This paper proposes a Fuzzy modified MRAC (FMRAC to control a coupled tank level process. The FMRAC uses a proportional control based Mamdani-type Fuzzy inference system (MFIS to improve the transient performance of a direct MRAC. In addition, it proposes the application of Differential Evolution (DE algorithm to tune the membership function parameters off-line of the FMRAC to improve its performance further. The proposed controller is called DE based Fuzzy Modified Model Reference Adaptive Controller (DEFMRAC. In this study, an MRAC, an FMRAC and the proposed DEFMRAC are designed for a coupled tank level process and their performances are compared. The coupled tank level process is modeled by using system identification procedure and the accuracy of the resultant model is further improved by parameter tuning using DE. The simulation results show that the FMRAC gives better transient performance than the direct MRAC. The results also show that the proposed DEFMRAC gives better transient performance than the direct MRAC or the FMRAC. It is concluded that the proposed controller can be used to obtain very good transient and steady state performance in the control of nonlinear processes.
Turbomachinery Airfoil Design Optimization Using Differential Evolution
Madavan, Nateri K.; Biegel, Bryan (Technical Monitor)
2002-01-01
An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine and compared to earlier methods. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.
Adaptive differential evolution a robust approach to multimodal problem optimization
Zhang, Jingqiao; Zhang, Jingqiao
2009-01-01
The fundamental theme of this book is theoretical study of differential evolution and algorithmic analysis of parameter adaptive schemes. The book offers real-world insights into a variety of large-scale complex industrial applications.
The Power Unit Coordinated Control via Uniform Differential Evolution
Zain Abdalla Zahran; Rui Feng Shi; Xiang Jie Liu
2013-01-01
This paper modified the differential evolution (DE) algorithm adaptively to solve the power unit coordinated control (PUCC) problem. It was modified in two aspects: 1) a uniform initialization, which was controlled and regulated by a zone factor (m), 2) a regular mutation process, to develop an effective searching process and improve the convergence of the basic DE algorithm. A numerical case study was employed to verify the performance of our proposed uniform differential evolution (UDE) a...
Immunity clone algorithm with particle swarm evolution
LIU Li-jue; CAI Zi-xing; CHEN Hong
2006-01-01
Combining the clonal selection mechanism of the immune system with the evolution equations of particle swarm optimization, an advanced algorithm was introduced for functions optimization. The advantages of this algorithm lies in two aspects.Via immunity operation, the diversity of the antibodies was maintained, and the speed of convergent was improved by using particle swarm evolution equations. Simulation programme and three functions were used to check the effect of the algorithm. The advanced algorithm were compared with clonal selection algorithm and particle swarm algorithm. The results show that this advanced algorithm can converge to the global optimum at a great rate in a given range, the performance of optimization is improved effectively.
Results of Evolution Supervised by Genetic Algorithms
Jäntschi, Lorentz; Bălan, Mugur C; Sestraş, Radu E
2010-01-01
A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.
Dichotomous Binary Differential Evolution for Knapsack Problems
Hu Peng
2016-01-01
Full Text Available Differential evolution (DE is one of the most popular and powerful evolutionary algorithms for the real-parameter global continuous optimization problems. However, how to adapt into combinatorial optimization problems without sacrificing the original evolution mechanism of DE is harder work to the researchers to design an efficient binary differential evolution (BDE. To tackle this problem, this paper presents a novel BDE based on dichotomous mechanism for knapsack problems, called DBDE, in which two new proposed methods (i.e., dichotomous mutation and dichotomous crossover are employed. DBDE almost has any difference with original DE and no additional module or computation has been introduced. The experimental studies have been conducted on a suite of 0-1 knapsack problems and multidimensional knapsack problems. Experimental results have verified the quality and effectiveness of DBDE. Comparison with three state-of-the-art BDE variants and other two state-of-the-art binary particle swarm optimization (PSO algorithms has proved that DBDE is a new competitive algorithm.
武富平; 张瑞华
2011-01-01
In recent years the optimisation algorithm has been widely used in wireless sensor network localisation algorithms. Based on an in-depth study on differential evolution algorithm, the authors propose a two-stage localisation algorithm. In the first phase, based on the Euclidean localisation algorithm, they added the idea of distance routing, which is to work with two anchor nodes within two-hop of the unknown node and with any one anchor node which locates two-hop away from the unknown node to calculate the estimated location. In the second phase,they used differential evolution algorithm to perform the iterative optimisation. The proposed algorithm is called the DE-Euclidean localisation algorithm. Simulation results show that,the DE-Euclidean algorithm significantly improves the precision of localisation.%近年来优化算法在无线传感器网络定位算法中得到了广泛应用.在对差分进化算法研究的基础上提出一种二阶段定位算法,第一阶段在Euclidean定位算法的基础上,加入了距离路由思想,通过与未知节点距离两跳之内的两个锚节点和距离两跳之外的任一锚节点利用Euclidean算法来计算估计位置.第二阶段利用差分进化算法进行迭代寻优,提出的新算法称之为DE-Euclidean定位算法.仿真结果表明,DE- Euclidean算法明显提高了定位精度.
王林; 富庆亮; 曾宇容
2011-01-01
针对不确定规划领域中存在的模糊相关机会规划模型，基于群体智能的差分进化算法，设计一种新的求解模糊相关机会规划模型的混合智能算法．该算法基于粒子群优化算法对差分进化算法进行改进，并运用模糊模拟技术对模糊相关机会规划模型进行分析和数值求解，无需像传统的基于遗传算法的混合智能算法需要很长时间并经过复杂的计算才能得到合理的结果．最后，通过实例表明了所提混合智能算法的合理性和有效性．%Aiming at the existing fuzzy dependent-chance programming model in uncertainty planning field and the optimization approach of group intelligence-differential evolution algorithm, a novel hybrid intelligent algorithm for fuzzy dependent-chance programming models is designed. The proposed algorithm uses hybrid differential evolution algorithm improved by particle swarm optimization algorithm and fuzzy simulation to solve this kind of programming problems. It does not need a long time and complex calculations to get reasonable results like the hybrid intelligent algorithm based on traditional genetic algorithm. Numerical examples show the rationality and effectiveness of the proposed hybrid intelligent algorithm.
彭志红; 孙琳; 陈杰
2012-01-01
为了解决无人机在部分未知敌对环境中的低空突防航迹规划问题，提出了一种改进的差分进化算法．该算法的进化模型采用冯·诺伊曼拓扑结构，并对其进行拓展，使种群在进化初期保持多样性，避免进化早期陷入局部最优，而进化后期加快收敛速度．该算法改进了差分进化算子中的变异操作，从而加快算法的收敛速度，快速找到多目标优化问题的最优解；同时，采用将绝对笛卡儿坐标和相对极坐标相结合的编码方式以提高搜索效率．将该算法用于无人机在线航迹规划仿真实验，并和未改进的算法结果作比较，验证了该算法的有效性．%An improved differential evolution algorithm was proposed for solving the online path planning problem of unmanned aerial vehicle （UAV） low-altitude penetration in partially known hostile environments. The algorithm adopts von Neumann topology and improves its structure to maintain the diversity of the population, prevent the population from falling into local optima in the early evolution and speed up the convergence rate in the later evolution as well. The mutation operator of differential evolution is improved to speed up the convergence rate of the algorithm, so that the optimal solution of the multi-objective optimization problem can be found quickly; the coding method combined the absolute Cartesian coordinates with the relative polar coordinates is used to improve the searching efficiency. The simulation experiment of online path planning for UAV low-altitude penetration shows that the proposed algorithm has a better performance than the unimproved differential evolution algorithm.
Differential AR algorithm for packet delay prediction
无
2006-01-01
Different delay prediction algorithms have been applied in multimedia communication, among which linear prediction is attractive because of its low complexity. AR (auto regressive) algorithm is a traditional one with low computation cost, while NLMS (normalize least mean square) algorithm is more precise. In this paper, referring to ARIMA (auto regression integrated with moving averages) model, a differential AR algorithm (DIAR) is proposed based on the analyses of both AR and NLMS algorithms. The prediction precision of the new algorithm is about 5-10 db higher than that of the AR algorithm without increasing the computation complexity.Compared with NLMS algorithm, its precision slightly improves by 0.1 db on average, but the algorithm complexity reduces more than 90%. Our simulation and tests also demonstrate that this method improves the performance of the average end-to-end delay and packet loss ratio significantly.
Quantum algorithms for solving linear differential equations
Berry, Dominic W
2010-01-01
Linear differential equations are ubiquitous in science and engineering. Quantum computers can simulate quantum systems, which are described by homogeneous linear differential equations that produce only oscillating terms. Here we extend quantum simulation algorithms to general inhomogeneous linear differential equations, which can include exponential terms as well as oscillating terms in their solution. As with other algorithms of this type, the solution is encoded in amplitudes of the quantum state. The algorithm does not give the explicit solution, but it is possible to extract global features of the solution.
姜凯; 左风朝
2012-01-01
为解决文档聚类问题,提出一种基于差分进化的聚类算法,通过把文档聚类问题建模为优化问题,对聚类准则函数进行优化,来寻找初始最优聚类中心.在此基础上,进一步提出两种差分进化算法与K均值结合的混合方法,来获得更好的聚类结果.实验表明,与经典K均值算法相比,新提出的两种混合方法能够获得较好的聚类质量.%This paper proposes a novel differential evolution clustering algorithm for solving Web document clustering.First,by modeling Web document clustering problem as an optimization problem,the clustering criterion function is optimized aiming at finding the promising initial centroids.Then,K-means and differential evolution clustering algorithm are hybridized in two ways to achieve better clustering performance.Compared with K-means algorithm,experimental results reveal that the two proposed hybrid approaches can acquire better and higher clustering quality.
WANG Shunjin; ZHANG Hua
2006-01-01
The problem of preserving fidelity in numerical computation of nonlinear ordinary differential equations is studied in terms of preserving local differential structure and approximating global integration structure of the dynamical system.The ordinary differential equations are lifted to the corresponding partial differential equations in the framework of algebraic dynamics,and a new algorithm-algebraic dynamics algorithm is proposed based on the exact analytical solutions of the ordinary differential equations by the algebraic dynamics method.In the new algorithm,the time evolution of the ordinary differential system is described locally by the time translation operator and globally by the time evolution operator.The exact analytical piece-like solution of the ordinary differential equations is expressd in terms of Taylor series with a local convergent radius,and its finite order truncation leads to the new numerical algorithm with a controllable precision better than Runge Kutta Algorithm and Symplectic Geometric Algorithm.
许欢; 温洁嫦
2013-01-01
It researches the unidirectional logistics distribution vehicle routing problem with no time win-dows, which constrains the vehicle capacity , the longest distance and the full load vehicle .The solution must ensure the non-full load is the least , and the total distance is the shortest .A mathematical model for multi-objective optimization of the logistics distribution vehicle routing was proposed , and a differential e-volution algorithm was presented for this problem .In the algorithm , an appropriate encoding method was presented .The advantage of the proposed algorithm was proved by simulation , based on Matlab lan-guage .The computational results demonstrate that the differential evolution algorithm is effective .%研究无时限单向物流配送车辆路径问题，主要考虑车辆容量、最大距离等约束，考虑车辆满载情况，以车辆非满载率最小、总的行驶路径最短为目标，提出了该物流配送问题的多目标优化问题的数学模型，运用差分进化算法求解该问题。算法构造了合适的编码方法，应用Matlab语言编程进行实例仿真计算，得到了模型的最优解，验证了算法的有效性。
简平; 邹鹏; 熊伟; 陈治科
2012-01-01
为提高求解多目标优化问题效率,对通用差异演化(GDE)算法及其自适应参数控制问题进行了研究.首先,分析了GDE3算法的编码、交叉、变异、选择等原理和算法流程；然后,利用个体的适应度作为参数调整的依据,并结合一定的调整概率提出一种新的对缩放因子和交叉概率参数自适应控制策略,提高算法的搜索能力；最后,通过典型的多目标函数对自适应控制参数的通用演化算法( selfGDE3)、GDE3和非劣分层遗传算法2(NSGA-Ⅱ)的性能进行比较分析,结果表明,selfGDE3算法具有良好的搜索性能.%In order to solve the multi-objective optimization problem efficiently, this paer researched on generalized differential evolution algorithm and the method of adaptive controlling parameter. Firstly, it analyzed the principle and process of generalized differential evolution algorithm 3,including coding,crossover,mutation. Secondly, the algorithm put forward adaptive controlling strategy to crossover and mutation parameter based on the fitness of individual and the adjusting probability, which improved the performance of algorithm. Finally, it compared the performance of selfGDE3 , GDE3 and NSGA- II through testing some benchmark functions. The results show the feasibility of selfGDE3.
杜文莉; 周仁; 赵亮; 钱锋
2012-01-01
一般的神经网络的结构是固定的，在实际应用中容易造成冗余连接和高计算成本。该文采用了协同量子差分进化算法（cooperative quantum differential evolution algo-rithm，CQGADE）以同时优化神经网络的结构和参数，即采用量子遗传算法（quantum genetic algorithm，QGA）来优化神经网络的结构和隐层节点数，采用差分算法来优化神经网络的权值。训练后的神经网络的连接开关能有效删除冗余连接，算法的量子概率幅编码和协同机制可以提高神经网络的学习效率、逼近精度和泛化能力。仿真实验结果表明：用训练后的神经网络预测太阳黑子和蒸汽透平流量具有更好的预测精度和鲁棒性。%Neural network structures are fixed, which results in redundant connections and high computing costs. This paper presents a cooperative quantum differential evolution algorithm （CQGADE） that simultaneously optimizes the neural network structure and parameters. The quantum genetic algorithm is used to optimize the neural network structure and-the number of hidden nodes, while the differential evolution algorithm is used to optimize the neural network weights. This reduces redundant neural network structures, while the amplitude-based coding method and a cooperation mechanism improve the learning efficiency, approximation accuracy, and generalization. Simulations show that this algorithm has better prediction accuracy and robustness for predicting the number of sunspots and the flow of steam turbine.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kai Yit Kok
Full Text Available The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Heterogeneous Differential Evolution for Numerical Optimization
Hui Wang
2014-01-01
Full Text Available Differential evolution (DE is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE to balance exploration and exploitation. In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool. Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems. The results show that heterogeneous DE achieves promising performance on a majority of the test problems.
Aerodynamic Shape Optimization Using Hybridized Differential Evolution
Madavan, Nateri K.
2003-01-01
An aerodynamic shape optimization method that uses an evolutionary algorithm known at Differential Evolution (DE) in conjunction with various hybridization strategies is described. DE is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Various hybridization strategies for DE are explored, including the use of neural networks as well as traditional local search methods. A Navier-Stokes solver is used to evaluate the various intermediate designs and provide inputs to the hybrid DE optimizer. The method is implemented on distributed parallel computers so that new designs can be obtained within reasonable turnaround times. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. (The final paper will include at least one other aerodynamic design application). The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated.
Paired Comparisons-based Interactive Differential Evolution
Takagi, Hideyuki
2009-01-01
We propose Interactive Differential Evolution (IDE) based on paired comparisons for reducing user fatigue and evaluate its convergence speed in comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User interface and convergence performance are two big keys for reducing Interactive Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, users of the proposed IDE and tournament IGA do not need to compare whole individuals each other but compare pairs of individuals, which largely decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate another factor, IEC convergence performance, using IEC simulators and show that our proposed IDE converges significantly faster than IGA and tournament IGA, i.e. our proposed one is superior to others from both user interface and convergence performance points of view.
Differential Evolution for Many-Particle Adaptive Quantum Metrology
Lovett, N.B.; Crosnier, C.; Perarnau- Llobet, M.; Sanders, B.
2013-01-01
We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods
Quantum Adiabatic Evolution Algorithms with Different Paths
Farhi, E; Gutmann, S; Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam
2002-01-01
In quantum adiabatic evolution algorithms, the quantum computer follows the ground state of a slowly varying Hamiltonian. The ground state of the initial Hamiltonian is easy to construct; the ground state of the final Hamiltonian encodes the solution of the computational problem. These algorithms have generally been studied in the case where the "straight line" path from initial to final Hamiltonian is taken. But there is no reason not to try paths involving terms that are not linear combinations of the initial and final Hamiltonians. We give several proposals for randomly generating new paths. Using one of these proposals, we convert an algorithmic failure into a success.
Image segmentation using an improved differential algorithm
Gao, Hao; Shi, Yujiao; Wu, Dongmei
2014-10-01
Among all the existing segmentation techniques, the thresholding technique is one of the most popular due to its simplicity, robustness, and accuracy (e.g. the maximum entropy method, Otsu's method, and K-means clustering). However, the computation time of these algorithms grows exponentially with the number of thresholds due to their exhaustive searching strategy. As a population-based optimization algorithm, differential algorithm (DE) uses a population of potential solutions and decision-making processes. It has shown considerable success in solving complex optimization problems within a reasonable time limit. Thus, applying this method into segmentation algorithm should be a good choice during to its fast computational ability. In this paper, we first propose a new differential algorithm with a balance strategy, which seeks a balance between the exploration of new regions and the exploitation of the already sampled regions. Then, we apply the new DE into the traditional Otsu's method to shorten the computation time. Experimental results of the new algorithm on a variety of images show that, compared with the EA-based thresholding methods, the proposed DE algorithm gets more effective and efficient results. It also shortens the computation time of the traditional Otsu method.
一种自适应差分进化算法在煤气分配中的应用%An application of adaptive differential evolution algorithm in gas distribution
孙良旭; 曲殿利
2016-01-01
By analyzing the gas using characteristics and constrains of the main energy consumption equipment in the process of gas distribution for iron and steel production,a mathematical model for gas distribution was formulated whose objective was to minimize the sum including consumption cost,emission cost and benefit from electric power generation.An adaptive hybrid differential evolution algorithm (AHDE)was proposed to solve the model which took advantage of the superiority ability in the aspect of path selection of ant colony optimization algorithm,and designed the selection mechanism of differential strategy,and improved the algorithm performance.The effective-ness of the proposed algorithm was verified by the simulation test based on the practical production data with the standard test functions.%通过分析钢铁生产过程中主要能耗设备的煤气使用特征及煤气分配过程中的约束,建立了以煤气消耗成本、放散成本及发电收益之和最小化的数学模型,提出了一种自适应混合差分进化算法(AHDE)进行求解.算法利用蚁群算法在路径选择能力方面的优势,构造算法中差分策略选择机制提高算法的性能.通过对标准测试函数和对实际生产数据的仿真实验,验证算法的有效性.
Exponentially Convergent Algorithms for Abstract Differential Equations
Gavrilyuk, Ivan; Vasylyk, Vitalii
2011-01-01
This book presents new accurate and efficient exponentially convergent methods for abstract differential equations with unbounded operator coefficients in Banach space. These methods are highly relevant for the practical scientific computing since the equations under consideration can be seen as the meta-models of systems of ordinary differential equations (ODE) as well as the partial differential equations (PDEs) describing various applied problems. The framework of functional analysis allows one to obtain very general but at the same time transparent algorithms and mathematical results which
黄健
2013-01-01
通过改变初始种群的产生过程,并对目标个体的变异、交叉操作过程进行改进,包括变异修复、变异个体取舍、中间个体处理、控制参数(试验时间)调整,给出了改进差分进化算法.通过试验详细分析了各个控制参数对算法寻优性能的影响.%By changing the process for generating initial population,four aspects of improvement were made on mutation operator and crossover operator for individuals,which included mutation operator repairing,mutation individuals choosing,temporary individuals processing and adjusting control parameter(test times).An improved differential evolution algorithm was proposed,and the effects of all kinds of parameters on the performance of improved differential evolution was analyzed in detail through experiment.
王君
2013-01-01
研究了带时间窗的车辆路径问题(Vehicle Routing Problem with Time Windows,VRPTW),建立了数学模型,并设计了求解VRPTW的离散差分进化混合算法.算法采用随机车辆配载方法构造初始解,并通过构造新的变异和交叉算子进行改进.进一步,利用插入可行邻域和2-Opt可行邻域两种搜索可行解的邻域结构,引入禁忌搜索进一步进行局部搜索以提高算法的寻优能力.实验结果表明该算法是求解VRPTW的一种有效方法.%The Vehicle Routing Problem with Time Windows(VRPTW) is addressed in this paper.A mathematical model is built and a discrete differential evolution hybrid algorithm for VRPTW is designed.In the algorithm, the population is initialized by a random vehicle loading method,and improved by new discrete mutation and crossover operators.Moreover,the tabu search is introduced to enhance the local searching capability of the algorithm, in which insertion and 2-Opt feasible neighborhoods are used to search feasible solutions.Experimental results show the effectiveness of the proposed approach for the VRPTW.
Design of Test Wrapper Scan Chain Based on Differential Evolution
Aijun Zhu
2013-08-01
Full Text Available Integrated Circuit has entered the era of design of the IP-based SoC (System on Chip, which makes the IP core reuse become a key issue. SoC test wrapper design for scan chain is a NP Hard problem, we propose an algorithm based on Differential Evolution (DE to design wrapper scan chain. Through group’s mutation, crossover and selection operations, the design of test wrapper scan chain is achieved. Experimental verification is carried out according to the international standard benchmark ITC’02. The results show that the algorithm can obtain shorter longest wrapper scan chains, compared with other algorithms.
Two-Stage Eagle Strategy with Differential Evolution
Yang, Xin-She
2012-01-01
Efficiency of an optimization process is largely determined by the search algorithm and its fundamental characteristics. In a given optimization, a single type of algorithm is used in most applications. In this paper, we will investigate the Eagle Strategy recently developed for global optimization, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimization problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to 10 in many applications.
傅阳光; 周成平; 胡汉平
2012-01-01
To investigate the path of unmanned aerial vehicle ( UA) in ocean environment, a method based on the differential evolution(DE) is proposed. It pretreats the planning environment and takes all islands as threatened areas, the path planning problem is simplified as a two-dimensional planning problem. A real number coding is used to represent the candidate paths, and a mathematical model of path cost is established. The performance of differential evolution algorithm is compared with that of genetic algorithm ( GA) and particle swarm optimization ( PSO) in terms of path quality, robustness and convergence speed. The experimental results demonstrate that the proposed method is able to generate a safe and flya-ble path for UAV in a complex ocean environment.%为研究海洋环境下的无人飞行器(UAv)航迹规划问题,提出了一种基于差分进化算法(DE)的航迹规划方法.该方法通过对规划环境进行预处理将岛屿处理成地形威胁区,使问题简化为二维平面规划.采用实数编码方式对航迹进行编码,建立了航迹代价函数的数学模型,从航迹质量、算法稳定性和收敛速度3个方面比较了DE与遗传算法(GA)和粒子群优化算法(PSO)的性能.仿真实验结果表明,所提方法能在复杂的海洋环境下为飞行器规划出一条安全的可飞航迹.
吴万旭; 牛钰莹; 刘甜甜; 赵全超
2016-01-01
针对因嵌入水印而造成图像视觉损失的问题,提出了一种基于差分进化的DWT-SVD数字水印盲检测算法。首先对水印图像进行置乱加密处理,其次对需要嵌入水印的图像进行离散小波变换,得到其低频、中频、次高频、高频四个子代,再次将置乱后的水印按照一定的方法嵌入到载体图像对应的奇异值矩阵S中,完成水印嵌入。最后,利用差分进化算法修改奇异值分解中的酉阵U和V,以弥补因添加水印造成的视觉损失。实验结果表明,该算法在保证水印鲁棒性情况下,使嵌入水印后的图像质量得到了有效提高。此外该算法在水印提取阶段不需要载体图像的参与,实现了水印的盲检测。%Aiming at the problem of visual loss caused by watermark embedding, a DWT-SVD digital wa-termarking blind detection algorithm based on differential evolution is proposed. Firstly, Arnold scrambling pretreatment is done on watermark image, and the host image decomposed by DWT,thus generating+ four different bands sub-maps, including the low frequency, intermediate frequency, sub-high frequency and high frequency. Then, the watermark image is embedded in S matrix after decomposition by singular value in the watermark of each block. Finally, a differential evolution algorithm is applied to modifying the U and V matrix, thus to remedy the visual loss caused by embedding in S matrix. Experimental results show that this algorithm could effectively improve image quality after watermarking being embadded while main-taining the robustness. In addition,no carrier image is needed in watermarking extraction stage, this indi-cates that the proposed algorithm could achieve blind detection of watermarking.
Algorithmic Differentiation for Calculus-based Optimization
Walther, Andrea
2010-10-01
For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.
Quantum Adiabatic Evolution Algorithms versus Simulated Annealing
Farhi, E; Gutmann, S; Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam
2002-01-01
We explain why quantum adiabatic evolution and simulated annealing perform similarly in certain examples of searching for the minimum of a cost function of n bits. In these examples each bit is treated symmetrically so the cost function depends only on the Hamming weight of the n bits. We also give two examples, closely related to these, where the similarity breaks down in that the quantum adiabatic algorithm succeeds in polynomial time whereas simulated annealing requires exponential time.
胡春平; 颜学峰
2009-01-01
A new version of differential evolution(DE)algorithm,in which immurle concepts and methods are applied to determine the parameter setting.named immune self-adaptive difierential evolution(ISDE),iS proposed to improve the performance of the DE algorithm.During the actual operation.ISDE seeks the optimal parameters arising from the evolutionary process.which enable ISDE to alter the algorithm for different optimization problems and improve the performance Of ISDE bv the control parameters'self-adaptation.The performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm and other well-known self-adaptive DE algorithms.The experiments conducted show that the ISDE clearly outperforms the othcr DE algorithms in all benchmark functions.Furthermore.ISDE iS applied to develop the kinetic model for homogeneous mercury (Hg) oxidation in tlue gas,and satistactory results are obtained.
Algorithm refinement for stochastic partial differential equations.
Alexander, F. J. (Francis J.); Garcia, Alejandro L.,; Tartakovsky, D. M. (Daniel M.)
2001-01-01
A hybrid particle/continuum algorithm is formulated for Fickian diffusion in the fluctuating hydrodynamic limit. The particles are taken as independent random walkers; the fluctuating diffusion equation is solved by finite differences with deterministic and white-noise fluxes. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass conservation. This methodology is an extension of Adaptive Mesh and Algorithm Refinement to stochastic partial differential equations. A variety of numerical experiments were performed for both steady and time-dependent scenarios. In all cases the mean and variance of density are captured correctly by the stochastic hybrid algorithm. For a non-stochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except within the particle region, far from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
刘自发; 刘刚; 刘幸
2013-01-01
针对计及需求响应计划的分布式电源系统经济运行问题，建立了一种考虑燃料费用和运行管理费用、电网交互费用、可中断负荷停运补偿费用和需求侧电费支出费用等的综合优化数学模型。同时为实现能量的有效互动，优化模型中加入了需求响应模型。提出一种量子差分进化算法对优化模型进行求解。该算法基于差分进化思想，采用量子计算中的并行、坍缩等特性，并在选择策略中考虑量子位的概率特性，具有较强的鲁棒性和全局搜索能力。通过算例分析证明文中提出的模型和算法科学、有效。% In allusion to economic operation of distributed generation (DG) system considering demand response, a comprehensive optimal mathematical model, in which the fuel cost and the cost of operation and management, the interaction cost, the compensation cost for the outage of interruptible loads and electricity cost of demand side are taken into account, is established. Meanwhile, to implement effective interaction of energy a demand side response model is added to the established optimal model. A kind of quantum differential evolution (QDE) algorithm is proposed to solve the established optimal model. Based on the idea of differential evolution and using parallel and collapse properties of the quantum calculation theory and considering probabilistic nature of quantum bit in the selection strategy, the proposed algorithm possesses strong robustness and global searching ability. Calculation results of a microgrid containing different kinds of DGs show that the established coordinated optimal dispatching model and the proposed algorithm are reasonable and effective.
聂宏展; 郑鹏飞; 于婷; 刘小满
2013-01-01
Differential evolution (DE) algorithm is a real number coding heuristic and global optimization of performance algorithm based on population. But the searching strategy of DE algorithm is too unitary and the local searching capability is very poor, so more mutation strategies and local optimization strategy can raise more global and local searching ability and reduce the searching time, which adapted to solving large-scale transmission network planning. Taking line investment costs, network loss cost, the over-load cost of normal operation and transmission corridor cost as objectives, and through the results of Garver -6 system and 18 node system, it can not only prove the DE algorithm and model correctness and effectiveness in transmission network planning, but also can demonstrate that the algorithm has high computing speed and convergence, which lay the foundation to the further improving of DE algorithm.%差分进化(DE)算法是一类基于种群的、具有全局优化性能的、通过实数编码的启发式算法.但差分算法搜索策略过于单一,局部搜索能力差,因此通过增加多策略变异和局部寻优策略来提升全局和局部搜索能力,同时降低搜索时间,使其适应于求解大规模输电网规划问题.采用基于线路投资费用、网损费用、正常运行时的过负荷费用及输电走廊费用的输电网规划模型,通过对Garver-6系统和18节点系统的计算,不仅验证了算法及模型应用于输电网规划的正确性和有效性,而且验证了算法具有很高的计算速度和收敛性,为DE算法的进一步改进应用打下基础.
康国胜; 刘建勋; 唐明董; 徐宇
2011-01-01
QoS全局最优动态Web服务选择是服务组合中的一个难题.基于差异演化算法,设计一种用于解决该问题的DE-GODSS算法.算法的主要思想是将问题表示为一个带QoS约束的多目标服务组合优化问题,通过理想点的方法将多目标向单目标转化,然后利用差异演化算法的智能优化原理进行算法设计及求解,最终产生一组满足约束条件的优化服务组合流程集.理论分析证明DE-GODSS算法的时间复杂度优于已有的多目标遗传算法,且实验结果表明该算法的收敛速度优于已有的多目标遗传算法.%Dynamic Web service selection with global QoS optimization is a critical issue in Web service composition. In order to solve the problem; based on the algorithm of differential evolution (DE); this paper proposes the DE-GODSS (global optimal of dynamic Web service selection based on DE) algorithm. The basic idea of the algorithm is to transform the original Web service selection problem into a multi-objective service composition optimization with global QoS constraints; which is further transformed into a single-object by using the method of ideal point. Then; the theory of intelligent optimization of DE is exploited to produce a set of optimal services composition process with QoS constraints. Theoretical analysis and experimental results indicate the feasibility and efficiency of this algorithm; and the time complexity and convergence rate of our algorithm are much better than that of the multi-objective genetic algorithm used in prior work.
An algorithm of computing inhomogeneous differential equations for definite integrals
Nakayama, Hiromasa; Nishiyama, Kenta
2010-01-01
We give an algorithm to compute inhomogeneous differential equations for definite integrals with parameters. The algorithm is based on the integration algorithm for $D$-modules by Oaku. Main tool in the algorithm is the Gr\\"obner basis method in the ring of differential operators.
Efficient receiver tuning using differential evolution strategies
Wheeler, Caleb H.; Toland, Trevor G.
2016-08-01
Differential evolution (DE) is a powerful and computationally inexpensive optimization strategy that can be used to search an entire parameter space or to converge quickly on a solution. The Kilopixel Array Pathfinder Project (KAPPa) is a heterodyne receiver system delivering 5 GHz of instantaneous bandwidth in the tuning range of 645-695 GHz. The fully automated KAPPa receiver test system finds optimal receiver tuning using performance feedback and DE. We present an adaptation of DE for use in rapid receiver characterization. The KAPPa DE algorithm is written in Python 2.7 and is fully integrated with the KAPPa instrument control, data processing, and visualization code. KAPPa develops the technologies needed to realize heterodyne focal plane arrays containing 1000 pixels. Finding optimal receiver tuning by investigating large parameter spaces is one of many challenges facing the characterization phase of KAPPa. This is a difficult task via by-hand techniques. Characterizing or tuning in an automated fashion without need for human intervention is desirable for future large scale arrays. While many optimization strategies exist, DE is ideal for time and performance constraints because it can be set to converge to a solution rapidly with minimal computational overhead. We discuss how DE is utilized in the KAPPa system and discuss its performance and look toward the future of 1000 pixel array receivers and consider how the KAPPa DE system might be applied.
Fan, Zhun; Liu, Jinchao; Sørensen, Torben
2009-01-01
This paper introduces an improved differential evolution (DE) algorithm for robust layout synthesis of microelectromechanical system components subject to inherent geometric uncertainties. A case study of the layout synthesis of a combdriven microresonator shows that the approach proposed in this...
Flow Shop Scheduling using Differential Evolution of CRM
Zuzana Čičková
2010-12-01
Full Text Available The article is focused on the application of differential evolution for solving flow shop problem that belongs to the class of scheduling problems. The scheduling problems arise in diverse areas such as manufacturing systems, production planning, computer design, logistics etc.. Only in very special cases there exist exact polynomial algorithms to reach optimal solution. In most of the other cases, its computational complexity is NP-hard and it seems to be desirable to employ some heuristics to solve it. Nowadays, the use of some methods that are based on metaheuristics is a popular way. One of them is a differential evolution, which belongs to the class of evolutionary techniques. The application of evolutionary algorithms to NP-hard problems generally requires a special modification of these algorithms; therefore the main object of the work is to adapt a canonical version of differential evolution for solving flow shop problem. The effectiveness of the proposed approach is compared with other evolutionary techniques known from the already published results. The available instance of flow shop Car and Rec are used for comparison.
Flexible Ligand Docking Using Differential Evolution
Thomsen, René
2003-01-01
evolution algorithm (DE) is applied to the docking problem using the AutoDock program. The introduced DockDE algorithm is compared with the Lamarckian GA (LGA) provided with AutoDock, and the DockEA previously found to outperform the LGA. The comparison is performed on a suite of six commonly used docking...... problems. In conclusion, the introduced DockDE outperformed the other algorithms on all problems. Further, the DockDE showed remarkable performance in terms of convergence speed and robustness regarding the found solution....
王丛佼; 王锡淮; 陈国初; 陈建民; 陈晶
2016-01-01
针对潮流能发电机组布局依赖经验法、缺乏自主优化而导致微观选址难度大的问题，提出了一种基于差分进化算法并结合流场仿真模型的微观选址优化方法。通过对流场的有限元仿真，获取选址区域在原始状态下的流速分布；在充分考虑地形、潮汐和尾流效应等因素的前提下，以仿真结果为依据，以潮流发电机组群输出功率最大为优化目标，以机组间距及水深限制为约束，建立微观选址优化的数学模型；采用差分进化算法进行模型求解，同时为更利于最优解的搜索，提出了对其变异算子及参数设置的改进策略。以龟山水道为例进行微观选址优化计算，验证了所提模型的准确性与算法的高效性。%A micrositing method based on differential evolution algorithm combined with flow field simulation model is proposedfor solving the problems that tidal generator layout depends on experience without optimization technology. This method firstly employsthe finite element simulation to obtain the original flow velocity distribution. Then a mathematical model based on the simulation results is built in full consideration of topography,tide,and wake effect. The objective function is the maximization of the whole tidal turbines’power outputand the free variables are the turbines’coordinates which are subject to the minimum distance conditions and the depth conditions. In order to solve this model,an improved differential evolution algorithmis proposed, in whichthe adaptive mutation operator and parameters increase the global search ability. The micrositing of tidal turbines is performed on the Guishan waterway. Then the optimized results demonstrate the accurateness of the proposed model and the effectiveness of the solving algorithm.
郑建国; 王翔
2011-01-01
Aiming at the problem of constrainted optimization,a novel diversity composite differential evolution algorithm was proposed.A multi-member mechanism based on composite trial vector generation strategies was designed by new algorithm to make full use of the information of a member in the population.Two groups of candidate submembers were generated from one member in the population,and each group of candidate submember included three different individual members which generated by three different trial vector generation strategies respectively.In addition,a novel dispersed mechanism was designed to utilize the information in infeasible solutions.When selecting occurred between the current member and optimum candidate submember,the comparison rules based on objective functions and DEB comparison rules were selected by using probability SR and 1-SR respectively.Simultaneously,SR was decreased gradually to 0 with the evolution.The experimental results indicated that the new algorithm exhibited obvious superiority in solving the most difficult optimization problems of g02,g10,and g13.%针对约束优化问题,提出一种新颖的多成员组合差分进化算法。新算法为了充分利用种群中一个成员的信息,设计了基于组合测试向量产生策略的多成员机制。该机制针对种群中一个成员产生两组候选子成员,每组候选子成员都包括利用三种变异策略分别产生的三个不同的个体。为了利用种群中不可行解的信息,新算法还设计了一种新颖的分散机制。当对最优候选子个体与当前个体进行选择操作时,该机制分别利用概率SR和1-SR选择基于目标函数的比较准则和DEB比较准则,同时概率SR随着进化代数的增加逐渐减小到0。实验结果表明,新算法在三个最难优化的问题g02,g10和g13上具有明显优势。
徐斌; 陶莉莉; 程武山
2016-01-01
A self-adaptive differential evolution algorithm with multiple strategies (SMDE) was proposed to overcome premature or localized optimization of differential evolution (DE) as a result of fixed parameter settings. Based on basic framework of classical DE, the first step in SMDE was to create a candidate set of mutation strategy, scale factor (F) and crossover rate (CR). In the followed searching process, mutation strategy,F andCR for each individual variable in next evolutionary generation were determined self-adaptively from the corresponding candidate set according to knowledge learnt from previous searches, so that proper mutation strategies and control parameters could be set at various evolution stages. Compared to other famous DE variants on optimizing 10 routine standard testing problems, SMDE had better search precision and faster convergence rate. Moreover, study on estimation of uncertain parameters in dynamic process systems of chemical engineering showed that SMDE could effectively solve engineering optimization challenges.%针对差分进化算法由于固定参数设置而易早熟或陷入局部最优的问题，提出了一种自适应多策略差分进化算法（SMDE）。该方法以基本差分进化为框架，首先引入一个变异策略候选集合，一个缩放因子候选集合和一个交叉参数候选集合，然后在搜索过程中，以过去的搜索信息为基础，自适应地为下一时刻进化群体中的每个个体从候选集合中选择一组合适的变异策略和控制参数，以便在不同的进化时刻设置合适的变异策略和控制参数。对10个常用的标准测试函数进行优化计算，并与其他算法的结果进行了比较，实验结果表明，SMDE具有较好的搜索精度和更快的收敛速度。将SMDE用于化工过程动态系统不确定参数估计问题，实验结果表明该算法能较好地处理实际工程优化问题。
辛斌; 陈杰
2011-01-01
Improving the performance of optimization algorithms has long been an important pursuit of researchers. It is a typical design idea and paradigm to combine different optimizers for a synergy of their complementary advantages. Regarding two kinds of population-based evolutionary algorithms, the particle swarm optimizer (PSO) and the differential evolution (DE), we present a systematic and comprehensive survey on their hybrids (DEPSOs) in the literature and propose a taxonomy of hybridization strategies. Based on the taxonomy, we make a classification of different DEPSOs and analyze their similarities and differences. We also point out some new directions for future research and provide several guidelines for hybridization design of optimizers.%优化算法的性能改进长期以来一直是算法研究者们追求的一个重要目标,对不同算法进行混合以期利用算法的互补优势来获得性能更优异的算法代表了一类典型的设计思想.针对两类基于群体演化的优化算法——粒子群优化(PSO)与差分进化(DE)算法,对基于二者的各种混合算法(DEPSO)进行了系统而全面的综述,并在此基础上提出了一种混合策略分类方法,对现有的各种典型DEPSO算法进行了分类,比较了各种混合策略的异同,并指出了一些新的研究方向和混合设计原则.
杨悦; 袁超; 李国庆
2011-01-01
Reactive power optimization is the basis of stability and economy of power system.The neighborhood topology cultural differential evolution algorithm is proposed.The premature convergence and easy to fall into local optimal solution of the Cultural differential evolution algorithm are improved.The algorithm is the first time applied to reactive power optimization,and the model of reactive power optimization based on the algorithm is established.The neighborhood topology cultural differential evolution algorithm is a directly and randomly searching method.The study shows that the algorithm can quickly obtain the global optimal solution,have a good property of global convergence,and meet the requirements for reactive power optimization goals.The algorithm of reactive power optimization is on a check with IEEE 30 buses system,and is analyzed with common cultural differential evolution algorithm.The results of simulation shows that the neighborhood topology cultural differential evolution algorithm has the better ability for optimization.%提出了求解无功优化问题的一种新算法——基于邻域拓扑文化差分进化算法。将邻域拓扑结构纳入了文化差分进化算法,改进了文化差分进化算法过早收敛,易于陷入局部最优解的问题。并首次将该算法应用到无功优化问题中,使其能迅速获得全局优化解,具有很好的全局收敛性能和更好的优化能力。最后,将该算法在IEEE 30节点系统上进行了无功优化问题的求解,并与应用普通文化差分进化算法的结果进行了比较分析。仿真结果验证了基于邻域拓扑文化差分进化算法在无功优化应用中的有效性。
刘述木; 杨建; 陈跃
2016-01-01
As the problem of the high complexity of 3D face recognition and 2D face recognition not providing granular clues, this paper proposed a fully automatic 3D facial expression recognition algorithm.It provided more clues than that of 2D face recognition and reduced the computational complexity at the same time.Firstly,it transformed 3D face into a 2D plane by con-formal mapping,retaining the changing of facial clues.Secondly,it proposed an optimization algorithm based on differential e-volution (DE)algorithm to improve the recognition efficiency,while extracting the best facial feature set and classification pa-rameters,and speed up robust features (SURF)described all the expected facial feature points.Experimental results on the data sets of Bosphorus,FRGC v2 and gathered face data sets show that the proposed algorithm solves high computational com-plexity of 3D face recognition and low clues of 2D face recognition.This algorithm greatly reduces the cost without lowering the recognition performance,compared to several more advanced 3D face recognition algorithm,the algorithm achieves better reco-gnition results,expecting to be applied to commercial face recognition systems.%针对三维人脸识别的高复杂度和二维人脸识别无法提供粒状线索的问题，提出一种全自动3D 人脸表情识别算法，该算法主要是提供比2D 人脸识别更多的线索，同时降低计算复杂度。通过保角映射将3D 人脸转换到2D 平面，保留了面部变化的线索，提出了基于优化算法的差分进化（DE）算法用于提高识别效率，同时提取最优人脸特征集和分类器参数，加速鲁棒特征池描述了所有预期的人脸特征点。在博斯普鲁斯、FRGC v2及笔者搜集的人脸数据集上的实验结果表明，算法解决了三维人脸识别的高计算复杂度和二维人脸识别的线索低的问题，并在不降低识别性能的前提下大大地节约了成本，相比几种较为先进的三
Differential evolution with ranking-based mutation operators.
Gong, Wenyin; Cai, Zhihua
2013-12-01
Differential evolution (DE) has been proven to be one of the most powerful global numerical optimization algorithms in the evolutionary algorithm family. The core operator of DE is the differential mutation operator. Generally, the parents in the mutation operator are randomly chosen from the current population. In nature, good species always contain good information, and hence, they have more chance to be utilized to guide other species. Inspired by this phenomenon, in this paper, we propose the ranking-based mutation operators for the DE algorithm, where some of the parents in the mutation operators are proportionally selected according to their rankings in the current population. The higher ranking a parent obtains, the more opportunity it will be selected. In order to evaluate the influence of our proposed ranking-based mutation operators on DE, our approach is compared with the jDE algorithm, which is a highly competitive DE variant with self-adaptive parameters, with different mutation operators. In addition, the proposed ranking-based mutation operators are also integrated into other advanced DE variants to verify the effect on them. Experimental results indicate that our proposed ranking-based mutation operators are able to enhance the performance of the original DE algorithm and the advanced DE algorithms.
Simulation research on control algorithm of differential pressure casting process
Chai Yan; Jie Wanqi; Yang Bo
2009-01-01
To improve the precision of the filling pressure curve of differential pressure casting controlled with PID controller,the model of differential pressure casting process is established and two pressure-difference control systems using PID algorithm and Dahlin algorithm are separately designed in MATLAB. The scheduled pressure curves controlled with PID algorithm and Dahlin algorithm,respectively,are comparatively simulated in MATLAB.The simulated pressure curves obtained show that the control precision with Dahlin algorithm is higher than that with PID algorithm in the differential pressure casting process,and it was further verified by production practice.
Flexible Ligand Docking Using Differential Evolution
Thomsen, René
2003-01-01
the most favorable energetic conformation among the large space of possible protein-ligand complexes. Stochastic search methods, such as evolutionary algorithms (EAs), can be used to sample large search spaces effectively and is one of the preferred methods for flexible ligand docking. The differential...
The Cellular Differential Evolution Based on Chaotic Local Search
Qingfeng Ding
2015-01-01
Full Text Available To avoid immature convergence and tune the selection pressure in the differential evolution (DE algorithm, a new differential evolution algorithm based on cellular automata and chaotic local search (CLS or ccDE is proposed. To balance the exploration and exploitation tradeoff of differential evolution, the interaction among individuals is limited in cellular neighbors instead of controlling parameters in the canonical DE. To improve the optimizing performance of DE, the CLS helps by exploring a large region to avoid immature convergence in the early evolutionary stage and exploiting a small region to refine the final solutions in the later evolutionary stage. What is more, to improve the convergence characteristics and maintain the population diversity, the binomial crossover operator in the canonical DE may be instead by the orthogonal crossover operator without crossover rate. The performance of ccDE is widely evaluated on a set of 14 bound constrained numerical optimization problems compared with the canonical DE and several DE variants. The simulation results show that ccDE has better performances in terms of convergence rate and solution accuracy than other optimizers.
Registration of image feature points using differential evolution
ZHANG Hao; HUANG Zhan-hua; YU Dao-ying
2005-01-01
This paper introduces a robust global nonlinear optimizer-differential evolution(DE),which is a simple evolution algorithm to search for an optimal transformation that makes the best alignment of two sets of feature points.To map the problem of matching into the framework of DE,the objective function is proportional to the registration error which is measured by Hausdorff distance,while the parameters of transformation are encoded in floating-point as the functional variables.Three termination criteria are proposed for DE.A simulation of 2-dimensional point sets and a similarity transformation are presented to compare the robustness and convergence properties of DE with genetic algorithm's (GA).And the registration of an object and its contour model have been demonstrated by using of DE to natural images.
黄伟; 黄婷; 周欢; 王冠男; 崔屹平
2014-01-01
针对微电网静态经济调度忽略了各时段之间内在联系的不足，考虑风电机组、光伏电池以及钠硫电池等不确定性因素对经济调度的影响，以微电源出力和微电网运行成本最小为目标函数，建立了微电网动态经济调度模型。采用VC++编制了利用改进微分进化算法的微电网动态经济调度程序，通过改变动态交叉因子，提高了算法的收敛速度和防止陷入局部最优的能力。根据微电网算例结构的特点，分别针对微电网孤网/并网运行情况，制定了微电源的出力原则和运行控制策略。计算结果表明，采用动态优化理论的微电网经济调度较静态调度在成本节约上更具优势，也使得钠硫电池的充放电更具有全局性和实际意义。%In order to deal with the shortcomings of static economical dispatch for microgrid ignoring the inherent link between the intervals,by considering the influence of wind turbines, photovoltaic cells and sodium sulfur battery on economical operation,and with micro power output and power operation minimum cost as objective function,a dynamic economical dispatch model for microgrid is proposed.A VC++ procedure for microgrid dynamic economical dispatch using the improved differential evolution algorithm is compiled.By improving the parameters set of the algorithm,the convergence speed and ability of preventing the algorithm from falling into local optimum are enhanced.According to the characteristics of the microgrid structure of calculation,the grid-isolated/grid-connected mode operation output principle of the micro-source and control strategy of typical microgrid structure are formulated.The results show that the dynamic economical dispatch model for microgrid has more advantages than static scheduling in cost saving,and is more global and practical for sodium sulfur battery charge and discharge.
An Enhanced Differential Evolution with Elite Chaotic Local Search
Zhaolu Guo
2015-01-01
Full Text Available Differential evolution (DE is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL. In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions.
薛羽; 庄毅; 顾晶晶; 常相茂; 王洲
2014-01-01
In line with the proposing process of the self-adaptive discrete differential evolution (SaDDE) algorithm, this research focuses on the strategy selection problem. The strategy pool plays a significant role in the SaDDE algorithm, and there are three issues need to be addressed in designing the strategy pool:(1) how to determine if a candidate solution generating strategy (CSGS) is effective;(2) which CSGSes to choose to constitute the strategy pool;and (3) how to find a suitable size forthe strategy pool. In order to solve these problems, a relative permutation order based scale method (RPOSM) and a RPOSM based analytic hierarchy process (RPOSM-AHP) are proposed in this paper. The experiments are mainly conducted on six test instances (T_INSes) which come from an electronic countermeasure (ECM) simulation experimental platform. 144 different CSGSes are designed, and 144×6 independent experiments are performed to obtain the sort sequences of the CSGSes. The RPOSM and the RPOSM-AHP are adopted to obtain the priority vector of the 144 CSGSes. Sequentially, 16 algorithms with different sizes of strategy pools are constructed and their performance is tested on the six T_INSes. Further, the RPOSM and RPOSM-AHP are employed again to find the suitable pool size for the SaDDE algorithm. Computational comparisons demonstrate that, within fixed number of fitness evaluations (NFE), the SaDDE algorithm can generate better results than its competitors.%根据自适应离散差分进化(SaDDE)算法的提出过程,对算法策略选择问题进行了重点研究。策略池在SaDDE中起着重要作用,策略池的设计面临着3个问题,即：(1)怎样鉴别某个候选解产生策略(CSGS)是有效的还是无效的；(2)应该选择哪些 CSGS 组成策略池；(3)策略池的大小应该是多少。为了解决这些问题,提出了基于相对排列顺序的标度法(RPOSM)和基于 RPOSM 的层次分析法(RPOSM-AHP)。
Optimization of Neutrino Oscillation Parameters using Differential Evolution
Mustafa, Ghulam; Masud, Bilal
2011-01-01
We combine Differential Evolution, a new technique, with the traditional grid based method for optimization of solar neutrino oscillation parameters $\\Delta m^2$ and $\\tan^{2}\\theta$ for the case of two neutrinos. The Differential Evolution is a population based stochastic algorithm for optimization of real valued non-linear non-differentiable objective functions that has become very popular during the last decade. We calculate well known chi-square ($\\chi^2$) function for neutrino oscillations for a grid of the parameters using total event rates of chlorine (Homestake), Gallax+GNO, SAGE, Superkamiokande and SNO detectors and theoretically calculated event rates. We find minimum $\\chi^2$ values in different regions of the parameter space. We explore regions around these minima using Differential Evolution for the fine tuning of the parameters allowing even those values of the parameters which do not lie on any grid. We note as much as 4 times decrease in $\\chi^2$ value in the SMA region and even better goodne...
GPU-accelerated adjoint algorithmic differentiation
Gremse, Felix; Höfter, Andreas; Razik, Lukas; Kiessling, Fabian; Naumann, Uwe
2016-03-01
Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the ;tape;. Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.
Differential evolution for many-particle adaptive quantum metrology.
Lovett, Neil B; Crosnier, Cécile; Perarnau-Llobet, Martí; Sanders, Barry C
2013-05-31
We devise powerful algorithms based on differential evolution for adaptive many-particle quantum metrology. Our new approach delivers adaptive quantum metrology policies for feedback control that are orders-of-magnitude more efficient and surpass the few-dozen-particle limitation arising in methods based on particle-swarm optimization. We apply our method to the binary-decision-tree model for quantum-enhanced phase estimation as well as to a new problem: a decision tree for adaptive estimation of the unknown bias of a quantum coin in a quantum walk and show how this latter case can be realized experimentally.
刘鹏; 蒙志君; 武哲
2012-01-01
由于共轴直升机特有的旋翼布局引发了上下旋翼间强烈的气动干扰,采用传统的理论分析和风洞试验的方法难以获得适用于共轴直升机控制系统的飞行动力学模型.为此,设计了飞行扫频试验,根据飞行试验数据得到了悬停状态下包含共轴直升机飞行动力学模型耦合特性的非参数频率响应,运用仿生智能计算方法中的微分进化（DE,Differential Evolution）算法拟合频率响应建立了悬停状态下的共轴直升机状态空间模型.利用Cramer-Rao边界和不灵敏度的相关理论进行分析计算,说明辨识得到的参数具有较高的精度和可信度.通过比较辨识模型的输出和实际飞行数据的结果,说明辨识得到的模型能充分反映共轴直升机的飞行动力学特性,可用于飞行品质评估和飞控系统设计.%The coaxial helicopter exists intense aerodynamic interaction between the upper and lower rotor,and it is difficult to establish the accurate dynamic model for flight control systems using the theory analysis and wind tunnel experiment.Frequency sweep flight experiment data was used to extract the non-parametric frequency responses that fully characterizes the coupled helicopter dynamics.A nonlinear search based on differential evolution algorithm for a linear state-space model which matches the frequency-response data set was conducted.Parameter insensitivity and Cramer-Rao bound analysis results have low values,indicating very good reliability of the identified model.The accuracy of the identified model is verified by comparing the model-predicted responses with the responses collected during flight experiments,and the model can be used for flight quality analysis and flight control system design.
Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
Yu Feng
2017-01-01
Full Text Available This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.
Structure-preserving algorithms for oscillatory differential equations
Wu, Xinyuan; Wang, Bin
2013-01-01
Structure-Preserving Algorithms for Oscillatory Differential Equations describes a large number of highly effective and efficient structure-preserving algorithms for second-order oscillatory differential equations by using theoretical analysis and numerical validation. Structure-preserving algorithms for differential equations, especially for oscillatory differential equations, play an important role in the accurate simulation of oscillatory problems in applied sciences and engineering. The book discusses novel advances in the ARKN, ERKN, two-step ERKN, Falkner-type and energy-preserving methods, etc. for oscillatory differential equations. The work is intended for scientists, engineers, teachers and students who are interested in structure-preserving algorithms for differential equations. Xinyuan Wu is a professor at Nanjing University; Xiong You is an associate professor at Nanjing Agricultural University; Bin Wang is a joint Ph.D student of Nanjing University and University of Cambridge.
Differential Evolution and Particle Swarm Optimization for Partitional Clustering
Krink, Thiemo; Paterlini, Sandra
2006-01-01
for numerical optimisation, which are hardly known outside the search heuristics field, are particle swarm optimisation (PSO) and differential evolution (DE). The performance of GAs for a representative point evolution approach to clustering is compared with PSO and DE. The empirical results show that DE...... is clearly and consistently superior compared to GAs and PSO for hard clustering problems, both with respect to precision as well as robustness (reproducibility) of the results. Only for simple data sets, the GA and PSO can obtain the same quality of results. Apart from superior performance, DE is easy...... to implement and requires hardly any parameter tuning compared to substantial tuning for GAs and PSOs. Our study shows that DE rather than GAs should receive primary attention in partitional clustering algorithms....
An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs
Zijian Cao
2015-01-01
Full Text Available Brain Storm Optimization (BSO algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO.
Fast Micro-Differential Evolution for Topological Active Net Optimization.
Li, Yuan-Long; Zhan, Zhi-Hui; Gong, Yue-Jiao; Zhang, Jun; Li, Yun; Li, Qing
2016-06-01
This paper studies the optimization problem of topological active net (TAN), which is often seen in image segmentation and shape modeling. A TAN is a topological structure containing many nodes, whose positions must be optimized while a predefined topology needs to be maintained. TAN optimization is often time-consuming and even constructing a single solution is hard to do. Such a problem is usually approached by a "best improvement local search" (BILS) algorithm based on deterministic search (DS), which is inefficient because it spends too much efforts in nonpromising probing. In this paper, we propose the use of micro-differential evolution (DE) to replace DS in BILS for improved directional guidance. The resultant algorithm is termed deBILS. Its micro-population efficiently utilizes historical information for potentially promising search directions and hence improves efficiency in probing. Results show that deBILS can probe promising neighborhoods for each node of a TAN. Experimental tests verify that deBILS offers substantially higher search speed and solution quality not only than ordinary BILS, but also the genetic algorithm and scatter search algorithm.
Artificial Neural Networks, Symmetries and Differential Evolution
Urfalioglu, Onay
2010-01-01
Neuroevolution is an active and growing research field, especially in times of increasingly parallel computing architectures. Learning methods for Artificial Neural Networks (ANN) can be divided into two groups. Neuroevolution is mainly based on Monte-Carlo techniques and belongs to the group of global search methods, whereas other methods such as backpropagation belong to the group of local search methods. ANN's comprise important symmetry properties, which can influence Monte-Carlo methods. On the other hand, local search methods are generally unaffected by these symmetries. In the literature, dealing with the symmetries is generally reported as being not effective or even yielding inferior results. In this paper, we introduce the so called Minimum Global Optimum Proximity principle derived from theoretical considerations for effective symmetry breaking, applied to offline supervised learning. Using Differential Evolution (DE), which is a popular and robust evolutionary global optimization method, we experi...
Hrstka, O; 10.1016/S0965-9978(03)00113-3
2009-01-01
This paper presents several types of evolutionary algorithms (EAs) used for global optimization on real domains. The interest has been focused on multimodal problems, where the difficulties of a premature convergence usually occurs. First the standard genetic algorithm (SGA) using binary encoding of real values and its unsatisfactory behavior with multimodal problems is briefly reviewed together with some improvements of fighting premature convergence. Two types of real encoded methods based on differential operators are examined in detail: the differential evolution (DE), a very modern and effective method firstly published by R. Storn and K. Price, and the simplified real-coded differential genetic algorithm SADE proposed by the authors. In addition, an improvement of the SADE method, called CERAF technology, enabling the population of solutions to escape from local extremes, is examined. All methods are tested on an identical set of objective functions and a systematic comparison based on a reliable method...
The Differential Equation Algorithm for General Deformed Swept Volumes
汪国平; 华宣积; 孙家广
2000-01-01
The differential equation approach for characterizing swept volume boundaries is extended to include objects experiencing deformation. For deformed swept volume, it is found that the structure and algorithm of sweep-envelope differential equation (SEDE) are similar between the deformed and the rigid swept volumes. The efficiency of SEDE approach for deformed swept volume is proved with an example.
Differential evolution enhanced with multiobjective sorting-based mutation operators.
Wang, Jiahai; Liao, Jianjun; Zhou, Ying; Cai, Yiqiao
2014-12-01
Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.
New Iterated Decoding Algorithm Based on Differential Frequency Hopping System
LIANG Fu-lin; LUO Wei-xiong
2005-01-01
A new iterated decoding algorithm is proposed for differential frequency hopping (DFH) encoder concatenated with multi-frequency shift-key (MFSK) modulator. According to the character of the frequency hopping (FH) pattern trellis produced by DFH function, maximum a posteriori (MAP) probability theory is applied to realize the iterate decoding of it. Further, the initial conditions for the new iterate algorithm based on MAP algorithm are modified for better performance. Finally, the simulation result compared with that from traditional algorithms shows good anti-interference performance.
Many-Objective Distinct Candidates Optimization using Differential Evolution
Justesen, Peter; Ursem, Rasmus Kjær
2010-01-01
fully nondominated. A more feasible approach is to discover a low number of solutions within a region of interest on the true Pareto front. Here, a convergent secondary selection criterion guide the search toward optimal regions of interest that may incorporate decision maker preferences. However......, diversity must also be taken into account to ensure that the population does not converge prematurely. In this paper, candidate distinctiveness is measured and controlled based on the novel relaxed objective distance (ROD) measure, which enables the decision maker to control the desired level of diversity...... for each objective. The Many-Objective Distinct Candidates Optimization using Differential Evolution (MODCODE) algorithm takes a novel approach by focusing search using a user-defined number of subpopulations each returning a distinct optimal solution within the preferred region of interest. In this paper...
Giovanni Iacca; Fabio Caraffini; Ferrante Neri
2012-01-01
Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions.These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory.This feature is crucially important in some engineering applications,especially in robotics.A high performance compact algorithm is the compact Differential Evolution (cDE) algorithm.This paper proposes a novel implementation of cDE,namely compact Differential Evolution light (cDElight),to address not only the memory saving necessities but also real-time requirements.cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss,with respect to cDE.Numerical results,carried out on a broad set of test problems,show that cDElight,despite its minimal hardware requirements,does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms.An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.
Algorithmic Thomas Decomposition of Algebraic and Differential Systems
Bächler, Thomas; Lange-Hegermann, Markus; Robertz, Daniel
2011-01-01
In this paper, we consider systems of algebraic and non-linear partial differential equations and inequations. We decompose these systems into so-called simple subsystems and thereby partition the set of solutions. For algebraic systems, simplicity means triangularity, square-freeness and non-vanishing initials. Differential simplicity extends algebraic simplicity with involutivity. We build upon the constructive ideas of J. M. Thomas and develop them into a new algorithm for disjoint decomposition. The given paper is a revised version of a previous paper and includes the proofs of correctness and termination of our decomposition algorithm. In addition, we illustrate the algorithm with further instructive examples and describe its Maple implementation together with an experimental comparison to some other triangular decomposition algorithms.
Differential diagnostic algorithm for diseases manifested with heart murmurs syndrome.
Naumov, Leonid B
2009-08-01
Diagnostic interpretation at auscultation of heart murmurs is accompanied by frequent errors. It creates serious clinical, pedagogical, organizational and social problems. The standard nosological principle of a clinical information description from the diagnosis (a disease name) to the description of symptoms/signs contradicts to real clinical practice from revealing of symptoms through differential diagnostics to a diagnosis establishment. The differential diagnostic algorithm or diagnostic algorithm developed by the author, is based on the opposite syndromic principle of thinking - from the signs to the diagnosis. It completely corresponds to the practical purposes of reliable diagnostics of 35 illnesses, manifested by heart murmurs at a heart auscultation.
Chaolu Temuer; Yu-shan BAI
2009-01-01
In this paper,we present a differential polynomial characteristic set algorithm for the complete symmetry classification of partial differential equations (PDEs)with some parameters. It can make the solution to the complete symmetry classification problem for PDEs become direct and systematic. As an illustrative example,the complete potential symmetry classifications of nonlinear and linear wave equations with an arbitrary function parameter are presented. This is a new application of the differential form characteristic set algorithm,i.e.,Wu's method,in differential equations.
On Models of Nonlinear Evolution Paths in Adiabatic Quantum Algorithms
SUN Jie; LU Song-Feng; Samuel L.Braunstein
2013-01-01
In this paper,we study two different nonlinear interpolating paths in adiabatic evolution algorithms for solving a particular class of quantum search problems where both the initial and final Hamiltonian are one-dimensional projector Hamiltonians on the corresponding ground state.If the overlap between the initial state and final state of the quantum system is not equal to zero,both of these models can provide a constant time speedup over the usual adiabatic algorithms by increasing some another corresponding "complexity".But when the initial state has a zero overlap with the solution state in the problem,the second model leads to an infinite time complexity of the algorithm for whatever interpolating functions being applied while the first one can still provide a constant running time.However,inspired by a related reference,a variant of the first model can be constructed which also fails for the problem when the overlap is exactly equal to zero if we want to make up the "intrinsic" fault of the second model — an increase in energy.Two concrete theorems are given to serve as explanations why neither of these two models can improve the usual adiabatic evolution algorithms for the phenomenon above.These just tell us what should be noted when using certain nonlinear evolution paths in adiabatic quantum algorithms for some special kind of problems.
A fuzzy simulated evolution algorithm for integrated manufacturing system design
Michael Mutingi
2013-04-01
Full Text Available Integrated cell formation and layout (CFLP is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry.
Dynamical transitions in the evolution of learning algorithms by selection
Neirotti, J P; Neirotti, Juan Pablo; Caticha, Nestor
2002-01-01
We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate a sequence of populations of algorithms which can be used by neural networks for supervised learning of a rule that generates examples. In opposition to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process and pay particular attention to the temporal order of appearance of functional structures responsible for the improvements in the learning process, as measured by the generalization capabilities of the resulting algorithms. The effect of such appearances can be described as dynamical phase transitions. The concepts of phenotypic and genotypic entropies, which serve to describe the distribution of fitness in the population and the distribution of symbols respectively, are used to monitor the dynamics. In different runs the phase transitions might be present or not, with the system finding out good solutions, or ...
MELO JR., A.
2013-06-01
Full Text Available The Proportional Differentiation Model (PDM is currently one of the main service proposals for the Next Generation Internet. This paper presents a new packet scheduling algorithm for implementing the PDM model using measurement windows and a mechanism of dynamic adjustment of packet delay estimation. Window Based Waiting-Time Priority Plus (WBWTP+, the proposed algorithm, is an evolution of the WBWTP algorithm that adjusts dynamically the relative weights of transmitted and waiting for transmission packets according to the current state of the system. The WBWTP+ delay estimator makes possible to accelerate or to delay the transmission of backlogged packets. Simulations performed to asses the performance of the WBWTP+ show that it presents significant improvement in the attendance of the PDM objective in relation to WBWTP in most scenarios, excepted when the link utilization rate is 100%. Even in that case the performance of WBWTP+ was better than that of WTP and PAD algorithms.
Yamina BOUGHARI
2017-06-01
Full Text Available In this paper the Cessna Citation X clearance criteria were evaluated for a new Flight Controller. The Flight Control Law were optimized and designed for the Cessna Citation X flight envelope by combining the Deferential Evolution algorithm, the Linear Quadratic Regulator method, and the Proportional Integral controller during a previous research presented in part 1. The optimal controllers were used to reach satisfactory aircraft’s dynamic and safe flight operations with respect to the augmentation systems’ handling qualities, and design requirements. Furthermore the number of controllers used to control the aircraft in its flight envelope was optimized using the Linear Fractional Representations features. To validate the controller over the whole aircraft flight envelope, the linear stability, eigenvalue, and handling qualities criteria in addition of the nonlinear analysis criteria were investigated during this research to assess the business aircraft for flight control clearance and certification. The optimized gains provide a very good stability margins as the eigenvalue analysis shows that the aircraft has a high stability, and a very good flying qualities of the linear aircraft models are ensured in its entire flight envelope, its robustness is demonstrated with respect to uncertainties due to its mass and center of gravity variations.
MRIO： A New Active Queue Management Algorithm for Differentiated Services
WUChunming; JIANGMing; ZHUMiaoliang
2005-01-01
The DiffServ (Differentiate service) architecture has recently become the preferred service model for addressing QoS (Quality of service) issues in IP networks due to its simplicity and scalability. In DiffServ domain, the core routers use RIO (Red with in and out) algorithm, which performs different packets dropping schemes when congestion occurs. But RIO algorithm cannot stabilize the queue size and therefore, leads to unpredictable queuing delay and jitter. Furthermore, RIO has the “bandwidth skew” problem. The object of MRIO (MRED with in and out) algorithm is to stabilize the queue size and mitigate the ""bandwidth skew"" problem. MRIO is based on RED (Random early detection) for IN packets and MRED (Modified RED) algorithm for OUT packets. The simulation results indicate that compared with RIO, MRIO performs better in stabilizing queue size and mitigating “bandwidth skew” problem.
Evolution of domain wall networks: the PRS algorithm
Sousa, L
2011-01-01
The Press-Ryden-Spergel (PRS) algorithm is a modification to the field theory equations of motion, parametrized by two parameters ($\\alpha$ and $\\beta$), implemented in numerical simulations of cosmological domain wall networks, in order to ensure a fixed comoving resolution. In this paper we explicitly demonstrate that the PRS algorithm provides the correct domain wall dynamics in $N+1$-dimensional Friedmann-Robertson-Walker (FRW) universes if $\\alpha+\\beta/2=N$, fully validating its use in numerical studies of cosmic domain evolution. We further show that this result is valid for generic thin featureless domain walls, independently of the Lagrangian of the model.
Karyotype evolution and species differentiation in the genus Rattus ...
Dhananjoy
Karyotype evolution and species differentiation in the genus Rattus of ... as primitive/ancestral types of chromosomes into either subtelocentric or small metacentrics leads to speciation or simply new ..... The features are quite common in the.
Eman Ali Hussain
2015-01-01
Full Text Available Absract In this project A new method for solving Stochastic Differential Equations SDEs deriving by Wiener process numerically will be construct and implement using Accelerated Genetic Algorithm AGA. An SDE is a differential equation in which one or more of the terms and hence the solutions itself is a stochastic process. Solving stochastic differential equations requires going away from the recognizable deterministic setting of ordinary and partial differential equations into a world where the evolution of a quantity has an inherent random component and where the expected behavior of this quantity can be described in terms of probability distributions. We applied our method on the Ito formula which is equivalent to the SDE to find approximation solution of the SDEs. Numerical experiments illustrate the behavior of the proposed method.
Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method
Rashida Adeeb Khanum
2016-01-01
Full Text Available Differential evolution (DE is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE, sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS, and differential ant-stigmergy algorithm (DASA. These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.
A Sumudu based algorithm for solving differential equations
Jun Zhang
2007-11-01
Full Text Available An algorithm based on Sumudu transform is developed. The algorithm can be implemented in computer algebra systems like Maple. It can be used to solve differential equations of the following form automatically without human interaction \\begin{displaymath} \\sum_{i=0}^{m} p_i(xy^{(i}(x = \\sum_{j=0}^{k}q_j(xh_j(x \\end{displaymath} where pi(x(i=0, 1, 2, ..., m and qj(x(j=0, 1, 2, ..., k are polynomials. hj(x are non-rational functions, but their Sumudu transforms are rational. m, k are nonnegative integers.
2016-05-01
Algorithm for Overcoming the Curse of Dimensionality for Certain Non-convex Hamilton-Jacobi Equations, Projections and Differential Games Yat Tin...complexity of the resulting algorithm is polynomial in the problem dimension; hence, it overcomes the curse of dimensionality [1, 2]. We extend previous work...compute the evolution of geometric objects [25], which was first used for reachability problems in [21, 22] to our knowledge . Numerical solutions to HJ PDE
Optimization of wind farm turbines layout using an evolutive algorithm
Gonzalez, Javier Serrano; Santos, Jesus Riquelme; Payan, Manuel Burgos [Department of Electrical Engineering, Av. de los Descubrimientos, University of Sevilla, Sevilla (Spain); Gonzalez Rodriguez, Angel G. [Department of Electronic Engineering and Automatic, University of Jaen, Jaen (Spain); Mora, Jose Castro [Persan S.A., Sevilla (Spain)
2010-08-15
The optimum wind farm configuration problem is discussed in this paper and an evolutive algorithm to optimize the wind farm layout is proposed. The algorithm's optimization process is based on a global wind farm cost model using the initial investment and the present value of the yearly net cash flow during the entire wind-farm life span. The proposed algorithm calculates the yearly income due to the sale of the net generated energy taking into account the individual wind turbine loss of production due to wake decay effects and it can deal with areas or terrains with non-uniform load-bearing capacity soil and different roughness length for every wind direction or restrictions such as forbidden areas or limitations in the number of wind turbines or the investment. The results are first favorably compared with those previously published and a second collection of test cases is used to proof the performance and suitability of the proposed evolutive algorithm to find the optimum wind farm configuration. (author)
Differential Evolution based SHEPWM for Seven-Level Inverter with Non-Equal DC Sources
Fayçal CHABNI
2016-09-01
Full Text Available This paper presents the application of differential evolution algorithm to obtain optimal switching angles for a single-phase seven-level to improve AC voltage quality. The proposed inverter in this article is composed of two H-bridge cells with non-equal DC voltage sources in order to generate multiple voltage levels. Selective harmonic elimination pulse width modulation (SHPWM strategy is used to improve the AC output voltage waveform generated by the proposed inverter. The differential evolution (DE optimization algorithm is used to solve non-linear transcendental equations necessary for the (SHPWM. Computational results obtained from computer simulations presented a good agreement with the theoretical predictions. A laboratory prototype based on STM32F407 microcontroller was built in order to validate the simulation results. The experimental results show the effectiveness of the proposed modulation method.
Roman Senkerik
2016-01-01
Full Text Available In this paper, evolutionary technique Differential Evolution (DE is used for the evolutionary tuning of controller parameters for the stabilization of selected discrete chaotic system, which is the two-dimensional Lozi map. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used within Chaos enhanced heuristic concept as the chaotic pseudo-random number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudo-random sequences given by chaotic map to help Differential evolution algorithm in searching for the best controller settings for the same chaotic system. The optimizations were performed for three different required final behavior of the chaotic system, and two types of developed cost function. To confirm the robustness of presented approach, comparisons with canonical DE strategy and PSO algorithm have been performed.
Structure-preserving algorithms for oscillatory differential equations II
Wu, Xinyuan; Shi, Wei
2015-01-01
This book describes a variety of highly effective and efficient structure-preserving algorithms for second-order oscillatory differential equations. Such systems arise in many branches of science and engineering, and the examples in the book include systems from quantum physics, celestial mechanics and electronics. To accurately simulate the true behavior of such systems, a numerical algorithm must preserve as much as possible their key structural properties: time-reversibility, oscillation, symplecticity, and energy and momentum conservation. The book describes novel advances in RKN methods, ERKN methods, Filon-type asymptotic methods, AVF methods, and trigonometric Fourier collocation methods. The accuracy and efficiency of each of these algorithms are tested via careful numerical simulations, and their structure-preserving properties are rigorously established by theoretical analysis. The book also gives insights into the practical implementation of the methods. This book is intended for engineers and sc...
Algorithm Refinement for Stochastic Partial Differential Equations. I. Linear Diffusion
Alexander, Francis J.; Garcia, Alejandro L.; Tartakovsky, Daniel M.
2002-10-01
A hybrid particle/continuum algorithm is formulated for Fickian diffusion in the fluctuating hydrodynamic limit. The particles are taken as independent random walkers; the fluctuating diffusion equation is solved by finite differences with deterministic and white-noise fluxes. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass conservation. This methodology is an extension of Adaptive Mesh and Algorithm Refinement to stochastic partial differential equations. Results from a variety of numerical experiments are presented for both steady and time-dependent scenarios. In all cases the mean and variance of density are captured correctly by the stochastic hybrid algorithm. For a nonstochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except in particle regions away from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
Algorithm refinement for stochastic partial differential equations I. linear diffusion
Alexander, F J; Tartakovsky, D M
2002-01-01
A hybrid particle/continuum algorithm is formulated for Fickian diffusion in the fluctuating hydrodynamic limit. The particles are taken as independent random walkers; the fluctuating diffusion equation is solved by finite differences with deterministic and white-noise fluxes. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass conservation. This methodology is an extension of Adaptive Mesh and Algorithm Refinement to stochastic partial differential equations. Results from a variety of numerical experiments are presented for both steady and time-dependent scenarios. In all cases the mean and variance of density are captured correctly by the stochastic hybrid algorithm. For a nonstochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except in particle regions away from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
A Population Classification Evolution Algorithm for the Parameter Extraction of Solar Cell Models
Yiqun Zhang
2016-01-01
Full Text Available To quickly and precisely extract the parameters for solar cell models, inspired by simplified bird mating optimizer (SBMO, a new optimization technology referred to as population classification evolution (PCE is proposed. PCE divides the population into two groups, elite and ordinary, to reach a better compromise between exploitation and exploration. For the evolution of elite individuals, we adopt the idea of parthenogenesis in nature to afford a fast exploitation. For the evolution of ordinary individuals, we adopt an effective differential evolution strategy and a random movement of small probability is added to strengthen the ability to jump out of a local optimum, which affords a fast exploration. The proposed PCE is first estimated on 13 classic benchmark functions. The experimental results demonstrate that PCE yields the best results on 11 functions by comparing it with six evolutional algorithms. Then, PCE is applied to extract the parameters for solar cell models, that is, the single diode and the double diode. The experimental analyses demonstrate that the proposed PCE is superior when comparing it with other optimization algorithms for parameter identification. Moreover, PCE is tested using three different sources of data with good accuracy.
Differential evolution Markov chain with snooker updater and fewer chains
Vrugt, Jasper A [Los Alamos National Laboratory; Ter Braak, Cajo J F [NON LANL
2008-01-01
Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50--100 with fewer parallel chains (e.g. N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5--26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5% point of a variable from a 25--50 dimensional Student T{sub 3} distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the model.
Förster, Michael
2014-01-01
Numerical programs often use parallel programming techniques such as OpenMP to compute the program's output values as efficient as possible. In addition, derivative values of these output values with respect to certain input values play a crucial role. To achieve code that computes not only the output values simultaneously but also the derivative values, this work introduces several source-to-source transformation rules. These rules are based on a technique called algorithmic differentiation. The main focus of this work lies on the important reverse mode of algorithmic differentiation. The inh
Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution
Satish Gajawada; Durga Toshniwal
2012-01-01
Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...
Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning
Smelyanskiy, Vadius; vonToussaint, Udo V.; Timucin, Dogan A.; Clancy, Daniel (Technical Monitor)
2002-01-01
We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum exitation gap, gmin = O(n2(sup -n/2)), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.
An enhanced algorithm to estimate BDS satellite's differential code biases
Shi, Chuang; Fan, Lei; Li, Min; Liu, Zhizhao; Gu, Shengfeng; Zhong, Shiming; Song, Weiwei
2016-02-01
This paper proposes an enhanced algorithm to estimate the differential code biases (DCB) on three frequencies of the BeiDou Navigation Satellite System (BDS) satellites. By forming ionospheric observables derived from uncombined precise point positioning and geometry-free linear combination of phase-smoothed range, satellite DCBs are determined together with ionospheric delay that is modeled at each individual station. Specifically, the DCB and ionospheric delay are estimated in a weighted least-squares estimator by considering the precision of ionospheric observables, and a misclosure constraint for different types of satellite DCBs is introduced. This algorithm was tested by GNSS data collected in November and December 2013 from 29 stations of Multi-GNSS Experiment (MGEX) and BeiDou Experimental Tracking Stations. Results show that the proposed algorithm is able to precisely estimate BDS satellite DCBs, where the mean value of day-to-day scattering is about 0.19 ns and the RMS of the difference with respect to MGEX DCB products is about 0.24 ns. In order to make comparison, an existing algorithm based on IGG: Institute of Geodesy and Geophysics, China (IGGDCB), is also used to process the same dataset. Results show that, the DCB difference between results from the enhanced algorithm and the DCB products from Center for Orbit Determination in Europe (CODE) and MGEX is reduced in average by 46 % for GPS satellites and 14 % for BDS satellites, when compared with DCB difference between the results of IGGDCB algorithm and the DCB products from CODE and MGEX. In addition, we find the day-to-day scattering of BDS IGSO satellites is obviously lower than that of GEO and MEO satellites, and a significant bias exists in daily DCB values of GEO satellites comparing with MGEX DCB product. This proposed algorithm also provides a new approach to estimate the satellite DCBs of multiple GNSS systems.
Synthesis of Spherical 4R Mechanism for Path Generation using Differential Evolution
Penunuri, F; Villanueva, C; Cruz-Villar, Carlos A
2011-01-01
The problem of path generation for the spherical 4R mechanism is solved using the Differential Evolution (DE) algorithm. Formulas for the spherical geodesics are employed in order to obtain the parametric equation for the trajectory of the mechanism end-effector. Direct optimization of the objective function gives solution to the path generation task without prescribed timing. Therefore, there is no need to separate this task into two stages and then proceed to the optimization. Moreover, the order defect problem can be solved without difficulty by means of manipulations of the individuals in the DE algorithm. Two examples of optimum synthesis showing the simplicity and effectiveness of the approach are included.
Jiang, Siwei; Cai, Zhihua
Differential evolution is a powerful and robust method to solve the Multi-Objective Problems in MOEAs. To enhance the differential evolution for MOPs, we focus on two aspects: the population initialization and acceptance rule. In this paper, we present a new differential evolution called DEMO_{DV}^{UD}, it mainly include: (1) the first population is constructed by statistical method: Uniform Design, which can get more evenly distributed solutions than random design, (2) a new acceptance rule is firstly presented as Minimum Reduce Hypervolume. Acceptance rule is a metric to decide which solution should be cut off when the archive is full to the setting size. Crowding Distance is frequently used to estimate the length of cuboid enclosing the solution, while Minimum Reduce Hypervolume is used to estimate the volume of cuboid. The new algorithm designs a fitness function Distance/Volume that balance the CD and MRV, which maintains the spread and hypervolume along the Pareto-front. Experiment on different multi-Objective problems include ZDTx and DTLZx by jMetal 2.0, the results show that the new algorithm gets higher hypervolume, faster convergence, better distributed solutions and needs less numbers of fitness function evolutions than NSGA-II, SPEA2 and GDE3.
Cell search algorithms for the 3G long-term evolution
SU Huan; ZHANG Jian-hua
2007-01-01
This article presents downlink initial synchronization and cell identification algorithms for long term evolution (LTE) of third-generation (3G) mobile communication systems, which are based on synchronization channel (SCH) and cell specific pilot symbols, respectively. The key features of the proposed scheme are: it can improve performance of the frequency synchronization through oversampling of the SCH, it can support a large number of target cells by modulating a cell-specific pilot sequence over two symbols within a subframe, and it can guarantee cell identification performance by maximally ratio combining the frequency domain differential cross-correlation. Simulations show that the proposed scheme has a potential use in 3G LTE.
Guang-zhou Chen
2015-01-01
Full Text Available Parameter identification plays a crucial role for simulating and using model. This paper firstly carried out the sensitivity analysis of the 2-chlorophenol oxidation model in supercritical water using the Monte Carlo method. Then, to address the nonlinearity of the model, two improved differential search (DS algorithms were proposed to carry out the parameter identification of the model. One strategy is to adopt the Latin hypercube sampling method to replace the uniform distribution of initial population; the other is to combine DS with simplex method. The results of sensitivity analysis reveal the sensitivity and the degree of difficulty identified for every model parameter. Furthermore, the posteriori probability distribution of parameters and the collaborative relationship between any two parameters can be obtained. To verify the effectiveness of the improved algorithms, the optimization performance of improved DS in kinetic parameter estimation is studied and compared with that of the basic DS algorithm, differential evolution, artificial bee colony optimization, and quantum-behaved particle swarm optimization. And the experimental results demonstrate that the DS with the Latin hypercube sampling method does not present better performance, while the hybrid methods have the advantages of strong global search ability and local search ability and are more effective than the other algorithms.
刘自发; 张伟
2012-01-01
A GIFANDE(Geography Information Factor and Adaptive Niche Differential Evolution) algorithm is proposed for substation sizing and locating of modern urban power grid. A comprehensive planning model is established based on the interval analytical hierarchy process method,which considers the expenditure of substation construction and operation,as well as different geographic information factors,such as land nature, transportation condition, flood control & drainage, geographical features, construction conditions, etc. The adaptive niche differential evolution algorithm is adopted in optimization process, which adjusts the individual adaptive value,speeds up the convergence by elimination,and adaptively adjusts the niche radius according to the intervals between individuals to improve the capability and efficiency of global optimal search. Practical example shows the effectiveness of the proposed algorithm in the substation planning of urban distribution network.%针对现代城市配电网变电站选址定容问题,提出一种充分考虑地理信息因子影响的自适应小生境微分进化算法.建立基于区间层次分析法,考虑用地性质、交通情况、防洪排水、地质地貌、施工条件等因素的地理信息因子和变电站建设、运行等费用的综合规划模型.在问题寻优过程中,在微分进化算法的基础上引入小生境中共享机制构成小生境微分进化算法,该算法改变个体适应度值,通过淘汰运算加快收敛速度,并根据个体间相对距离判断种群的聚集情况以自适应调整小生境半径,从而较大提高了算法的全局寻优能力和搜索效率.实际算例表明,所提算法能较好地解决城市配电网变电站规划问题.
Local structure-preserving algorithms for partial differential equations
2008-01-01
In this paper, we discuss the concept of local structure-preserving algorithms (SPAs) for partial differential equations, which are the natural generalization of the corresponding global SPAs. Local SPAs for the problems with proper boundary conditions are global SPAs, but the inverse is not necessarily valid. The concept of the local SPAs can explain the difference between different SPAs and provide a basic theory for analyzing and constructing high performance SPAs. Furthermore, it enlarges the applicable scopes of SPAs. We also discuss the application and the construction of local SPAs and derive several new SPAs for the nonlinear Klein-Gordon equation.
Fuzzy logic-based diversity-controlled self-adaptive differential evolution
Amali, S. Miruna Joe; Baskar, S.
2013-08-01
This article presents a novel method using a fuzzy system (FS) to control the population diversity during the various phases of evolution. A local search is applied at regular intervals on an individual selected at random to aid the population in convergence. This diversity control methodology is applied to vary the crossover rate of self-adaptive differential evolution (SaDE). Three variants of the SaDE algorithm are proposed: (1) diversity-controlled SaDE (DCSaDE); (2) SaDE with local search (SaDE-LS); and (3) diversity-controlled SaDE with local search (DCSaDE-LS). The performance of the proposed algorithms is analysed using a set of unconstrained benchmark functions with respect to average function evaluations, success rate and the mean of the objectives of 30 independent trials. The DCSaDE-LS algorithm had a better success rate for high-dimensional multimodal problems and conserved the number of function evaluations required for most of the problems. It is compared with other popular algorithms and the outcome of the proposed DCSaDE-LS algorithm is validated using non-parametric statistical tests. MATLAB codes for the proposed algorithms may be obtained on request.
Random Matrix Approach to Quantum Adiabatic Evolution Algorithms
Boulatov, Alexei; Smelyanskiy, Vadier N.
2004-01-01
We analyze the power of quantum adiabatic evolution algorithms (Q-QA) for solving random NP-hard optimization problems within a theoretical framework based on the random matrix theory (RMT). We present two types of the driven RMT models. In the first model, the driving Hamiltonian is represented by Brownian motion in the matrix space. We use the Brownian motion model to obtain a description of multiple avoided crossing phenomena. We show that the failure mechanism of the QAA is due to the interaction of the ground state with the "cloud" formed by all the excited states, confirming that in the driven RMT models. the Landau-Zener mechanism of dissipation is not important. We show that the QAEA has a finite probability of success in a certain range of parameters. implying the polynomial complexity of the algorithm. The second model corresponds to the standard QAEA with the problem Hamiltonian taken from the Gaussian Unitary RMT ensemble (GUE). We show that the level dynamics in this model can be mapped onto the dynamics in the Brownian motion model. However, the driven RMT model always leads to the exponential complexity of the algorithm due to the presence of the long-range intertemporal correlations of the eigenvalues. Our results indicate that the weakness of effective transitions is the leading effect that can make the Markovian type QAEA successful.
Bech, Michael Møller; Nørgård, Christian; Roemer, Daniel Beck
2016-01-01
This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri-objectiv...... different optimization control parameter settings and it is concluded that GDE3 is a reliable optimization tool that can assist mechatronic engineers in the design and decision making process.......This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri......-objective problems having 10+ design variables are both highly constrained, nonlinear and non-smooth but nevertheless the algorithm converges to the Pareto-front within a hours of computation (20k function evaluations). Additionally, the robustness and convergence speed of the algorithm are investigated using...
Optimal Overlay of Ligands with Flexible Bonds Using Differential Evolution
Pedersen, Christian Storm; Kristensen, Thomas Greve
When designing novel drugs, the need arise to screen databases for structures resembling active ligands, e.g. by generating a query meta-structure which summarizes these. We propose a flexible bond method for making a meta-structure and present Monte Carlo, Nelder-Mead and Differential Evolution ...
Optimal Overlay of Ligands with Flexible Bonds Using Differential Evolution
Pedersen, Christian Storm; Kristensen, Thomas Greve
When designing novel drugs, the need arise to screen databases for structures resembling active ligands, e.g. by generating a query meta-structure which summarizes these. We propose a flexible bond method for making a meta-structure and present Monte Carlo, Nelder-Mead and Differential Evolution...
Design of Robust Optimal Fixed Structure Controller Using Self Adaptive Differential Evolution
Joe Amali, S. Miruna; Baskar, S.
This paper presents a design of robust optimal fixed structure controller for systems with uncertainties and disturbance using Self Adaptive Differential Evolution (SaDE) algorithm. PID controller and second order polynomial structure are considered for fixed structure controller. The design problem is formulated as minimization of maximum value of real part of the poles subject to the robust stability criteria and load disturbance attenuation criteria. The performance of the proposed method is demonstrated with a test system. SaDE self adapts the trial vector generation strategy and crossover rate (CR) value during evolution. Self adaptive Penalty (SP) method is used for constraint handling. The results are compared with constrained PSO and mixed Deterministic/Randomized algorithms. It is shown experimentally that the SaDE adapts automatically to the best strategy and CR value. Performance of the SaDE-based controller is superior to other methods in terms of success rate, robust stability, and disturbance attenuation.
New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy
Zhengzheng Xian
2017-01-01
Full Text Available Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse. Singular Value Decomposition (SVD is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users and items and is realized by using algebraic feature extraction. The derivative model SVD++ of SVD achieves better predictive accuracy due to the addition of implicit feedback information. Differential privacy is defined very strictly and can be proved, which has become an effective measure to solve the problem of attackers indirectly deducing the personal privacy information by using background knowledge. In this paper, differential privacy is applied to the SVD++ model through three approaches: gradient perturbation, objective-function perturbation, and output perturbation. Through theoretical derivation and experimental verification, the new algorithms proposed can better protect the privacy of the original data on the basis of ensuring the predictive accuracy. In addition, an effective scheme is given that can measure the privacy protection strength and predictive accuracy, and a reasonable range for selection of the differential privacy parameter is provided.
On the practical usage of genetic algorithms in ecology and evolution
Hamblin, Steven; Hansen, Thomas
2013-01-01
Genetic algorithms are a heuristic global optimisation technique mimicking the action of natural selection to solve hard optimisation problems, which has enjoyed growing usage in evolution and ecology...
A Differential Evolution Based MPPT Method for Photovoltaic Modules under Partial Shading Conditions
Kok Soon Tey
2014-01-01
Full Text Available Partially shaded photovoltaic (PV modules have multiple peaks in the power-voltage (P-V characteristic curve and conventional maximum power point tracking (MPPT algorithm, such as perturbation and observation (P&O, which is unable to track the global maximum power point (GMPP accurately due to its localized search space. Therefore, this paper proposes a differential evolution (DE based optimization algorithm to provide the globalized search space to track the GMPP. The direction of mutation in the DE algorithm is modified to ensure that the mutation always converges to the best solution among all the particles in the generation. This helps to provide the rapid convergence of the algorithm. Simulation of the proposed PV system is carried out in PSIM and the results are compared to P&O algorithm. In the hardware implementation, a high step-up DC-DC converter is employed to verify the proposed algorithm experimentally on partial shading conditions, load variation, and solar intensity variation. The experimental results show that the proposed algorithm is able to converge to the GMPP within 1.2 seconds with higher efficiency than P&O.
A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem
Zhaolu Guo
2014-11-01
Full Text Available Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the drawbacks of the selection operator in the traditional differential evolution (DE, we present a novel discrete differential evolution (TDDE for solving 0-1 knapsack problem. In TDDE, an enhanced selection operator inspired by the principle of the minimal free energy in thermodynamics is employed, trying to balance the conflict between the selective pressure and the diversity of population to some degree. An experimental study is conducted on twenty 0-1 knapsack test instances. The comparison results show that TDDE can gain competitive performance on the majority of the test instances.
An Improved Differential Evolution Based Dynamic Economic Dispatch with Nonsmooth Fuel Cost Function
R. Balamurugan
2007-09-01
Full Text Available Dynamic economic dispatch (DED is one of the major operational decisions in electric power systems. DED problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. This paper presents an improved differential evolution (IDE method to solve the DED problem of generating units considering valve-point effects. Heuristic crossover technique and gene swap operator are introduced in the proposed approach to improve the convergence characteristic of the differential evolution (DE algorithm. To illustrate the effectiveness of the proposed approach, two test systems consisting of five and ten generating units have been considered. The results obtained through the proposed method are compared with those reported in the literature.
A delay differential equation solver based on the parallel Adams algorithms
ChengjianZHANG; HongbingYU
2001-01-01
This paper constructs a class of parallel Adams algorithms for the systems of delay differential equations.The results on convergence and stability are given.The theoretical analysis and numerical test shows that this algorithm is effect and comparable.
Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds
Vojtěch Uher
2016-01-01
Full Text Available The Differential Evolution (DE is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE applying the principle of the discrete-coded DE in discrete point clouds (PCs. The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.
Uher, Vojtěch; Gajdoš, Petr; Radecký, Michal; Snášel, Václav
2016-01-01
The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.
Rashida Adeeb Khanum
2016-02-01
Full Text Available JADE is an adaptive scheme of nature inspired algorithm, Differential Evolution (DE. It performed considerably improved on a set of well-studied benchmark test problems. In this paper, we evaluate the performance of new JADE with two external archives to deal with unconstrained continuous large-scale global optimization problems labeled as Reflected Adaptive Differential Evolution with Two External Archives (RJADE/TA. The only archive of JADE stores failed solutions. In contrast, the proposed second archive stores superior solutions at regular intervals of the optimization process to avoid premature convergence towards local optima. The superior solutions which are sent to the archive are reflected by new potential solutions. At the end of the search process, the best solution is selected from the second archive and the current population. The performance of RJADE/TA algorithm is then extensively evaluated on two test beds. At first on 28 latest benchmark functions constructed for the 2013 Congress on Evolutionary Computation special session. Secondly on ten benchmark problems from CEC2010 Special Session and Competition on Large-Scale Global Optimization. Experimental results demonstrated a very competitive perfor-mance of the algorithm.
Senkerik, Roman; Zelinka, Ivan; Pluhacek, Michal; Davendra, Donald; Oplatková Kominkova, Zuzana
2014-01-01
Evolutionary technique differential evolution (DE) is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudorandom sequences given by chaotic map to help differential evolution algorithm search for the best controller settings for the very same chaotic system. The optimizations were performed for three different chaotic systems, two types of case studies and developed cost functions.
Roman Senkerik
2014-01-01
Full Text Available Evolutionary technique differential evolution (DE is used for the evolutionary tuning of controller parameters for the stabilization of set of different chaotic systems. The novelty of the approach is that the selected controlled discrete dissipative chaotic system is used also as the chaotic pseudorandom number generator to drive the mutation and crossover process in the DE. The idea was to utilize the hidden chaotic dynamics in pseudorandom sequences given by chaotic map to help differential evolution algorithm search for the best controller settings for the very same chaotic system. The optimizations were performed for three different chaotic systems, two types of case studies and developed cost functions.
Differential Evolution with Gaussian Mutation for Economic Dispatch
Basu, Mousumi; Jena, Chitralekha; Panigrahi, Chinmoy Kumar
2016-12-01
This paper presents differential evolution with Gaussian mutation (DEGM) to solve economic dispatch problem of thermal generating units with non-smooth/non-convex cost functions due to valve-point loading, taking into account transmission losses and nonlinear generator constraints such as prohibited operating zones. Differential evolution (DE) is a simple yet powerful global optimization technique. It exploits the differences of randomly sampled pairs of objective vectors for its mutation process. This mutation process is not suitable for complex multimodal optimization. This paper proposes Gaussian mutation in DE which improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the simplicity of the structure of DE. The effectiveness of the proposed method has been verified on three different test systems. From the comparison with other evolutionary methods, it is found that DEGM based approach is able to provide better solution.
Modified constrained differential evolution for solving nonlinear global optimization problems
2013-01-01
Nonlinear optimization problems introduce the possibility of multiple local optima. The task of global optimization is to find a point where the objective function obtains its most extreme value while satisfying the constraints. Some methods try to make the solution feasible by using penalty function methods, but the performance is not always satisfactory since the selection of the penalty parameters for the problem at hand is not a straightforward issue. Differential evolut...
Improved Differential Evolution for Combined Heat and Power Economic Dispatch
Jena, C.; Basu, M.; Panigrahi, C. K.
2016-04-01
This paper presents an improved differential evolution to solve non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. Valve-point loading and prohibited operating zones of conventional thermal generators are taken into account. Differential evolution (DE) exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective function toward the optimization process and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. This paper proposes Gaussian random variable instead of scaling factor which improves search efficiency. The effectiveness of the proposed method has been verified on four test systems. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed improved differential evolution based approach is able to provide better solution.
Multi-path planning algorithm based on fitness sharing and species evolution
ZHANG Jing-juan; LI Xue-lian; HAO Yan-ling
2003-01-01
A new algorithm is proposed for underwater vehicles multi-path planning. This algorithm is based on fitness sharing genetic algorithm, clustering and evolution of multiple populations, which can keep the diversity of the solution path, and decrease the operating time because of the independent evolution of each subpopulation. The multi-path planning algorithm is demonstrated by a number of two-dimensional path planning problems. The results show that the multi-path planning algorithm has the following characteristics: high searching capability, rapid convergence and high reliability.
Hao, Xiao-Hu; Zhang, Gui-Jun; Zhou, Xiao-Gen
2017-09-05
Protein structure prediction can be considered as a multimodal optimization problem for sampling the protein conformational space associated with an extremely complex energy landscape. To address this problem, a conformational space sampling method using multi-subpopulation differential evolution, MDE, is proposed. MDE first devotes to generate given numbers of concerned modal under the ultrafast shape recognition based modal identification protocol, which regards each individual as one modal at beginning. Then, differential evolution is used for keeping the preserved modal survival in the evolution process. Meanwhile, a local descent direction used to sample along with is constructed based on the abstract convex underestimate technique for modal enhancement, which could enhance the ability of sampling in the region with lower energy. Through the sampling process of evolution, several certain clusters contain a series of conformations in proportion to the energy score will be obtained. Representative conformations in the generated clusters can be directly picked out as decoy conformations for further refinement with no extra clustering operation needs. A total of 20 target proteins are tested. In which 10 target proteins are tested for comparison with Rosetta and 3 evolutionary algorithms. And 10 easy/hard target proteins in CASP 11 are tested for further verifying the effectiveness of MDE. Test results show strong sampling ability that MDE holds, and near-native conformations can be effectively obtained.
J2 invariant relative orbits via differential correction algorithm
Ming Xu; Shijie Xu
2007-01-01
This paper describes a practical method for finding the invariant orbits in J2 relative dynamics. Working with the Hamiltonian model of the relative motion in cludingthe J2 perturbation, the effective differential correction algorithm for finding periodic orbits in three-body problem is extended to formation flying of Earth's orbiters. Rather than using orbital elements, the analysis is done directly in physical space, which makes a direct connection with physical requirements. The asymptotic behavior of the invariant orbit is indicated by its stable and unstable manifolds. The period of the relative orbits is proved numerically to be slightly different from the ascending node period of the leader satellite,and a preliminary explanation for this phenomenon is presented. Then the compatibility between J2 invariant orbit and desired relative geometry is considered, and the design procedure for the initial values of the compatible configurationis proposed. The influences of measure errors on the invariantorbit are also investigated by the Monte-Carlo simulation.
A SLAM based on auxiliary marginalised particle filter and differential evolution
Havangi, R.; Nekoui, M. A.; Teshnehlab, M.; Taghirad, H. D.
2014-09-01
FastSLAM is a framework for simultaneous localisation and mapping (SLAM) using a Rao-Blackwellised particle filter. In FastSLAM, particle filter is used for the robot pose (position and orientation) estimation, and parametric filter (i.e. EKF and UKF) is used for the feature location's estimation. However, in the long term, FastSLAM is an inconsistent algorithm. In this paper, a new approach to SLAM based on hybrid auxiliary marginalised particle filter and differential evolution (DE) is proposed. In the proposed algorithm, the robot pose is estimated based on auxiliary marginal particle filter that operates directly on the marginal distribution, and hence avoids performing importance sampling on a space of growing dimension. In addition, static map is considered as a set of parameters that are learned using DE. Compared to other algorithms, the proposed algorithm can improve consistency for longer time periods and also, improve the estimation accuracy. Simulations and experimental results indicate that the proposed algorithm is effective.
Biwei Tang
2016-05-01
Full Text Available Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of nondeterministic polynomial-time hard (NP-hard. Particle swarm optimization (PSO has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO, is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self adaptive DE (RBSADE, is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
A Novel Resource-Leveling Approach for Construction Project Based on Differential Evolution
Hong-Hai Tran
2014-01-01
Full Text Available In construction engineering, project schedules are commonly established by the critical path method. Nevertheless, these schedules often lead to substantial fluctuations in the resource profile that are not only impractical but also costly for the contractors to execute. Therefore, in order to smooth out the resource profile, construction managers need to perform resource-leveling procedures. This paper proposes a novel approach for resource leveling, named as resource leveling based on differential evolution (RLDE. The performance of the RLDE is compared to that of Microsoft Project software, the genetic algorithm, and the particle swarm optimization algorithm. Experiments have proved that the newly developed method can deliver the most desirable resource-leveling result. Thus, the RLDE is an effective method and it can be a useful tool for assisting managers/planners in the field of project management.
J.Jithendranath
2013-07-01
Full Text Available This paper presents an evolutionary based algorithm for solving optimal reactive power dispatch problem in power system. The problem was designed as a Multi-Objective case with loss minimization and voltage stability as objectives. Generator terminal voltages, tap setting of transformers and reactive power generation of capacitor banks were taken as optimization variables. Modal analysis method is adopted to assess the voltage stability of system. The above presented problem was solved on basis of efficient and reliable technique among all evolutionary based algorithms, the Differential Evolution Technique. The proposed method has been tested on IEEE 30 bus system where the obtained results were found satisfactorily to a large extent that of reported earlier.
An Improved Differential Evolution Trained Neural Network Scheme for Nonlinear System Identification
Bidyadhar Subudhi; Debashisha Jena
2009-01-01
This paper prescnts an improved nonlinear system identification scheme using differential evolution (DE), neural network (NN) and Levenberg Marquardt algorithm (LM). With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE+NN on a numbcr of examples including a practical case study. The identification rcsults obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.
A Differential Evolution Approach for Protein Folding Using a Lattice Model
Heitor Silverio Lopes; Reginaldo Bitello
2007-01-01
Protein folding is a relevant computational problem in Bioinformatics, for which many heuristic algorithms have been proposed. This work presents a methodology for the application of differential evolution (DE) to the problem of protein folding, using the bi-dimensional hydrophobic-polar model. DE is a relatively recent evolutionary algorithm, and has been used successfully in several engineering optimization problems, usually with continuous variables. We introduce the concept of genotype-phenotype mapping in DE in order to provide a mapping between the real-valued vector and an actual folding. The methodology is detailed and several experiments with benchmarks are done. We compared the results with other similar implementations. The proposed DE has shown to be competitive, statistically consistent and very promising.
Aerodynamic optimization of supersonic compressor cascade using differential evolution on GPU
Aissa, Mohamed Hasanine; Verstraete, Tom; Vuik, Cornelis
2016-06-01
Differential Evolution (DE) is a powerful stochastic optimization method. Compared to gradient-based algorithms, DE is able to avoid local minima but requires at the same time more function evaluations. In turbomachinery applications, function evaluations are performed with time-consuming CFD simulation, which results in a long, non affordable, design cycle. Modern High Performance Computing systems, especially Graphic Processing Units (GPUs), are able to alleviate this inconvenience by accelerating the design evaluation itself. In this work we present a validated CFD Solver running on GPUs, able to accelerate the design evaluation and thus the entire design process. An achieved speedup of 20x to 30x enabled the DE algorithm to run on a high-end computer instead of a costly large cluster. The GPU-enhanced DE was used to optimize the aerodynamics of a supersonic compressor cascade, achieving an aerodynamic loss minimization of 20%.
Bakkiyaraj, Ashok; Kumarappan, N.
2015-09-01
This paper presents a new approach for evaluating the reliability indices of a composite power system that adopts binary differential evolution (BDE) algorithm in the search mechanism to select the system states. These states also called dominant states, have large state probability and higher loss of load curtailment necessary to maintain real power balance. A chromosome of a BDE algorithm represents the system state. BDE is not applied for its traditional application of optimizing a non-linear objective function, but used as tool for exploring more number of dominant states by producing new chromosomes, mutant vectors and trail vectors based on the fitness function. The searched system states are used to evaluate annualized system and load point reliability indices. The proposed search methodology is applied to RBTS and IEEE-RTS test systems and results are compared with other approaches. This approach evaluates the indices similar to existing methods while analyzing less number of system states.
S. S. Motsa
2014-01-01
Full Text Available This paper presents a new application of the homotopy analysis method (HAM for solving evolution equations described in terms of nonlinear partial differential equations (PDEs. The new approach, termed bivariate spectral homotopy analysis method (BISHAM, is based on the use of bivariate Lagrange interpolation in the so-called rule of solution expression of the HAM algorithm. The applicability of the new approach has been demonstrated by application on several examples of nonlinear evolution PDEs, namely, Fisher’s, Burgers-Fisher’s, Burger-Huxley’s, and Fitzhugh-Nagumo’s equations. Comparison with known exact results from literature has been used to confirm accuracy and effectiveness of the proposed method.
A quantum search algorithm based on partial adiabatic evolution
Zhang Ying-Yu; Hu He-Ping; Lu Song-Feng
2011-01-01
This paper presents and implements a specified partial adiabatic search algorithm on a quantum circuit. It studies the minimum energy gap between the first excited state and the ground state of the system Hamiltonian and it finds that, in the case of M=1, the algorithm has the same performance as the local adiabatic algorithm. However, the algorithm evolves globally only within a small interval, which implies that it keeps the advantages of global adiabatic algorithms without losing the speedup of the local adiabatic search algorithm.
Estimation of drying parameters in rotary dryers using differential evolution
Lobato, F S; Jr, V Steffen; Barrozo, M A S; Arruda, E B, E-mail: vsteffen@mecanica.ufu.br, E-mail: masbarrozo@ufu.br
2008-11-01
Inverse problems arise from the necessity of obtaining parameters of theoretical models to simulate the behavior of the system for different operating conditions. Several heuristics that mimic different phenomena found in nature have been proposed for the solution of this kind of problem. In this work, the Differential Evolution Technique is used for the estimation of drying parameters in realistic rotary dryers, which is formulated as an optimization problem by using experimental data. Test case results demonstrate both the feasibility and the effectiveness of the proposed methodology.
MULTI OBJECTIVE ECONOMIC DISPATCH USING PARETO FRONTIER DIFFERENTIAL EVOLUTION
JAGADEESH GUNDA
2011-10-01
Full Text Available Multi Objective Economic dispatch (MOED problem has gained recent attention due to the deregulation of power industry and environmental regulations. So generating utilities should optimize their emission inaddition to the operating cost. In this paper a Pareto frontier Differential Evolution (PDE technique is developed to solve MOED problem, which provides a set of feasible solutions to the problem. To evaluate the performance and applicability of the proposed method, it is implemented on the standard IEEE-30 bus system having six generating units including valve point effects. The results obtained demonstrate the effectiveness of the proposed method for solving the Multi Objective economic dispatch problem considering security constraints.
Differential evolution to enhance localization of mobile robots
Lisowski, Michal; Fan, Zhun; Ravn, Ole
2011-01-01
This paper focuses on the mobile robot localization problems: pose tracking, global localization and robot kidnap. Differential Evolution (DE) applied to extend Monte Carlo Localization (MCL) was investigated to better solve localization problem by increasing localization reliability and speed....... In addition, a novel mechanism for effective robot kidnap detection was proposed. Experiments were performed using computer simulations based on the odometer data and laser range finder measurements collected in advance by a robot in real-life. Experimental results showed that integrating DE enables MCL...
Mitra, Avik; Ghosh, Arindam; Das, Ranabir; Patel, Apoorva; Kumar, Anil
2005-12-01
Quantum adiabatic algorithm is a method of solving computational problems by evolving the ground state of a slowly varying Hamiltonian. The technique uses evolution of the ground state of a slowly varying Hamiltonian to reach the required output state. In some cases, such as the adiabatic versions of Grover's search algorithm and Deutsch-Jozsa algorithm, applying the global adiabatic evolution yields a complexity similar to their classical algorithms. However, using the local adiabatic evolution, the algorithms given by J. Roland and N.J. Cerf for Grover's search [J. Roland, N.J. Cerf, Quantum search by local adiabatic evolution, Phys. Rev. A 65 (2002) 042308] and by Saurya Das, Randy Kobes, and Gabor Kunstatter for the Deutsch-Jozsa algorithm [S. Das, R. Kobes, G. Kunstatter, Adiabatic quantum computation and Deutsh's algorithm, Phys. Rev. A 65 (2002) 062301], yield a complexity of order N (where N=2(n) and n is the number of qubits). In this paper, we report the experimental implementation of these local adiabatic evolution algorithms on a 2-qubit quantum information processor, by Nuclear Magnetic Resonance.
Sang Yong Han
2009-05-01
Full Text Available This paper applies the Differential Evolution (DE algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II and Multi-Objective Clustering with an unknown number of Clusters K (MOCK. Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.
Suresh, Kaushik; Kundu, Debarati; Ghosh, Sayan; Das, Swagatam; Abraham, Ajith; Han, Sang Yong
2009-01-01
This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.
Simulating Evolution of Drosophila melanogaster Ebony Mutants Using a Genetic Algorithm
Helles, Glennie
2009-01-01
Genetic algorithms are generally quite easy to understand and work with, and they are a popular choice in many cases. One area in which genetic algorithms are widely and successfully used is artificial life where they are used to simulate evolution of artificial creatures. However, despite...... their suggestive name, simplicity and popularity in artificial life, they do not seem to have gained a footing within the field of population genetics to simulate evolution of real organisms --- possibly because genetic algorithms are based on a rather crude simplification of the evolutionary mechanisms known...... today. However, in this paper we report how a standard genetic algorithm is used to successfully simulate evolution of ebony mutants in a population of Drosophila melanogaster (D.melanogaster). The results show a remarkable resemblance to the evolution observed in real biological experiments with ebony...
A hybrid algorithm for Caputo fractional differential equations
Salgado, G. H. O.; Aguirre, L. A.
2016-04-01
This paper is concerned with the numerical solution of fractional initial value problems (FIVP) in sense of Caputo's definition for dynamical systems. Unlike for integer-order derivatives that have a single definition, there is more than one definition of non integer-order derivatives and the solution of an FIVP is definition-dependent. In this paper, the chief differences of the main definitions of fractional derivatives are revisited and a numerical algorithm to solve an FIVP for Caputo derivative is proposed. The main advantages of the algorithm are twofold: it can be initialized with integer-order derivatives, and it is faster than the corresponding standard algorithm. The performance of the proposed algorithm is illustrated with examples which suggest that it requires about half the computation time to achieve the same accuracy than the standard algorithm.
Sotirios K. Goudos
2015-01-01
Full Text Available This paper addresses the problem of designing shaped beam patterns with arbitrary arrays subject to constraints. The constraints could include the sidelobe level suppression in specified angular intervals, the mainlobe halfpower beamwidth, and the predefined number of elements. In this paper, we propose a new Differential Evolution algorithm, which combines Composite DE with an eigenvector-based crossover operator (CODE-EIG. This operator utilizes eigenvectors of covariance matrix of individual solutions, which makes the crossover rotationally invariant. We apply this novel design method to shaped beam pattern synthesis for linear and conformal arrays. We compare this algorithm with other popular algorithms and DE variants. The results show CODE-EIG outperforms the other DE algorithms in terms of statistical results and convergence speed.
Optimization of Neutrino Oscillation Parameters Using Differential Evolution
Ghulam Mustafa; Faisal Akram; Bilal Masud
2013-01-01
We show how the traditional grid based method for finding neutrino oscillation parameters △m2 and tan2θ can be combined with an optimization technique,Differential Evolution (DE),to get a significant decrease in computer processing time required to obtain minimal chi-square (x2) in four different regions of the parameter space.We demonstrate efficiency for the two-neutrinos case.For this,the x2 function for neutrino oscillations is evaluated for grids with different density of points in standard allowed regions of the parameter space of △m2 and tan2 θ using experimental and theoretical total event rates of chlorine (Homestake),Gallex+GNO,SAGE,Superkamiokande,and SNO detectors.We find that using DE in combination with the grid based method with small density of points can produce the results comparable with the one obtained using high density grid,in much lesser computation time.
Nonlinear evolution operators and semigroups applications to partial differential equations
Pavel, Nicolae H
1987-01-01
This research monograph deals with nonlinear evolution operators and semigroups generated by dissipative (accretive), possibly multivalued operators, as well as with the application of this theory to partial differential equations. It shows that a large class of PDE's can be studied via the semigroup approach. This theory is not available otherwise in the self-contained form provided by these Notes and moreover a considerable part of the results, proofs and methods are not to be found in other books. The exponential formula of Crandall and Liggett, some simple estimates due to Kobayashi and others, the characterization of compact semigroups due to Brézis, the proof of a fundamental property due to Ursescu and the author and some applications to PDE are of particular interest. Assuming only basic knowledge of functional analysis, the book will be of interest to researchers and graduate students in nonlinear analysis and PDE, and to mathematical physicists.
Image Encryption Using Differential Evolution Approach in Frequency Domain
Hassan, Maaly Awad S; 10.5121/sipij.2011.2105
2011-01-01
This paper presents a new effective method for image encryption which employs magnitude and phase manipulation using Differential Evolution (DE) approach. The novelty of this work lies in deploying the concept of keyed discrete Fourier transform (DFT) followed by DE operations for encryption purpose. To this end, a secret key is shared between both encryption and decryption sides. Firstly two dimensional (2-D) keyed discrete Fourier transform is carried out on the original image to be encrypted. Secondly crossover is performed between two components of the encrypted image, which are selected based on Linear Feedback Shift Register (LFSR) index generator. Similarly, keyed mutation is performed on the real parts of a certain components selected based on LFSR index generator. The LFSR index generator initializes it seed with the shared secret key to ensure the security of the resulting indices. The process shuffles the positions of image pixels. A new image encryption scheme based on the DE approach is developed...
Rearrangements of immunoglobulin genes during differentiation and evolution.
Honjo, T; Nakai, S; Nishida, Y; Kataoka, T; Yamawaki-Kataoka, Y; Takahashi, N; Obata, M; Shimizu, A; Yaoita, Y; Nikaido, T; Ishida, N
1981-01-01
Immunoglobulin genes are shown to undergo dynamic rearrangements during differentiation as well as evolution. We have demonstrated that a complete immunoglobulin heavy chain gene is formed by at least two types of DNA rearrangement during B cell differentiation. The first type of rearrangement is V-D-J recombination to complete a variable region sequence and the second type is S-S recombination to switch a constant region sequence. Both types of recombination are accompanied by deletion of the intervening DNA segment. Structure and organization of CH genes are elucidated by molecular cloning and nucleotide sequence determination. Organization of H chain genes is summarized as VH-(unknown distance)-JH-(6.5 kb)-C mu-(4.5 kb)-C delta-(unknown distance)-C gamma 3-(34 kb)-C gamma 1-(21 kb)-C gamma 2b-(15 kb)-C gamma 2a-(14.5 kb)-C epsilon-(12.5 kb)-C alpha. The S-S recombination takes place at the S region which is located at the 5' side of each CH gene. Nucleotide sequence of the S region comprises tandem repetition of closely related sequences. The S-S recombination seems to be mediated by short common sequences shared among S regions. A sister chromatid exchange model was proposed as a mechanism for S-S recombination. Comparison of nucleotide sequences of CH genes indicates that immunoglobulin genes have scrambled by intervening sequence-mediated domain transfer during their evolution.
Minggang Dong
2014-01-01
Full Text Available Motivated by recent advancements in differential evolution and constraints handling methods, this paper presents a novel modified oracle penalty function-based composite differential evolution (MOCoDE for constrained optimization problems (COPs. More specifically, the original oracle penalty function approach is modified so as to satisfy the optimization criterion of COPs; then the modified oracle penalty function is incorporated in composite DE. Furthermore, in order to solve more complex COPs with discrete, integer, or binary variables, a discrete variable handling technique is introduced into MOCoDE to solve complex COPs with mix variables. This method is assessed on eleven constrained optimization benchmark functions and seven well-studied engineering problems in real life. Experimental results demonstrate that MOCoDE achieves competitive performance with respect to some other state-of-the-art approaches in constrained optimization evolutionary algorithms. Moreover, the strengths of the proposed method include few parameters and its ease of implementation, rendering it applicable to real life. Therefore, MOCoDE can be an efficient alternative to solving constrained optimization problems.
Vrugt, Jasper A [Los Alamos National Laboratory; Hyman, James M [Los Alamos National Laboratory; Robinson, Bruce A [Los Alamos National Laboratory; Higdon, Dave [Los Alamos National Laboratory; Ter Braak, Cajo J F [NETHERLANDS; Diks, Cees G H [UNIV OF AMSTERDAM
2008-01-01
Markov chain Monte Carlo (MCMC) methods have found widespread use in many fields of study to estimate the average properties of complex systems, and for posterior inference in a Bayesian framework. Existing theory and experiments prove convergence of well constructed MCMC schemes to the appropriate limiting distribution under a variety of different conditions. In practice, however this convergence is often observed to be disturbingly slow. This is frequently caused by an inappropriate selection of the proposal distribution used to generate trial moves in the Markov Chain. Here we show that significant improvements to the efficiency of MCMC simulation can be made by using a self-adaptive Differential Evolution learning strategy within a population-based evolutionary framework. This scheme, entitled DiffeRential Evolution Adaptive Metropolis or DREAM, runs multiple different chains simultaneously for global exploration, and automatically tunes the scale and orientation of the proposal distribution in randomized subspaces during the search. Ergodicity of the algorithm is proved, and various examples involving nonlinearity, high-dimensionality, and multimodality show that DREAM is generally superior to other adaptive MCMC sampling approaches. The DREAM scheme significantly enhances the applicability of MCMC simulation to complex, multi-modal search problems.
A least mean squares CUBIC algorithm for on-line differential of sampled analog signals
Allum, J. H. J.
1975-01-01
A digital computer algorithm is developed for on-line time differentiation of sampled analog voltage signals. The derivative is obtained by employing a least mean squares technique. The recursive algorithm results in a considerable reduction in computer time compared to a complete new solution of the normal equations each time a new data point is accepted. Implementation of the algorithm on a digital computer is discussed. Examples are simulated on a DEC PDP-8 computer.
SGO: A fast engine for ab initio atomic structure global optimization by differential evolution
Chen, Zhanghui; Jia, Weile; Jiang, Xiangwei; Li, Shu-Shen; Wang, Lin-Wang
2017-10-01
As the high throughout calculations and material genome approaches become more and more popular in material science, the search for optimal ways to predict atomic global minimum structure is a high research priority. This paper presents a fast method for global search of atomic structures at ab initio level. The structures global optimization (SGO) engine consists of a high-efficiency differential evolution algorithm, accelerated local relaxation methods and a plane-wave density functional theory code running on GPU machines. The purpose is to show what can be achieved by combining the superior algorithms at the different levels of the searching scheme. SGO can search the global-minimum configurations of crystals, two-dimensional materials and quantum clusters without prior symmetry restriction in a relatively short time (half or several hours for systems with less than 25 atoms), thus making such a task a routine calculation. Comparisons with other existing methods such as minima hopping and genetic algorithm are provided. One motivation of our study is to investigate the properties of magnetic systems in different phases. The SGO engine is capable of surveying the local minima surrounding the global minimum, which provides the information for the overall energy landscape of a given system. Using this capability we have found several new configurations for testing systems, explored their energy landscape, and demonstrated that the magnetic moment of metal clusters fluctuates strongly in different local minima.
ZHANG Xing; BAI YongQiang; XIN Bin; CHEN Jie
2013-01-01
This paper presents online motion planning for UAV (unmanned aerial vehicle) in complex threat field,including both static threats and moving threats,which can be formulated as a dynamic constrained optimal control problem.Receding horizon control (RHC) based on differential evolution (DE) algorithm is adopted.A location-predicting model of moving threats is established to assess the value of threat that UAV faces in flight.Then flyable paths can be generated by the control inputs which are optimized by DE under the guidance of the objective function.Simulation results demonstrate that the proposed method not only generates smooth and flyable paths,but also enables UAV to avoid threats efficiently and arrive at destination safely.
Kela, K. B.; Arya, L. D.
2014-09-01
This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system.
Kuiper, W.E.; Cozijnsen, A.J.
2011-01-01
We outline a new estimation method for the multinomial probit model (MNP). The method is a differential evolution Markov chain algorithm that employs a Metropolis-within-Gibbs sampler with data augmentation and the Geweke–Hajivassiliou–Keane (GHK) probability simulator. The method lifts the curse of
Motion Planning Using a Memetic Evolution Algorithm for Swarm Robots
Chien-Chou Lin
2012-05-01
Full Text Available A hierarchical memetic algorithm (MA is proposed for the path planning and formation control of swarm robots. The proposed algorithm consists of a global path planner (GPP and a local motion planner (LMP. The GPP plans a trajectory within the Voronoi diagram (VD of the free space. An MA with a non‐random initial population plans a series of configurations along the path given by the former stage. The MA locally adjusts the robot positions to search for better fitness along the gradient direction of the distance between the swarm robots and the intermediate goals (IGs. Once the optimal configuration is obtained, the best chromosomes are reserved as the initial population for the next generation. Since the proposed MA has a non‐random initial population and local searching, it is more efficient and the planned path is faster compared to a traditional genetic algorithm (GA. The simulation results show that the proposed algorithm works well in terms of path smoothness and computation efficiency.
NUMERICAL METHOD BASED ON HAMILTON SYSTEM AND SYMPLECTIC ALGORITHM TO DIFFERENTIAL GAMES
无
2006-01-01
The resolution of differential games often concerns the difficult problem of two points border value (TPBV), then ascribe linear quadratic differential game to Hamilton system. To Hamilton system, the algorithm of symplectic geometry has the merits of being able to copy the dynamic structure of Hamilton system and keep the measure of phase plane. From the viewpoint of Hamilton system, the symplectic characters of linear quadratic differential game were probed; as a try, Symplectic-Runge-Kutta algorithm was presented for the resolution of infinite horizon linear quadratic differential game. An example of numerical calculation was given, and the result can illuminate the feasibility of this method. At the same time, it embodies the fine conservation characteristics of symplectic algorithm to system energy.
Itoh, Jyunpei; Yamamoto, Masayoshi; Funabiki, Shigeyuki
Electric power demand has an increasing tendency year by year. The fluctuation of the electric power causes further increase in the cost of the electric power facility and electricity charges. The development of the electric power-leveling systems (EPLS) using energy storage technology is desired to improve the electric power quality. The EPLS with a SMES is proposed as one of the countermeasures for the electric power quality improvement. However, the SMES is very expensive and it is difficult to decide the gains of the controller. It is essential in the practical use that the reduction of SMES capacity is realized. This paper proposes a new optimization method of the EPLS. The proposed algorithm is hybrid architecture with a combination of SimE (Simulated Evolution) and GA (Genetic Algorithms). The optimization of the EPLS can be achieved by the proposed hybrid algorithm compared to the SimE and the GA.
Peak alignment using wavelet pattern matching and differential evolution.
Zhang, Zhi-Min; Chen, Shan; Liang, Yi-Zeng
2011-01-30
Retention time shifts badly impair qualitative or quantitative results of chemometric analyses when entire chromatographic data are used. Hence, chromatograms should be aligned to perform further analysis. Being inspired and motivated by this purpose, a practical and handy peak alignment method (alignDE) is proposed, implemented in this research for one-way chromatograms, which basically consists of five steps: (1) chromatogram lengths equalization using linear interpolation; (2) accurate peak pattern matching by continuous wavelet transform (CWT) with the Mexican Hat and Haar wavelets as its mother wavelets; (3) flexible baseline fitting utilizing penalized least squares; (4) peak clustering when gap of two peaks is smaller than a certain threshold; (5) peak alignment using differential evolution (DE) to maximize linear correlation coefficient between reference signal and signal to be aligned. This method is demonstrated with both simulated chromatograms and real chromatograms, for example, chromatograms of fungal extracts and Red Peony Root obtained by HPLC-DAD. It is implemented in R language and available as open source software to a broad range of chromatograph users (http://code.google.com/p/alignde).
Calogero, Francesco
2016-10-01
Recently a simple differential algorithm to compute all the zeros of a generic polynomial was introduced. In this paper an analogous, but finite-difference, algorithm is introduced and discussed. At the end of the paper a minor generalization of the differential algorithm is also mentioned.
Differential Evolution and Particle Swarm Optimization for Partitional Clustering
Krink, Thiemo; Paterlini, Sandra
2006-01-01
Many partitional clustering algorithms based on genetic algorithms (GA) have been proposed to tackle the problem of finding the optimal partition of a data set. Very few studies considered alternative stochastic search heuristics other than GAs or simulated annealing. Two promising algorithms...... to implement and requires hardly any parameter tuning compared to substantial tuning for GAs and PSOs. Our study shows that DE rather than GAs should receive primary attention in partitional clustering algorithms....
Efficient algorithms for reconstructing gene content by co-evolution
Tuller Tamir
2011-10-01
Full Text Available Abstract Background In a previous study we demonstrated that co-evolutionary information can be utilized for improving the accuracy of ancestral gene content reconstruction. To this end, we defined a new computational problem, the Ancestral Co-Evolutionary (ACE problem, and developed algorithms for solving it. Results In the current paper we generalize our previous study in various ways. First, we describe new efficient computational approaches for solving the ACE problem. The new approaches are based on reductions to classical methods such as linear programming relaxation, quadratic programming, and min-cut. Second, we report new computational hardness results related to the ACE, including practical cases where it can be solved in polynomial time. Third, we generalize the ACE problem and demonstrate how our approach can be used for inferring parts of the genomes of non-ancestral organisms. To this end, we describe a heuristic for finding the portion of the genome ('dominant set’ that can be used to reconstruct the rest of the genome with the lowest error rate. This heuristic utilizes both evolutionary information and co-evolutionary information. We implemented these algorithms on a large input of the ACE problem (95 unicellular organisms, 4,873 protein families, and 10, 576 of co-evolutionary relations, demonstrating that some of these algorithms can outperform the algorithm used in our previous study. In addition, we show that based on our approach a ’dominant set’ cab be used reconstruct a major fraction of a genome (up to 79% with relatively low error-rate (e.g. 0.11. We find that the ’dominant set’ tends to include metabolic and regulatory genes, with high evolutionary rate, and low protein abundance and number of protein-protein interactions. Conclusions The ACE problem can be efficiently extended for inferring the genomes of organisms that exist today. In addition, it may be solved in polynomial time in many practical cases
Adaptive cockroach swarm algorithm
Obagbuwa, Ibidun C.; Abidoye, Ademola P.
2017-07-01
An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms.
Aijun Zhu; Chuanpei Xu; Zhi Li; Jun Wu; Zhenbing Liu
2015-01-01
A new meta-heuristic method is proposed to enhance current meta-heuristic methods for global optimization and test scheduling for three-dimensional (3D) stacked system-on-chip (SoC) by hybridizing grey wolf optimization with differential evo-lution (HGWO). Because basic grey wolf optimization (GWO) is easy to fal into stagnation when it carries out the operation of at-tacking prey, and differential evolution (DE) is integrated into GWO to update the previous best position of grey wolf Alpha, Beta and Delta, in order to force GWO to jump out of the stagnation with DE’s strong searching ability. The proposed algorithm can accele-rate the convergence speed of GWO and improve its performance. Twenty-three wel-known benchmark functions and an NP hard problem of test scheduling for 3D SoC are employed to verify the performance of the proposed algorithm. Experimental results show the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.
Armando Céspedes-Mota
2016-01-01
Full Text Available Location information for wireless sensor nodes is needed in most of the routing protocols for distributed sensor networks to determine the distance between two particular nodes in order to estimate the energy consumption. Differential evolution obtains a suboptimal solution based on three features included in the objective function: area, energy, and redundancy. The use of obstacles is considered to check how these barriers affect the behavior of the whole solution. The obstacles are considered like new restrictions aside of the typical restrictions of area boundaries and the overlap minimization. At each generation, the best element is tested to check whether the node distribution is able to create a minimum spanning tree and then to arrange the nodes using the smallest distance from the initial position to the suboptimal end position based on the Hungarian algorithm. This work presents results for different scenarios delimited by walls and testing whether it is possible to obtain a suboptimal solution with inner obstacles. Also, a case with an area delimited by a star shape is presented showing that the algorithm is able to fill the whole area, even if such area is delimited for the peaks of the star.
Mohd Arfian Ismail
2017-09-01
Full Text Available In this paper, an improve method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contain with many components. In addition, the multi-objective problem also need to be considered. Due to that, this study proposed and improve method that comprises with Newton method, differential evolution algorithm (DE and competitive co-evolutionary algorithm(ComCA. The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems and the experimental results show that the proposed method perform well compared to other works.
A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem
Farhi, E; Gutmann, S; Lapan, J; Lundgren, A; Preda, D; Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam; Lapan, Joshua; Lundgren, Andrew; Preda, Daniel
2001-01-01
A quantum system will stay near its instantaneous ground state if the Hamiltonian that governs its evolution varies slowly enough. This quantum adiabatic behavior is the basis of a new class of algorithms for quantum computing. We test one such algorithm by applying it to randomly generated, hard, instances of an NP-complete problem. For the small examples that we can simulate, the quantum adiabatic algorithm works well, and provides evidence that quantum computers (if large ones can be built) may be able to outperform ordinary computers on hard sets of instances of NP-complete problems.
Chun-Liang Lu
2014-12-01
Full Text Available Differential evolution (DE is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP. Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations.
The Effective Clustering Partition Algorithm Based on the Genetic Evolution
LIAO Qin; LI Xi-wen
2006-01-01
To the problem that it is hard to determine the clustering number and the abnormal points by using the clustering validity function, an effective clustering partition model based on the genetic algorithm is built in this paper. The solution to the problem is formed by the combination of the clustering partition and the encoding samples, and the fitness function is defined by the distances among and within clusters. The clustering number and the samples in each cluster are determined and the abnormal points are distinguished by implementing the triple random crossover operator and the mutation. Based on the known sample data, the results of the novel method and the clustering validity function are compared. Numerical experiments are given and the results show that the novel method is more effective.
Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.
2017-04-01
In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.
Covariance and crossover matrix guided differential evolution for global numerical optimization.
Li, YongLi; Feng, JinFu; Hu, JunHua
2016-01-01
Differential evolution (DE) is an efficient and robust evolutionary algorithm and has wide application in various science and engineering fields. DE is sensitive to the selection of mutation and crossover strategies and their associated control parameters. However, the structure and implementation of DEs are becoming more complex because of the diverse mutation and crossover strategies that use distinct parameter settings during the different stages of the evolution. A novel strategy is used in this study to improve the crossover and mutation operations. The crossover matrix, instead of a crossover operator and its control parameter CR, is proposed to implement the function of the crossover operation. Meanwhile, Gaussian distribution centers the best individuals found in each generation based on the proposed covariance matrix, which is generated between the best individual and several better individuals. Improved mutation operator based on the crossover matrix is randomly selected to generate the trial population. This operator is used to generate high-quality solutions to improve the capability of exploitation and enhance the preference of exploration. In addition, the memory population is randomly chosen from previous generation and used to control the search direction in the novel mutation strategy. Accordingly, the diversity of the population is improved. Thus, CCDE, which is a novel efficient and simple DE variant, is presented in this paper. CCDE has been tested on 30 benchmarks and 5 real-world optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2011, respectively. Experimental and statistical results demonstrate the effectiveness of CCDE for global numerical and engineering optimization. CCDE can solve the test benchmark functions and engineering problems more successfully than the other DE variants and algorithms from CEC 2014.
Neumann, Wladimir; Breuer, Doris; Spohn, Tilman; Henke, Stephan; Gail, Hans-Peter; Schwarz, Winfried; Trieloff, Mario; Hopp, Jens
2015-04-01
The acapulcoites and lodranites are rare groups of achondritic meteorites. Several characteristics such as unique oxygen isotope composition and similar cosmic ray exposure ages indicate that these meteorites originate from a common parent body (Weigel et al. 1999). By contrast to both undifferentiated and differentiated meteorites, acapulcoites and lodranites are especially interesting because they experienced melting that was, however, not complete (McCoy et al. 2006). Thus, unravelling their origin contributes directly to the understanding of the initial differentiation stage of planetary objects in the Solar system. The information preserved in the structure and composition of meteorites can be recovered by modelling the evolution of their parent bodies and comparing the results with the laboratory investigations. Model calculations for the thermal evolution of the parent body of the Acapulco and Lodran-like meteorite clan were performed using two numerical models. Both models (from [3] and [4], termed (a) and (b), respectively) solve a 1D heat conduction equation in spherical symmetry considering heating by short- and long-lived radioactive isotopes, temperature- and porosity-dependent parameters, compaction of initially porous material, and melting. The calculations with (a) were compared to the maximum metamorphic temperatures and thermo-chronological data available for acapulcoites and lodranites. Applying a genetic algorithm, an optimised set of parameters of a common parent body was determined, which fits to the data for the cooling histories of these meteorites. The optimum fit corresponds to a body with the radius of 270 km and a formation time of 1.66 Ma after the CAIs. Using the model by (b) that considers differentiation by porous flow and magmatic heat transport, the differentiation of the optimum fit body was calculated. The resulting structure consists of a metallic core, a silicate mantle, a partially differentiated layer, an undifferentiated
无
2007-01-01
In this paper, a novel concept of the joint state of input and output of encoder is proposed. Based on it, a recursive algorithm that implements the multi-symbol differential detection in criterion of maximum likelihood is proposed. Simulation results show that its performance can approach to the theoretical boundary of multi-symbol differential detection without increasing the complexity per symbol, and it is effective in practical systems. It is also an optimal algorithm to solve the problems of estimating the state sequence of finite-state Markov process observed in memoryless noise.
Unified algorithm for partial differential equations and examples of numerical computation
Watanabe, Tsuguhiro [National Inst. for Fusion Science, Toki, Gifu (Japan)
1999-04-01
A new unified algorithm is proposed to solve partial differential equations which describe nonlinear boundary value problems, eigenvalue problems and time developing boundary value problems. The algorithm is composed of implicit difference scheme and multiple shooting scheme and is named as HIDM (Higher order Implicit Difference Method). A new prototype computer programs for 2-dimensional partial differential equations is constructed and tested successfully to several problems. Extension of the computer programs to 3 or more higher order dimension problems will be easy due to the direct product type difference scheme. (author)
Physicians' diagnoses compared with algorithmic differentiation of causes of jaundice.
Boom, R; Chavez-Oest, J; Gonzalez, C; Cantu, M A; Rivero, F; Reyes, A; Aguilar, E; Santamaria, J
1988-01-01
Clinical data were collected in 194 cases of jaundiced patients treated at the "Adolfo Lopez Mateos" ISSSTE Hospital in Mexico City from July 1985 to July 1986. A copy of the clinical history of each patient was given to each of four physicians--one recently graduated from medical school, another in his first year of gastroenterology, and two others who were experienced gastroenterologists. The same clinical data were processed by a computer set up to use a modified Danish COMIC algorithm. All physicians and the computer technician were blinded to the "gold standard" pathologic diagnoses, with which their diagnoses were compared. Accuracy rates of the physicians in distinguishing intrahepatic (medical) from extrahepatic (surgical) jaundice were 78%, 86%, 86%, and 91%, and the accuracy of computer-assisted diagnoses was 96%. Chi-squared analysis of the diagnoses of three of the physicians and those of the computer showed significant differences (p between 0.1 and 0.01). For the diagnoses of the remaining physician, however, no significant difference was found after chi-squared continuity correction.
Differentiating QoS and Un-conflicting Channel-timeslot Allocation Algorithm in WDM
LI Li-xiu; FENG Jin-yuan; YANG Qi-hong
2005-01-01
A new effective and reliable channel-timeslot allocation algorithm has been proposed, which is applied in WDM optical networks using fiber as their media to allocate the operational request. This new algorithm can differentiate the priority of all requests and avoid conflict and collision in the process of distributing channel and timeslot for the data waiting for transmission. The method in calculating the best wavelength channel and the best timeslot number is simply given. The algorithm is carried out in a math language. On the basis of the experiment results, the performance and feasibility of the algorithm are analyzed, which prove that the allocation method can reach the requirements of an algorithm both in space complexity and time complexity.
Day-ahead distributed energy resource scheduling using differential search algorithm
Soares, J.; Lobo, C.; Silva, M.
2015-01-01
considers that energy resources are managed by a VPP which establishes contracts with their owners. The full AC power flow calculation included in the model takes into account network constraints. This paper presents an application of differential search algorithm (DSA) for solving the day-ahead scheduling...... a contingency on the large wind farm and different forecasts regarding load demand....
Mpalane, Kealeboga
2016-09-01
Full Text Available encryption standard(AES) cryptographic algorithm implementation in a microcontroller crypto-device against differential power analysis (DPA) attacks. ChipWhisperer capture hardware Rev2 tool was used to collect 1000 power traces for DPA. We observed...
Ji-Huan He
2012-01-01
Full Text Available This paper applies an ancient Chinese algorithm to differential-difference equations, and a solitary-solution formulation is obtained. The discrete mKdV lattice equation is used as an example to elucidate the solution procedure.
Phien, Ho N; Vidal, Guifré
2014-01-01
We propose an environment recycling scheme to speed up a class of tensor network algorithms that produce an approximation to the ground state of a local Hamiltonian by simulating an evolution in imaginary time. Specifically, we consider the time-evolving block decimation (TEBD) algorithm applied to infinite systems in 1D and 2D, where the ground state is encoded, respectively, in a matrix product state (MPS) and in a projected entangled-pair state (PEPS). An important ingredient of the TEBD algorithm (and a main computational bottle-neck, especially with PEPS in 2D) is the computation of the so-called environment, which is used to determine how to optimally truncate the bond indices of the tensor network so that their dimension is kept constant. In current algorithms, the environment is computed at each step of the imaginary time evolution, to account for the changes that the time evolution introduces in the many-body state represented by the tensor network. Our key insight is that close to convergence, most ...
Guoliang Li
2017-01-01
Full Text Available We study the order acceptance and scheduling (OAS problem with time-dependent earliness-tardiness penalties in a single agile earth observation satellite environment where orders are defined by their release dates, available processing time windows ranging from earliest start date to deadline, processing times, due dates, sequence-dependent setup times, and revenues. The objective is to maximise total revenue, where the revenue from an order is a piecewise linear function of its earliness and tardiness with reference to its due date. We formulate this problem as a mixed integer linear programming model and develop a novel hybrid differential evolution (DE algorithm under self-adaptation framework to solve this problem. Compared with classical DE, hybrid DE employs two mutation operations, scaling factor adaptation and crossover probability adaptation. Computational tests indicate that the proposed algorithm outperforms classical DE in addition to two other variants of DE.
CHEN Jie; XIN Bin; PENG ZhiHong; PAN Feng
2009-01-01
This brief paper reports a hybrid algorithm we developed recently to solve the global optimization problems of multimodal functions, by combining the advantages of two powerful population-based metaheuristics-differential evolution (DE) and particle swarm optimization (PSO). In the hybrid denoted by DEPSO, each individual in one generation chooses its evolution method, DE or PSO, in a statistical learning way. The choice depends on the relative success ratio of the two methods in a previous learning period. The proposed DEPSO is compared with its PSO and DE parents, two advanced DE variants one of which is suggested by the originators of DE, two advanced PSO variants one of which is acknowledged as a recent standard by PSO community, and also a previous DEPSO. Benchmark tests demonstrate that the DEPSO is more competent for the global optimization of multimodal functions due to its high optimization quality.
EXISTENCE RESULTS FOR IMPULSIVE NEUTRAL EVOLUTION DIFFERENTIAL EQUATIONS WITH STATE-DEPENDENT DELAY
无
2011-01-01
This paper is mainly concerned with the existence of mild solutions to a first order impulsive neutral evolution differential equations with state-dependent delay. By suitable fixed point theorems combined with theories of evolution systems,we prove some existence theorems. As an application,an example is also given to illustrate the obtained results.
Farzinfar, Mahshid; Teoh, Eam Khwang; Xue, Zhong
2011-11-01
This study proposes an expectation-maximization (EM)-based curve evolution algorithm for segmentation of magnetic resonance brain images. In the proposed algorithm, the evolution curve is constrained not only by a shape-based statistical model but also by a hidden variable model from image observation. The hidden variable model herein is defined by the local voxel labeling, which is unknown and estimated by the expected likelihood function derived from the image data and prior anatomical knowledge. In the M-step, the shapes of the structures are estimated jointly by encoding the hidden variable model and the statistical prior model obtained from the training stage. In the E-step, the expected observation likelihood and the prior distribution of the hidden variables are estimated. In experiments, the proposed automatic segmentation algorithm is applied to multiple gray nuclei structures such as caudate, putamens and thalamus of three-dimensional magnetic resonance imaging in volunteers and patients. As for the robustness and accuracy of the segmentation algorithm, the results of the proposed EM-joint shape-based algorithm outperformed those obtained using the statistical shape model-based techniques in the same framework and a current state-of-the-art region competition level set method.
THE UNIFICATION OF DIFFERENTIAL ENCODING AND DECODING ALGORITHM FOR VARIOUS SIGNALS
无
2007-01-01
Many monographs point out that differential encoding and decoding is necessary for effectual information transmission against phase ambiguity while seldom discuss the reason why phase ambiguity will emerge inevitably. Available algorithms are specially designed for certain modulation scheme; these algorithms cannot satisfy the requirement of soft-defined radio, which perhaps demands a uniform algorithm for different modulations. This paper proposes a new opinion on phase ambiguity from the view of probability. This opinion believes that modulating symbol sequence can affect, at optimum sampling epoch, the modulated waveform as oscillating carrier has done, and so the stochastic sequence leads to phase ambiguity. Based on a general signal model, this paper also puts forward a novel universal algorithm, which is suitable for different signals, even some new ones, by configuring several parameters.
Panda, Sidhartha; Yegireddy, Narendra Kumar
2015-09-01
In this paper, a hybrid Improved Differential Evolution and Pattern Search (hIDEPS) approach is proposed for the design of a PI-Type Multi-Input Single Output (MISO) Static Synchronous Series Compensator (SSSC) based damping controller. The improvement in Differential Evolution (DE) algorithm is introduced by a simple but effective scheme of changing two of its most important control parameters i.e. step size and crossover probability with an objective of achieving improved performance. Pattern Search (PS) is subsequently employed to fine tune the best solution provided by modified DE algorithm. The superiority of a proposed hIDEPS technique over DE and improved DE has also been demonstrated. At the outset, this concept is applied to a SSSC connected in a Single Machine Infinite Bus (SMIB) power system and then extended to a multi-machine power system. To show the effectiveness and robustness of the proposed design approach, simulation results are presented and compared with DE and Particle Swarm Optimization (PSO) optimized Single Input Single Output (SISO) SSSC based damping controllers. It is observed that the proposed approach yield superior damping performance compared to some approaches available in the literature.
Culbreath, Karissa; Ager, Edward; Nemeyer, Ronald J.; Kerr, Alan
2012-01-01
We present the evolution of testing algorithms at our institution in which the C. Diff Quik Chek Complete immunochromatographic cartridge assay determines the presence of both glutamate dehydrogenase and Clostridium difficile toxins A and B as a primary screen for C. difficile infection and indeterminate results (glutamate dehydrogenase positive, toxin A and B negative) are confirmed by the GeneXpert C. difficile PCR assay. This two-step algorithm is a cost-effective method for highly sensitive detection of toxigenic C. difficile. PMID:22718938
Pandu Sandi Pratama
2012-12-01
Full Text Available This paper proposed an algorithm to track the obstacle position and avoid the moving objects for differential driving Automatic Guided Vehicles (AGV system in industrial environment. This algorithm has several abilities such as: to detect the moving objects, to predict the velocity and direction of moving objects, to predict the collision possibility and to plan the avoidance maneuver. For sensing the local environment and positioning, the laser measurement system LMS-151 and laser navigation system NAV-200 are applied. Based on the measurement results of the sensors, the stationary and moving obstacles are detected and the collision possibility is calculated. The velocity and direction of the obstacle are predicted using Kalman filter algorithm. Collision possibility, time, and position can be calculated by comparing the AGV movement and obstacle prediction result obtained by Kalman filter. Finally the avoidance maneuver using the well known tangent Bug algorithm is decided based on the calculation data. The effectiveness of proposed algorithm is verified using simulation and experiment. Several examples of experiment conditions are presented using stationary obstacle, and moving obstacles. The simulation and experiment results show that the AGV can detect and avoid the obstacles successfully in all experimental condition. [Keywords— Obstacle avoidance, AGV, differential drive, laser measurement system, laser navigation system].
Piovesan, Luis Sergio
1997-07-01
The appliance of two algorithms is evaluated, one based in Fourier analysis and other based in a rectangular transform technique over Fourier analysis, to be used in digital logical circuits (digital protection relays) for the purpose of differential protection of power transformers (ANSI 87T). The first chapter has a brief introduction about electrical protection. The second chapter discusses the general problems of transform protection, the development of digital technology and, with more detail, the differential protection associated to this technology. In this chapter are presented the particular aspects of transformers differential protection concerning sensibility, inrush current situations and harmonic distortions caused by transformer core saturations and the differential protection algorithms and their applications in a specific relay design. In chapter three, a method to make possible testing the protection performance is developed. This work applies digital simulations using EMTP to generate current signal of transformer operation and fault conditions. Digital simulation using Matlab is used to simulate the protection. The EMTP generated field signals are sent to the relay under test, furnishing data of normal operation, internal and external faults. The relay logic simulator at Matlab will work this data and so, it will be possible to verify and evaluate the algorithm behavior and performance. Chapter 4 shows the protection operation over simulations of several of transformer operation and fault conditions. The last chapter presents a conclusion about the protection performance, discussions about all the methods applied in this work and suggestions for further studies. (author)
Implementation of Evolution Strategies (ES) Algorithm to Optimization Lovebird Feed Composition
Agung Mustika Rizki; Wayan Firdaus Mahmudy; Gusti Eka Yuliastuti
2017-01-01
Lovebird current society, especially popular among bird lovers. Some people began to try to develop the cultivation of these birds. In the cultivation process to consider the composition of feed to produce a quality bird. Determining the feed is not easy because it must consider the cost and need for vitamin Lovebird. This problem can be solved by the algorithm Evolution Strategies (ES). Based on test results obtained optimal fitness value of 0.3125 using a population size of 100 and optimal ...
Milinkovitch Michel C
2007-11-01
Full Text Available Abstract Background Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME principle (aiming at recovering the phylogeny with shortest length is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@-hard class of problems. Results In this paper, we introduce an Ant Colony Optimization (ACO algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. Conclusion We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.
Dahlgren, Kathryn Marie [California State Univ., Turlock, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Morris, Karla Vanessa [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Debusschere, Bert [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2014-08-01
The future of extreme-scale computing is expected to magnify the influence of soft faults as a source of inaccuracy or failure in solutions obtained from distributed parallel computations. The development of resilient computational tools represents an essential recourse for understanding the best methods for absorbing the impacts of soft faults without sacrificing solution accuracy. The Rexsss (Resilient Extreme Scale Scientific Simulations) project pursues the development of fault resilient algorithms for solving partial differential equations (PDEs) on distributed systems. Performance analyses of current algorithm implementations assist in the identification of runtime inefficiencies.
An Improved Self-adaptive Control Parameter of Differential Evolution for Global Optimization
Jia, Liyuan; Gong, Wenyin; Wu, Hongbin
Differential evolution (DE), a fast and robust evolutionary algorithm for global optimization, has been widely used in many areas. However, the success of DE for solving different problems mainly depends on properly choosing the control parameter values. On the other hand, DE is good at exploring the search space and locating the region of global minimum, but it is slow at exploiting the solution. In order to alleviate these drawbacks of DE, this paper proposes an improved self-adaptive control parameter of DE, referred to as ISADE, for global numerical optimization. The proposed approach employs the individual fitness information to adapt the parameter settings. Hence, it can exploit the information of the individual and generate the promising offspring efficiently. To verify the viability of the proposed ISADE, 10 high-dimensional benchmark problems are chosen from literature. Experiment results indicate that this approach is efficient and effective. It is proved that this approach performs better than the original DE in terms of the convergence rate and the quality of the final solutions. Moreover, ISADE obtains faster convergence than the original self-adaptive control parameter of DE (SADE).
A microblog recommendation algorithm based on social tagging and a temporal interest evolution model
Zhen-ming YUAN; Chi HUANG; Xiao-yan SUN; Xing-xing LI; Dong-rong XU
2015-01-01
Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the topn microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred.
Dong, Li-Yang; Zhou, Wei-Zhong; Ni, Jun-Wei; Xiang, Wei; Hu, Wen-Hao; Yu, Chang; Li, Hai-Yan
2017-02-01
The objective of this study was to identify the optimal gene and gene set for hepatocellular carcinoma (HCC) utilizing differential expression and differential co-expression (DEDC) algorithm. The DEDC algorithm consisted of four parts: calculating differential expression (DE) by absolute t-value in t-statistics; computing differential co-expression (DC) based on Z-test; determining optimal thresholds on the basis of Chi-squared (χ2) maximization and the corresponding gene was the optimal gene; and evaluating functional relevance of genes categorized into different partitions to determine the optimal gene set with highest mean minimum functional information (FI) gain (Δ*G). The optimal thresholds divided genes into four partitions, high DE and high DC (HDE-HDC), high DE and low DC (HDE-LDC), low DE and high DC (LDE‑HDC), and low DE and low DC (LDE-LDC). In addition, the optimal gene was validated by conducting reverse transcription-polymerase chain reaction (RT-PCR) assay. The optimal threshold for DC and DE were 1.032 and 1.911, respectively. Using the optimal gene, the genes were divided into four partitions including: HDE-HDC (2,053 genes), HED-LDC (2,822 genes), LDE-HDC (2,622 genes), and LDE-LDC (6,169 genes). The optimal gene was microtubule‑associated protein RP/EB family member 1 (MAPRE1), and RT-PCR assay validated the significant difference between the HCC and normal state. The optimal gene set was nucleoside metabolic process (GO\\GO:0009116) with Δ*G = 18.681 and 24 HDE-HDC partitions in total. In conclusion, we successfully investigated the optimal gene, MAPRE1, and gene set, nucleoside metabolic process, which may be potential biomarkers for targeted therapy and provide significant insight for revealing the pathological mechanism underlying HCC.
An adaptive left-right eigenvector evolution algorithm for vibration isolation control
Wu, T. Y.
2009-11-01
The purpose of this research is to investigate the feasibility of utilizing an adaptive left and right eigenvector evolution (ALREE) algorithm for active vibration isolation. As depicted in the previous paper presented by Wu and Wang (2008 Smart Mater. Struct. 17 015048), the structural vibration behavior depends on both the disturbance rejection capability and mode shape distributions, which correspond to the left and right eigenvector distributions of the system, respectively. In this paper, a novel adaptive evolution algorithm is developed for finding the optimal combination of left-right eigenvectors of the vibration isolator, which is an improvement over the simultaneous left-right eigenvector assignment (SLREA) method proposed by Wu and Wang (2008 Smart Mater. Struct. 17 015048). The isolation performance index used in the proposed algorithm is defined by combining the orthogonality index of left eigenvectors and the modal energy ratio index of right eigenvectors. Through the proposed ALREE algorithm, both the left and right eigenvectors evolve such that the isolation performance index decreases, and therefore one can find the optimal combination of left-right eigenvectors of the closed-loop system for vibration isolation purposes. The optimal combination of left-right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active isolation control shows that the proposed method can be utilized to improve the vibration isolation performance compared with the previous approaches.
The knowledge instinct, cognitive algorithms, modeling of language and cultural evolution
Perlovsky, Leonid I.
2008-04-01
The talk discusses mechanisms of the mind and their engineering applications. The past attempts at designing "intelligent systems" encountered mathematical difficulties related to algorithmic complexity. The culprit turned out to be logic, which in one way or another was used not only in logic rule systems, but also in statistical, neural, and fuzzy systems. Algorithmic complexity is related to Godel's theory, a most fundamental mathematical result. These difficulties were overcome by replacing logic with a dynamic process "from vague to crisp," dynamic logic. It leads to algorithms overcoming combinatorial complexity, and resulting in orders of magnitude improvement in classical problems of detection, tracking, fusion, and prediction in noise. I present engineering applications to pattern recognition, detection, tracking, fusion, financial predictions, and Internet search engines. Mathematical and engineering efficiency of dynamic logic can also be understood as cognitive algorithm, which describes fundamental property of the mind, the knowledge instinct responsible for all our higher cognitive functions: concepts, perception, cognition, instincts, imaginations, intuitions, emotions, including emotions of the beautiful. I present our latest results in modeling evolution of languages and cultures, their interactions in these processes, and role of music in cultural evolution. Experimental data is presented that support the theory. Future directions are outlined.
A User Differential Range Error Calculating Algorithm Based on Analytic Method
SHAO Bo; LIU Jiansheng; ZHAO Ruibin; HUANG Zhigang; LI Rui
2011-01-01
To enhance the integrity,an analytic method (AM) which has less execution time is proposed to calculate the user differential range error (UDRE) used by the user to detect the potential risk.An ephemeris and clock correction calculation method is introduced first.It shows that the most important thing of computing UDRE is to find the worst user location (WUL) in the service volume.Then,a UDRE algorithm using AM is described to solve this problem.By using the covariance matrix of the error vector,the searching of WUL is converted to an analytic geometry problem.The location of WUL can be obtained directly by mathematical derivation.Experiments are conducted to compare the performance between the proposed AM algorithm and the exhaustive grid search (EGS) method used in the master station.The results show that the correctness of the AM algorithm can be proved by the EGS method and the AM algorithm can reduce the calculation time by more than 90%.The computational complexity of this proposed algorithm is better than that of EGS.Thereby this algorithm is more suitable for computing UDRE at the master station.
Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun
2017-07-01
Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.
A PARALLEL ARCHITECTURE FOR CURVE-EVOLUTION PARTIAL DIFFERENTIAL EQUATIONS
Eva Dejnožková; Petr Dokládal
2011-01-01
The computation of the distance function is a crucial and limiting element in many applications of image processing. This is particularly true for the PDE-based methods, where the distance is used to compute various geometric properties of the travelling curve. Massive Marchinga is a parallel algorithm computing the distance function by propagating the solution from the sources and permitting simultaneous spreading of component labels in the infiuence zones. Its hardware implementation is con...
DAI Chao-Qing; MENG Jian-Ping; ZHANG Jie-Fang
2005-01-01
The Jacobian elliptic function expansion method for nonlinear differential-different equations and its algorithm are presented by using some relations among ten Jacobian elliptic functions and successfully construct more new exact doubly-periodic solutions of the integrable discrete nonlinear Schrodinger equation. When the modulous m → 1or 0, doubly-periodic solutions degenerate to solitonic solutions including bright soliton, dark soliton, new solitons as well as trigonometric function solutions.
Karimi, Mohammad
2011-01-01
Many loads in power systems are inductive loads then consume reactive power, this fact lead to drop voltage and in worst case blackout and collapse voltage. Best option in distribution networks for avoid of this problem is installation of capacitor bank. In capacitor installation, finding optimal location and size of capacitor have special importance. In this paper, Differential Evolutionary (DE) algorithm is proposed for optimal placement and sizing of capacitor. Our objective funct...
DNA Lossless Differential Compression Algorithm based on Similarity of Genomic Sequence Database
Afify, Heba; Wahed, Manal Abdel
2011-01-01
Modern biological science produces vast amounts of genomic sequence data. This is fuelling the need for efficient algorithms for sequence compression and analysis. Data compression and the associated techniques coming from information theory are often perceived as being of interest for data communication and storage. In recent years, a substantial effort has been made for the application of textual data compression techniques to various computational biology tasks, ranging from storage and indexing of large datasets to comparison of genomic databases. This paper presents a differential compression algorithm that is based on production of difference sequences according to op-code table in order to optimize the compression of homologous sequences in dataset. Therefore, the stored data are composed of reference sequence, the set of differences, and differences locations, instead of storing each sequence individually. This algorithm does not require a priori knowledge about the statistics of the sequence set. The...
Analysis of planetary evolution with emphasis on differentiation and dynamics
Kaula, William M.; Newman, William I.
1987-01-01
In order to address the early stages of nebula evolution, a three-dimensional collapse code which includes not only hydrodynamics and radiative transfer, but also the effects of ionization and, possibly, magnetic fields is being addressed. As part of the examination of solar system evolution, an N-body code was developed which describes the latter stages of planet formation from the accretion of planetesimals. To test the code for accuracy and run-time efficiency, and to develop a stronger theoretical foundation, problems were studied in orbital dynamics. A regional analysis of the correlation in the gravity and topography fields of Venus was performed in order to determine the small and intermediate scale subsurface structure.
Loewner Theory in annulus I: evolution families and differential equations
Contreras, Manuel D; Gumenyuk, Pavel
2010-01-01
Loewner Theory, based on dynamical viewpoint, is a powerful tool in Complex Analysis, which plays a crucial role in such important achievements as the proof of famous Bieberbach's conjecture and well-celebrated Schramm's Stochastic Loewner Evolution (SLE). Recently Bracci et al [Bracci et al, to appear in J. Reine Angew. Math. Available on ArXiv 0807.1594; Bracci et al, Math. Ann. 344(2009), 947--962; Contreras et al, Revista Matematica Iberoamericana 26(2010), 975--1012] have proposed a new approach bringing together all the variants of the (deterministic) Loewner Evolution in a simply connected reference domain. We construct an analogue of this theory for the annulus. In this paper, the first of two articles, we introduce a general notion of an evolution family over a system of annuli and prove that there is a 1-to-1 correspondence between such families and semicomplete weak holomorphic vector fields. Moreover, in the non-degenerate case, we establish a constructive characterization of these vector fields a...
Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar
2013-11-01
Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal
Civicioglu, Pinar
2012-09-01
In order to solve numerous practical navigational, geodetic and astro-geodetic problems, it is necessary to transform geocentric cartesian coordinates into geodetic coordinates or vice versa. It is very easy to solve the problem of transforming geodetic coordinates into geocentric cartesian coordinates. On the other hand, it is rather difficult to solve the problem of transforming geocentric cartesian coordinates into geodetic coordinates as it is very hard to define a mathematical relationship between the geodetic latitude (φ) and the geocentric cartesian coordinates (X, Y, Z). In this paper, a new algorithm, the Differential Search Algorithm (DS), is presented to solve the problem of transforming the geocentric cartesian coordinates into geodetic coordinates and its performance is compared with the performances of the classical methods (i.e., Borkowski, 1989; Bowring, 1976; Fukushima, 2006; Heikkinen, 1982; Jones, 2002; Zhang, 2005; Borkowski, 1987; Shu, 2010 and Lin, 1995) and Computational-Intelligence algorithms (i.e., ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES). The statistical tests realized for the comparison of performances indicate that the problem-solving success of DS algorithm in transforming the geocentric cartesian coordinates into geodetic coordinates is higher than those of all classical methods and Computational-Intelligence algorithms used in this paper.
Reem A. Al-Omair
2009-03-01
Full Text Available In this paper we prove the existence of a mild solution for a semilinear evolution differential inclusion with nonlocal condition and governed by a family of linear operators, not necessarily bounded or closed, in a Banach space. No compactness assumption is assumed on the evolution operator generated by the family operators. Also, we prove that the set of mild solutions is compact.
Zhang, X.; Kusari, A.; Glennie, C. L.; Oskin, M. E.; Hinojosa-Corona, A.; Borsa, A. A.; Arrowsmith, R.
2013-12-01
Differential LiDAR (Light Detection and Ranging) from repeated surveys has recently emerged as an effective tool to measure three-dimensional (3D) change for applications such as quantifying slip and spatially distributed warping associated with earthquake ruptures, and examining the spatial distribution of beach erosion after hurricane impact. Currently, the primary method for determining 3D change is through the use of the iterative closest point (ICP) algorithm and its variants. However, all current studies using ICP have assumed that all LiDAR points in the compared point clouds have uniform accuracy. This assumption is simplistic given that the error for each LiDAR point is variable, and dependent upon highly variable factors such as target range, angle of incidence, and aircraft trajectory accuracy. Therefore, to rigorously determine spatial change, it would be ideal to model the random error for every LiDAR observation in the differential point cloud, and use these error estimates as apriori weights in the ICP algorithm. To test this approach, we implemented a rigorous LiDAR observation error propagation method to generate estimated random error for each point in a LiDAR point cloud, and then determine 3D displacements between two point clouds using an anistropic weighted ICP algorithm. The algorithm was evaluated by qualitatively and quantitatively comparing post earthquake slip estimates from the 2010 El Mayor-Cucapah Earthquake between a uniform weight and anistropically weighted ICP algorithm, using pre-event LiDAR collected in 2006 by Instituto Nacional de Estadística y Geografía (INEGI), and post-event LiDAR collected by The National Center for Airborne Laser Mapping (NCALM).
An implementation of differential search algorithm (DSA) for inversion of surface wave data
Song, Xianhai; Li, Lei; Zhang, Xueqiang; Shi, Xinchun; Huang, Jianquan; Cai, Jianchao; Jin, Si; Ding, Jianping
2014-12-01
Surface wave dispersion analysis is widely used in geophysics to infer near-surface shear (S)-wave velocity profiles for a wide variety of applications. However, inversion of surface wave data is challenging for most local-search methods due to its high nonlinearity and to its multimodality. In this work, we proposed and implemented a new Rayleigh wave dispersion curve inversion scheme based on differential search algorithm (DSA), one of recently developed swarm intelligence-based algorithms. DSA is inspired from seasonal migration behavior of species of the living beings throughout the year for solving highly nonlinear, multivariable, and multimodal optimization problems. The proposed inverse procedure is applied to nonlinear inversion of fundamental-mode Rayleigh wave dispersion curves for near-surface S-wave velocity profiles. To evaluate calculation efficiency and stability of DSA, four noise-free and four noisy synthetic data sets are firstly inverted. Then, the performance of DSA is compared with that of genetic algorithms (GA) by two noise-free synthetic data sets. Finally, a real-world example from a waste disposal site in NE Italy is inverted to examine the applicability and robustness of the proposed approach on surface wave data. Furthermore, the performance of DSA is compared against that of GA by real data to further evaluate scores of the inverse procedure described here. Simulation results from both synthetic and actual field data demonstrate that differential search algorithm (DSA) applied to nonlinear inversion of surface wave data should be considered good not only in terms of the accuracy but also in terms of the convergence speed. The great advantages of DSA are that the algorithm is simple, robust and easy to implement. Also there are fewer control parameters to tune.
Three dimensional evolution of differentially rotating magnetized neutron stars
Kiuchi, Kenta; Shibata, Masaru
2012-01-01
We construct a new three-dimensional general relativistic magnetohydrodynamics code, in which a fixed mesh refinement technique is implemented. To ensure the divergence-free condition as well as the magnetic flux conservation, we employ the method by Balsara (2001). Using this new code, we evolve differentially rotating magnetized neutron stars, and find that a magnetically driven outflow is launched from the star exhibiting a kink instability. The matter ejection rate and Poynting flux are still consistent with our previous finding (Shibata et al., 2011) obtained in axisymmetric simulations.
Optimum Parameters for Tuned Mass Damper Using Shuffled Complex Evolution (SCE Algorithm
Hessamoddin Meshkat Razavi
2015-06-01
Full Text Available This study is investigated the optimum parameters for a tuned mass damper (TMD under the seismic excitation. Shuffled complex evolution (SCE is a meta-heuristic optimization method which is used to find the optimum damping and tuning frequency ratio for a TMD. The efficiency of the TMD is evaluated by decreasing the structural displacement dynamic magnification factor (DDMF and acceleration dynamic magnification factor (ADMF for a specific vibration mode of the structure. The optimum TMD parameters and the corresponding optimized DDMF and ADMF are achieved for two control levels (displacement control and acceleration control, different structural damping ratio and mass ratio of the TMD system. The optimum TMD parameters are checked for a 10-storey building under earthquake excitations. The maximum storey displacement and acceleration obtained by SCE method are compared with the results of other existing approaches. The results show that the peak building response decreased with decreases of about 20% for displacement and 30% for acceleration of the top floor. To show the efficiency of the adopted algorithm (SCE, a comparison is also made between SCE and other meta-heuristic optimization methods such as genetic algorithm (GA, particle swarm optimization (PSO method and harmony search (HS algorithm in terms of success rate and computational processing time. The results show that the proposed algorithm outperforms other meta-heuristic optimization methods.
Tahir Nadeem MALIK; Salman ZAFAR; Saaqib HAROON
2015-01-01
Short-term hydrothermal scheduling (STHTS) is a non-linear and complex optimization problem with a set of oper-ational hydraulic and thermal constraints. Earlier, this problem has been addressed by several classical techniques;however, due to limitations such as non-linearity and non-convexity in cost curves, artificial intelligence tools based techniques are being used to solve the STHTS problem. In this paper an improved chaotic hybrid differential evolution (ICHDE) algorithm is proposed to find an optimal solution to this problem taking into account practical constraints. A self-adjusted parameter setting is obtained in differential evolution (DE) with the application of chaos theory, and a chaotic hybridized local search mechanism is embedded in DE to effectively prevent it from premature convergence. Furthermore, heuristic constraint handling techniques without any penalty factor setting are adopted to handle the complex hydraulic and thermal constraints. The superiority and effectiveness of the developed methodology are evaluated by its application in two illustrated hydrothermal test systems taken from the literature. The transmission line losses, prohibited discharge zones of hydel plants, and ramp rate limits of thermal plants are also taken into account. The simulation results reveal that the proposed technique is competent to produce an encouraging solution as com-pared with other recently established evolutionary approaches.
Implementation of Evolution Strategies (ES Algorithm to Optimization Lovebird Feed Composition
Agung Mustika Rizki
2017-05-01
Full Text Available Lovebird current society, especially popular among bird lovers. Some people began to try to develop the cultivation of these birds. In the cultivation process to consider the composition of feed to produce a quality bird. Determining the feed is not easy because it must consider the cost and need for vitamin Lovebird. This problem can be solved by the algorithm Evolution Strategies (ES. Based on test results obtained optimal fitness value of 0.3125 using a population size of 100 and optimal fitness value of 0.3267 in the generation of 1400.
Malicious Botnet Survivability Mechanism Evolution Forecasting by Means of a Genetic Algorithm
Nikolaj Goranin
2012-04-01
Full Text Available Botnets are considered to be among the most dangerous modern malware types and the biggest current threats to global IT infrastructure. Botnets are rapidly evolving, and therefore forecasting their survivability strategies is important for the development of countermeasure techniques. The article propose the botnet-oriented genetic algorithm based model framework, which aimed at forecasting botnet survivability mechanisms. The model may be used as a framework for forecasting the evolution of other characteristics. The efficiency of different survivability mechanisms is evaluated by applying the proposed fitness function. The model application area also covers scientific botnet research and modelling tasks.
YAN Zhen-Ya
2004-01-01
A Weierstrass elliptic function expansion method and its algorithm are developed in this paper. The method changes the problem solving nonlinear evolution equations into another one solving the correspondingsystem of nonlinear algebraic equations. With the aid of symbolic computation (e.g. Maple), the method is applied to the combined KdV-mKdV equation and (2+1)-dimensional coupled Davey-Stewartson equation. As a consequence, many new types of doubly periodic solutions are obtained in terms of the Weierstrass elliptic function. Jacobi elliptic function solutions and solitary wave solutions are also given as simple limits of doubly periodic solutions.
YANZhen-Ya
2004-01-01
A Weierstrass elliptic function expansion method and its algorithm are developed in this paper. The method changes the problem solving nonlinear evolution equations into another one solving the corresponding system of nonlinear algebraic equations. With the aid of symbolic computation (e.g. Maple), the method is applied to the combined KdV-mKdV equation and (2+1)-dimensional coupled Davey-Stewartson equation. As a consequence, many new types of doubly periodic solutions are obtained in terms of the Weierstrass elliptic function. Jacobi elliptic function solutions and solitary wave solutions are also given as simple limits of doubly periodic solutions.
Ighravwe, D. E.; Oke, S. A.; Adebiyi, K. A.
2017-08-01
This paper draws on the "human reliability" concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.
Finitely approximable random sets and their evolution via differential equations
Ananyev, B. I.
2016-12-01
In this paper, random closed sets (RCS) in Euclidean space are considered along with their distributions and approximation. Distributions of RCS may be used for the calculation of expectation and other characteristics. Reachable sets on initial data and some ways of their approximate evolutionary description are investigated for stochastic differential equations (SDE) with initial state in some RCS. Markov property of random reachable sets is proved in the space of closed sets. For approximate calculus, the initial RCS is replaced by a finite set on the integer multidimensional grid and the multistage Markov chain is substituted for SDE. The Markov chain is constructed by methods of SDE numerical integration. Some examples are also given.
Evolution and differential expression of a vertebrate vitellogenin gene cluster
Kongshaug Heidi
2009-01-01
Full Text Available Abstract Background The multiplicity or loss of the vitellogenin (vtg gene family in vertebrates has been argued to have broad implications for the mode of reproduction (placental or non-placental, cleavage pattern (meroblastic or holoblastic and character of the egg (pelagic or benthic. Earlier proposals for the existence of three forms of vertebrate vtgs present conflicting models for their origin and subsequent duplication. Results By integrating phylogenetics of novel vtg transcripts from old and modern teleosts with syntenic analyses of all available genomic variants of non-metatherian vertebrates we identify the gene orthologies between the Sarcopterygii (tetrapod branch and Actinopterygii (fish branch. We argue that the vertebrate vtg gene cluster originated in proto-chromosome m, but that vtg genes have subsequently duplicated and rearranged following whole genome duplications. Sequencing of a novel fourth vtg transcript in labrid species, and the presence of duplicated paralogs in certain model organisms supports the notion that lineage-specific gene duplications frequently occur in teleosts. The data show that the vtg gene cluster is more conserved between acanthomorph teleosts and tetrapods, than in ostariophysan teleosts such as the zebrafish. The differential expression of the labrid vtg genes are further consistent with the notion that neofunctionalized Aa-type vtgs are important determinants of the pelagic or benthic character of the eggs in acanthomorph teleosts. Conclusion The vertebrate vtg gene cluster existed prior to the separation of Sarcopterygii from Actinopterygii >450 million years ago, a period associated with the second round of whole genome duplication. The presence of higher copy numbers in a more highly expressed subcluster is particularly prevalent in teleosts. The differential expression and latent neofunctionalization of vtg genes in acanthomorph teleosts is an adaptive feature associated with oocyte hydration
A.V. Gusynin
2005-02-01
Full Text Available The approach to simulation of flight dynamics and numerically-analytical method of airship control algorithms are offered. It’s based on differential transformations of initial mathematical model of airship motion. The given approach allows for elimination of viewing time function for their differential spectra in the image field. It gives the possibility to reduce a problem of closed algorithm synthesis of vehicle control to the solution of non-linear equation system concerning control variable.
An FBP image reconstruction algorithm for x-ray differential phase contrast CT
Qi, Zhihua; Chen, Guang-Hong
2008-03-01
Most recently, a novel data acquisition method has been proposed and experimentally implemented for x-ray differential phase contrast computed tomography (DPC-CT), in which a conventional x-ray tube and a Talbot-Lau type interferometer were utilized in data acquisition. The divergent nature of the data acquisition system requires a divergent-beam image reconstruction algorithm for DPC-CT. This paper focuses on addressing this image reconstruction issue. We developed a filtered backprojection algorithm to directly reconstruct the DPC-CT images from acquired projection data. The developed algorithm allows one to directly reconstruct the decrement of the real part of the refractive index from the measured data. In order to accurately reconstruct an image, the data need to be acquired over an angular range of at least 180° plus the fan-angle. Different from the parallel beam data acquisition and reconstruction methods, a 180° rotation angle for data acquisition system does not provide sufficient data for an accurate reconstruction of the entire field of view. Numerical simulations have been conducted to validate the image reconstruction algorithm.
Cognitive algorithms: dynamic logic, working of the mind, evolution of consciousness and cultures
Perlovsky, Leonid I.
2007-04-01
The paper discusses evolution of consciousness driven by the knowledge instinct, a fundamental mechanism of the mind which determines its higher cognitive functions. Dynamic logic mathematically describes the knowledge instinct. It overcomes past mathematical difficulties encountered in modeling intelligence and relates it to mechanisms of concepts, emotions, instincts, consciousness and unconscious. The two main aspects of the knowledge instinct are differentiation and synthesis. Differentiation is driven by dynamic logic and proceeds from vague and unconscious states to more crisp and conscious states, from less knowledge to more knowledge at each hierarchical level of the mind. Synthesis is driven by dynamic logic operating in a hierarchical organization of the mind; it strives to achieve unity and meaning of knowledge: every concept finds its deeper and more general meaning at a higher level. These mechanisms are in complex relationship of symbiosis and opposition, which leads to complex dynamics of evolution of consciousness and cultures. Modeling this dynamics in a population leads to predictions for the evolution of consciousness, and cultures. Cultural predictive models can be compared to experimental data and used for improvement of human conditions. We discuss existing evidence and future research directions.
Yusuf Pandir
2012-01-01
Full Text Available We obtain the classification of exact solutions, including soliton, rational, and elliptic solutions, to the one-dimensional general improved Camassa Holm KP equation and KdV equation by the complete discrimination system for polynomial method. In discussion, we propose a more general trial equation method for nonlinear partial differential equations with generalized evolution.
Ren-Jie He; Zhen-Yu Yang
2012-01-01
Differential evolution (DE) has become a very popular and effective global optimization algorithm in the area of evolutionary computation.In spite of many advantages such as conceptual simplicity,high efficiency and ease of use,DE has two main components,i.e.,mutation scheme and parameter control,which significantly influence its performance.In this paper we intend to improve the performance of DE by using carefully considered strategies for both of the two components.We first design an adaptive mutation scheme,which adaptively makes use of the bias of superior individuals when generating new solutions.Although introducing such a bias is not a new idea,existing methods often use heuristic rules to control the bias.They can hardly maintain the appropriate balance between exploration and exploitation during the search process,because the preferred bias is often problem and evolution-stage dependent.Instead of using any fixed rule,a novel strategy is adopted in the new adaptive mutation scheme to adjust the bias dynamically based on the identified local fitness landscape captured by the current population.As for the other component,i.e.,parameter control,we propose a mechanism by using the Lévy probability distribution to adaptively control the scale factor F of DE.For every mutation in each generation,an Fi is produced from one of four different Lévy distributions according to their historical performance.With the adaptive mutation scheme and parameter control using Lévy distribution as the main components,we present a new DE variant called Lévy DE (LDE).Experimental studies were carried out on a broad range of benchmark functions in global numerical optimization.The results show that LDE is very competitive,and both of the two main components have contributed to its overall performance.The scalability of LDE is also discussed by conducting experiments on some selected benchmark functions with dimensions from 30 to 200.
Evolution of the environmental justice movement: activism, formalization and differentiation
Colsa Perez, Alejandro; Grafton, Bernadette; Mohai, Paul; Hardin, Rebecca; Hintzen, Katy; Orvis, Sara
2015-10-01
To complement a recent flush of research on transnational environmental justice movements, we sought a deeper organizational history of what we understand as the contemporary environmental justice movement in the United States. We thus conducted in-depth interviews with 31 prominent environmental justice activists, scholars, and community leaders across the US. Today’s environmental justice groups have transitioned from specific local efforts to broader national and global mandates, and more sophisticated political, technological, and activist strategies. One of the most significant transformations has been the number of groups adopting formal legal status, and emerging as registered environmental justice organizations (REJOs) within complex partnerships. This article focuses on the emergence of REJOs, and describes the respondents’ views about the implications of this for more local grassroots groups. It reveals a central irony animating work across groups in today’s movement: legal formalization of many environmental justice organizations has made the movement increasingly internally differentiated, dynamic, and networked, even as the passage of actual national laws on environmental justice has proven elusive.
Algorithms of estimation for nonlinear systems a differential and algebraic viewpoint
Martínez-Guerra, Rafael
2017-01-01
This book acquaints readers with recent developments in dynamical systems theory and its applications, with a strong focus on the control and estimation of nonlinear systems. Several algorithms are proposed and worked out for a set of model systems, in particular so-called input-affine or bilinear systems, which can serve to approximate a wide class of nonlinear control systems. These can either take the form of state space models or be represented by an input-output equation. The approach taken here further highlights the role of modern mathematical and conceptual tools, including differential algebraic theory, observer design for nonlinear systems and generalized canonical forms.
Patterson, Larissa B; Bain, Emily J; Parichy, David M
2014-11-06
Fishes have diverse pigment patterns, yet mechanisms of pattern evolution remain poorly understood. In zebrafish, Danio rerio, pigment-cell autonomous interactions generate dark stripes of melanophores that alternate with light interstripes of xanthophores and iridophores. Here, we identify mechanisms underlying the evolution of a uniform pattern in D. albolineatus in which all three pigment cell classes are intermingled. We show that in this species xanthophores differentiate precociously over a wider area, and that cis regulatory evolution has increased expression of xanthogenic Colony Stimulating Factor-1 (Csf1). Expressing Csf1 similarly in D. rerio has cascading effects, driving the intermingling of all three pigment cell classes and resulting in the loss of stripes, as in D. albolineatus. Our results identify novel mechanisms of pattern development and illustrate how pattern diversity can be generated when a core network of pigment-cell autonomous interactions is coupled with changes in pigment cell differentiation.
Vrettas, Michail D; Opper, Manfred; Cornford, Dan
2015-01-01
This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade.
Antonia Klein
2016-03-01
Full Text Available The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the 'theory of facilitated variation', we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx and a krüppel homolog 2 (kr-h2 with putative regulatory function, exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues, and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets, thus allowing them to control differential development into morphological castes.
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade.
Klein, Antonia; Schultner, Eva; Lowak, Helena; Schrader, Lukas; Heinze, Jürgen; Holman, Luke; Oettler, Jan
2016-03-01
The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the 'theory of facilitated variation', we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx) and a krüppel homolog 2 (kr-h2) with putative regulatory function, exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues), and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets, thus allowing them to control differential development into morphological castes.
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade
Klein, Antonia; Schultner, Eva; Lowak, Helena; Schrader, Lukas; Heinze, Jürgen; Holman, Luke; Oettler, Jan
2016-01-01
The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the ‘theory of facilitated variation’, we identify sex differentiation pathways as promising candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx) and a krüppel homolog 2 (kr-h2) with putative regulatory function, exhibit both sex and morph-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues), and that their inherent switch-like and epistatic behavior facilitated signal transfer to downstream targets, thus allowing them to control differential development into morphological castes. PMID:27031240
An algorithm for the study of DNA sequence evolution based on the genetic code.
Sirakoulis, G Ch; Karafyllidis, I; Sandaltzopoulos, R; Tsalides, Ph; Thanailakis, A
2004-11-01
Recent studies of the quantum-mechanical processes in the DNA molecule have seriously challenged the principle that mutations occur randomly. The proton tunneling mechanism causes tautomeric transitions in base pairs resulting in mutations during DNA replication. The meticulous study of the quantum-mechanical phenomena in DNA may reveal that the process of mutagenesis is not completely random. We are still far away from a complete quantum-mechanical model of DNA sequence mutagenesis because of the complexity of the processes and the complex three-dimensional structure of the molecule. In this paper we have developed a quantum-mechanical description of DNA evolution and, following its outline, we have constructed a classical model for DNA evolution assuming that some aspects of the quantum-mechanical processes have influenced the determination of the genetic code. Conversely, our model assumes that the genetic code provides information about the quantum-mechanical mechanisms of mutagenesis, as the current code is the product of an evolutionary process that tries to minimize the spurious consequences of mutagenesis. Based on this model we develop an algorithm that can be used to study the accumulation of mutations in a DNA sequence. The algorithm has a user-friendly interface and the user can change key parameters in order to study relevant hypotheses.
Kubota, N. [Osaka Inst. of Technology, Osaka (Japan); Fukuda, T. [Nagoya University, Nagoya (Japan)
1998-05-31
This paper deals with virus evolutionary genetic algorithm. The genetic algorithms (GAs) have been demonstrated its effectiveness in optimization problems in these days. In general, the GAs simulate the survival of fittest by natural selection and the heredity of the Darwin`s theory of evolution. However, some types of evolutionary hypotheses such as neutral theory of molecular evolution, Imanishi`s evolutionary theory, serial symbiosis theory, and virus theory of evolution, have been proposed in addition to the Darwinism. Virus theory of evolution is based on the view that the virus transduction is a key mechanism for transporting segments of DNA across species. This paper proposes genetic algorithm based on the virus theory of evolution (VE-GA), which has two types of populations: host population and virus population. The VE-GA is composed of genetic operators and virus operators such as reverse transcription and incorporation. The reverse transcription operator transcribes virus genes on the chromosome of host individual and the incorporation operator creates new genotype of virus from host individual. These operators by virus population make it possible to transmit segment of DNA between individuals in the host population. Therefore, the VE-GA realizes not only vertical but also horizontal propagation of genetic information. Further, the VE-GA is applied to the traveling salesman problem in order to show the effectiveness. 20 refs., 10 figs., 3 tabs.
Majeed, Muhammad Usman
2017-07-19
Steady-state elliptic partial differential equations (PDEs) are frequently used to model a diverse range of physical phenomena. The source and boundary data estimation problems for such PDE systems are of prime interest in various engineering disciplines including biomedical engineering, mechanics of materials and earth sciences. Almost all existing solution strategies for such problems can be broadly classified as optimization-based techniques, which are computationally heavy especially when the problems are formulated on higher dimensional space domains. However, in this dissertation, feedback based state estimation algorithms, known as state observers, are developed to solve such steady-state problems using one of the space variables as time-like. In this regard, first, an iterative observer algorithm is developed that sweeps over regular-shaped domains and solves boundary estimation problems for steady-state Laplace equation. It is well-known that source and boundary estimation problems for the elliptic PDEs are highly sensitive to noise in the data. For this, an optimal iterative observer algorithm, which is a robust counterpart of the iterative observer, is presented to tackle the ill-posedness due to noise. The iterative observer algorithm and the optimal iterative algorithm are then used to solve source localization and estimation problems for Poisson equation for noise-free and noisy data cases respectively. Next, a divide and conquer approach is developed for three-dimensional domains with two congruent parallel surfaces to solve the boundary and the source data estimation problems for the steady-state Laplace and Poisson kind of systems respectively. Theoretical results are shown using a functional analysis framework, and consistent numerical simulation results are presented for several test cases using finite difference discretization schemes.
Kiupel, M; Smedley, R C; Pfent, C; Xie, Y; Xue, Y; Wise, A G; DeVaul, J M; Maes, R K
2011-01-01
Differentiating between inflammatory bowel disease (IBD) and small intestinal lymphoma in cats is often difficult, especially when only endoscopic biopsy specimens are available for evaluation. However, a correct diagnosis is imperative for proper treatment and prognosis. A retrospective study was performed using surgical and endoscopic intestinal biopsy specimens from 63 cats with a history of chronic diarrhea or vomiting or weight loss. A diagnosis of lymphoma or inflammation was based on microscopic examination of hematoxylin and eosin (HE)-stained sections alone, HE-stained sections plus results of immunohistochemical labeling (IHC) for CD3e and CD79a, and HE staining, immunophenotyping, and polymerase chain reaction (PCR) results for B and/or T cell clonality. In addition, various histomorphologic parameters were evaluated for significant differences between lymphoma and IBD using Fisher's exact test. The sensitivity and specificity of each parameter in the diagnosis of lymphoma were also determined. Results of Bayesian statistical analysis demonstrated that combining histologic evaluation of small intestinal biopsy specimens with immunophenotyping and analysis of clonality of lymphoid infiltrates results in more accurate differentiation of neoplastic versus inflammatory lymphocytes. Important histologic features that differentiated intestinal lymphoma from IBD included lymphoid infiltration of the intestinal wall beyond the mucosa, epitheliotropism (especially intraepithelial nests and plaques), heterogeneity, and nuclear size of lymphocytes. Based on the results of this study, a stepwise diagnostic algorithm that first uses histologic assessment, followed by immunophenotyping and then PCR to determine clonality of the lymphocytes, was developed to more accurately differentiate between intestinal lymphoma and IBD.
Jia Hui Ong
2016-07-01
Full Text Available Parameter searching is one of the most important aspects in getting favorable results in optimization problems. It is even more important if the optimization problems are limited by time constraints. In a limited time constraint problems, it is crucial for any algorithms to get the best results or near-optimum results. In a previous study, Differential Evolution (DE has been found as one of the best performing algorithms under time constraints. As this has help in answering which algorithm that yields results that are near-optimum under a limited time constraint. Hence to further enhance the performance of DE under time constraint evaluation, a throughout parameter searching for population size, mutation constant and f constant have been carried out. CEC 2015 Global Optimization Competition’s 15 scalable test problems are used as test suite for this study. In the previous study the same test suits has been used and the results from DE will be use as the benchmark for this study since it shows the best results among the previous tested algorithms. Eight different populations size are used and they are 10, 30, 50, 100, 150, 200, 300, and 500. Each of these populations size will run with mutation constant of 0.1 until 0.9 and from 0.1 until 0.9. It was found that population size 100, Cr = 0.9, F=0.5 outperform the benchmark results. It is also observed from the results that good higher Cr around 0.8 and 0.9 with low F around 0.3 to 0.4 yields good results for DE under time constraints evaluation
Goldberg, Daniel N.; Krishna Narayanan, Sri Hari; Hascoet, Laurent; Utke, Jean
2016-05-01
We apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory requirements and in some cases shorter computation times of the adjoint. The optimization is done via implementation of the algorithm of Christianson (1994) for reverse accumulation of fixed-point problems, with the AD tool OpenAD. For test problems, the optimized adjoint is shown to have far lower memory requirements, potentially enabling larger problem sizes on memory-limited machines. In the case of the land ice model, implementation of the algorithm allows further optimization by having the adjoint model solve a sequence of linear systems with identical (as opposed to varying) matrices, greatly improving performance. The methods introduced here will be of value to other efforts applying AD tools to ice models, particularly ones which solve a hybrid shallow ice/shallow shelf approximation to the Stokes equations.
Chandramouli Anandaraman
2012-01-01
Full Text Available A new evolutionary computation algorithm, Superbug algorithm, which simulates evolution of bacteria in a culture, is proposed. The algorithm is developed for solving large scale optimization problems such as scheduling, transportation and assignment problems. In this work, the algorithm optimizes machine schedules in a Flexible Manufacturing System (FMS by minimizing makespan. The FMS comprises of four machines and two identical Automated Guided Vehicles (AGVs. AGVs are used for carrying jobs between the Load/Unload (L/U station and the machines. Experimental results indicate the efficiency of the proposed algorithm in its optimization performance in scheduling is noticeably superior to other evolutionary algorithms when compared to the best results reported in the literature for FMS Scheduling.
Evolution of Social Insect Polyphenism Facilitated by the Sex Differentiation Cascade
Klein, Antonia; Schultner, Eva; Lowak, Helena;
2016-01-01
-specific expression across life stages in the ant Cardiocondyla obscurior. We hypothesize that genes in the sex differentiation cascade evolved perception of alternative input signals for caste differentiation (i.e. environmental or genetic cues), and that their inherent switch-like and epistatic behavior facilitated......The major transition to eusociality required the evolution of a switch to canalize development into either a reproductive or a helper, the nature of which is currently unknown. Following predictions from the 'theory of facilitated variation', we identify sex differentiation pathways as promising...... candidates because of their pre-adaptation to regulating development of complex phenotypes. We show that conserved core genes, including the juvenile hormone-sensitive master sex differentiation gene doublesex (dsx) and a krüppel homolog 2 (kr-h2) with putative regulatory function, exhibit both sex and morph...
Onset of Differentiation and Internal Evolution: the case of 21 Lutetia
Formisano, M; Federico, C; Capaccioni, F; De Sanctis, M C
2013-01-01
Asteroid 21 Lutetia, visited by the Rosetta spacecraft, plays a crucial role in the reconstruction of primordial phases of planetary objects. Its high bulk density and its primitive chondritic crust (Weiss et al. 2011) suggest that Lutetia could be partially differentiated. We developed a numerical code, also used for studying the geophysical history of Vesta (Formisano et al., submitted), to explore several scenarios of internal evolution of Lutetia, differing in the strength of radiogenic sources and in the global post-sintering porosity. The only significant heat source for partial differentiation is represented by Al26, the other possible sources (Fe60, accretion and differentiation) being negligible. In scenarios in which Lutetia completed its accretion in less than 0.7 Ma from injection of Al26 in Solar Nebula and for post-sintering values of macroporosity not exceeding 30 vol. %, the asteroid experienced only partial differentiation. The formation of the proto-core, a structure enriched in metals and a...
Model based on a quantum algorithm to study the evolution of an epidemics.
León, A; Pozo, J
2007-03-01
A model based on a quantum algorithm is used to study the spread of HIV virus and to predict infection rates on individuals who are not aware of their particular condition. The model makes an analogy between quantum systems and individuals who are infected by the disease. Individuals are represented by two-level quantum systems (quantum "bit"), and the interactions among individuals who cause the infection are represented by unitary transformations. The population is divided into categories according to their behaviour, and the interactions among those individuals in the same category and those in different categories are simulated. The objective is to obtain statistical data on the number of infected individuals depending on the time for every category and for the entire population. Besides, we analyse the impact of the evolution of the disease on individuals who have not knowledge of their specific sanitary condition.
A Numerical Study of the Performance of a Quantum Adiabatic Evolution Algorithm for Satisfiability
Farhi, E; Gutmann, S; Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam
2000-01-01
Quantum computation by adiabatic evolution, as described in quant-ph/0001106, will solve satisfiability problems if the running time is long enough. In certain special cases (that are classically easy) we know that the quantum algorithm requires a running time that grows as a polynomial in the number of bits. In this paper we present numerical results on randomly generated instances of an NP-complete problem and of a problem that can be solved classically in polynomial time. We simulate a quantum computer (of up to 16 qubits) by integrating the Schrodinger equation on a conventional computer. For both problems considered, for the set of instances studied, the required running time appears to grow slowly as a function of the number of bits.
Fernando Martín
2015-09-01
Full Text Available One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot’s pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area.
Martín, Fernando; Moreno, Luis; Garrido, Santiago; Blanco, Dolores
2015-09-16
One of the most important skills desired for a mobile robot is the ability to obtain its own location even in challenging environments. The information provided by the sensing system is used here to solve the global localization problem. In our previous work, we designed different algorithms founded on evolutionary strategies in order to solve the aforementioned task. The latest developments are presented in this paper. The engine of the localization module is a combination of the Markov chain Monte Carlo sampling technique and the Differential Evolution method, which results in a particle filter based on the minimization of a fitness function. The robot's pose is estimated from a set of possible locations weighted by a cost value. The measurements of the perceptive sensors are used together with the predicted ones in a known map to define a cost function to optimize. Although most localization methods rely on quadratic fitness functions, the sensed information is processed asymmetrically in this filter. The Kullback-Leibler divergence is the basis of a cost function that makes it possible to deal with different types of occlusions. The algorithm performance has been checked in a real map. The results are excellent in environments with dynamic and unmodeled obstacles, a fact that causes occlusions in the sensing area.
Xiangtao Li
2011-01-01
Full Text Available Multibeam antenna arrays have important applications in communications and radar. This paper presents a new method of designing a reconfigurable antenna with quantized phase excitations using a new hybrid algorithm, called DE/BBO. The reconfigurable design problem is to find the element excitation that will result in a sector pattern main beam with low sidelobes with additional requirement that the same excitation amplitudes applied to the array with zero-phase should be in a high directivity, low sidelobe pencil-shaped main beam. In order to reduce the effect of mutual coupling between the antenna-array elements, the dynamic range ratio is minimized. Additionally, compared with the continuous realization and subsequent quantization, experimental results indicate that the performance of the discrete realization of the phase excitation value can be improved. In order to test the performances of hybrid differential evolution with biogeography-based optimization, the results of some state-of-art algorithms are considered, for the purposed of comparison. Experiment results indicate the better performance of the DE/BBO.
Multiple Active Contours Guided by Differential Evolution for Medical Image Segmentation
I. Cruz-Aceves
2013-01-01
Full Text Available This paper presents a new image segmentation method based on multiple active contours guided by differential evolution, called MACDE. The segmentation method uses differential evolution over a polar coordinate system to increase the exploration and exploitation capabilities regarding the classical active contour model. To evaluate the performance of the proposed method, a set of synthetic images with complex objects, Gaussian noise, and deep concavities is introduced. Subsequently, MACDE is applied on datasets of sequential computed tomography and magnetic resonance images which contain the human heart and the human left ventricle, respectively. Finally, to obtain a quantitative and qualitative evaluation of the medical image segmentations compared to regions outlined by experts, a set of distance and similarity metrics has been adopted. According to the experimental results, MACDE outperforms the classical active contour model and the interactive Tseng method in terms of efficiency and robustness for obtaining the optimal control points and attains a high accuracy segmentation.
Ligang Cui
2013-01-01
Full Text Available The capacitated vehicle routing problem (CVRP is the most classical vehicle routing problem (VRP; many solution techniques are proposed to find its better answer. In this paper, a new improved quantum evolution algorithm (IQEA with a mixed local search procedure is proposed for solving CVRPs. First, an IQEA with a double chain quantum chromosome, new quantum rotation schemes, and self-adaptive quantum Not gate is constructed to initialize and generate feasible solutions. Then, to further strengthen IQEA's searching ability, three local search procedures 1-1 exchange, 1-0 exchange, and 2-OPT, are adopted. Experiments on a small case have been conducted to analyze the sensitivity of main parameters and compare the performances of the IQEA with different local search strategies. Together with results from the testing of CVRP benchmarks, the superiorities of the proposed algorithm over the PSO, SR-1, and SR-2 have been demonstrated. At last, a profound analysis of the experimental results is presented and some suggestions on future researches are given.
A Closer Look At Differential Evolution For The Optimal Well Placement Problem
Carosio, Grazieli L. C.; Humphries, Thomas D.; Haynes, Ronald D.; Farquharson, Colin G.
2015-01-01
Energy demand has increased considerably with the growth of world population, increasing the interest in the hydrocarbon reservoir management problem. Companies are concerned with maximizing oil recovery while minimizing capital investment and operational costs. A first step in solving this problem is to consider optimal well placement. In this work, we investigate the Differential Evolution (DE) optimization method, using distinct configurations with respect to population size, mutation fact...
FSM State-Encoding for Area and Power Minimization Using Simulated Evolution Algorithm
Sadiq M. Sait
2012-11-01
Full Text Available In this paper we describe the engineering of a non-deterministic iterative heuristic [1] known as simulated evolution(SimE to solve the well-known NP-hard state assignment problem (SAP. Each assignment of a code to a state isgiven a Goodness value derived from a matrix representation of the desired adjacency graph (DAG proposed byAmaral et.al [2]. We use the (DAGa proposed in previous studies to optimize the area, and propose a new DAGpand employ it to reduce the power dissipation. In the process of evolution, those states that have high Goodness havea smaller probability of getting perturbed, while those with lower Goodness can be easily reallocated. States areassigned to cells of a Karnaugh-map, in a way that those states that have to be close in terms of Hamming distanceare assigned adjacent cells. Ordered weighed average (OWA operator proposed by Yager [3] is used to combine thetwo objectives. Results are compared with those published in previous studies, for circuits obtained from the MCNCbenchmark suite. It was found that the SimE heuristic produces better quality results in most cases, and/or in lessertime, when compared to both deterministic heuristics and non-deterministic iterative heuristics such as GeneticAlgorithm.
Ohtani, Misato; Akiyoshi, Nobuhiro; Takenaka, Yuto; Sano, Ryosuke; Demura, Taku
2017-01-01
One crucial problem that plants faced during their evolution, particularly during the transition to growth on land, was how to transport water, nutrients, metabolites, and small signaling molecules within a large, multicellular body. As a solution to this problem, land plants developed specific tissues for conducting molecules, called water-conducting cells (WCCs) and food-conducting cells (FCCs). The well-developed WCCs and FCCs in extant plants are the tracheary elements and sieve elements, respectively, which are found in vascular plants. Recent molecular genetic studies revealed that transcriptional networks regulate the differentiation of tracheary and sieve elements, and that the networks governing WCC differentiation are largely conserved among land plant species. In this review, we discuss the molecular evolution of plant conducting cells. By focusing on the evolution of the key transcription factors that regulate vascular cell differentiation, the NAC transcription factor VASCULAR-RELATED NAC-DOMAIN for WCCs and the MYB-coiled-coil (CC)-type transcription factor ALTERED PHLOEM DEVELOPMENT for sieve elements, we describe how land plants evolved molecular systems to produce the specialized cells that function as WCCs and FCCs. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.
Algorithms to solve coupled systems of differential equations in terms of power series
Ablinger, Jakob; Bluemlein, Johannes; de Freitas, Abilio; Schneider, Carsten
2016-01-01
Using integration by parts relations, Feynman integrals can be represented in terms of coupled systems of differential equations. In the following we suppose that the unknown Feynman integrals can be given in power series representations, and that sufficiently many initial values of the integrals are given. Then there exist algorithms that decide constructively if the coefficients of their power series representations can be given within the class of nested sums over hypergeometric products. In this article we will work out the calculation steps that solve this problem. First, we will present a successful tactic that has been applied recently to challenging problems coming from massive 3-loop Feynman integrals. Here our main tool is to solve scalar linear recurrences within the class of nested sums over hypergeometric products. Second, we will present a new variation of this tactic which relies on more involved summation technologies but succeeds in reducing the problem to solve scalar recurrences with lower ...
ZHAO La-la; WANG Zhong-bin; ZANG Feng
2008-01-01
Our differential and grading toothed roll crusher blends the advantages of a toothed roll crusher and a jaw crusher and possesses characteristics of great crushing, high breaking efficiency, multi-sieving and has, for the moment, made up for the shortcomings of the toothed roll crusher. The moving jaw of the crusher is a crank-rocker mechanism. For optimizing the dynamic performance and improving the cracking capability of the crusher, a mathematical model was established to optimize the transmission angle γ and to minimize the travel characteristic value m of the moving jaw. Genetic algorithm is used to optimize the crusher crank-rocker mechanism for multi-object design and an optimum result is obtained. According to the implementation, it is shown that the performance of the crusher and the cracking capability of the moving jaw have been improved.
S. K. Lahiri
2009-05-01
Full Text Available This paper describes a robust hybrid artificial neural network (ANN methodology which can offer a superior performance for the important process engineering problems. The method incorporates a hybrid artificial neural network and differential evolution technique (ANN-DE for the efficient tuning of ANN meta parameters. The algorithm has been applied for the prediction of the hold up of the solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed ANN correlation noticeably improved the prediction of hold up over a wide range of operating conditions, physical properties, and pipe diameters.
Bai Li
2014-01-01
Full Text Available Unmanned combat aerial vehicles (UCAVs have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC algorithm improved by a balance-evolution strategy (BES is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.
Li, Bai; Gong, Li-gang; Yang, Wen-lun
2014-01-01
Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.
A hybrid algorithm for coupling partial differential equation and compartment-based dynamics.
Harrison, Jonathan U; Yates, Christian A
2016-09-01
Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time.
Debkalpa Goswami
2014-01-01
Full Text Available Electrochemical micromachining (EMM appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nano-electromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer’s desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.
Yuqing He
2014-01-01
Full Text Available Autonomous maneuvering flight control of rotor-flying robots (RFR is a challenging problem due to the highly complicated structure of its model and significant uncertainties regarding many aspects of the field. As a consequence, it is difficult in many cases to decide whether or not a flight maneuver trajectory is feasible. It is necessary to conduct an analysis of the flight maneuvering ability of an RFR prior to test flight. Our aim in this paper is to use a numerical method called algorithm differentiation (AD to solve this problem. The basic idea is to compute the internal state (i.e., attitude angles and angular rates and input profiles based on predetermined maneuvering trajectory information denoted by the outputs (i.e., positions and yaw angle and their higher-order derivatives. For this purpose, we first present a model of the RFR system and show that it is flat. We then cast the procedure for obtaining the required state/input based on the desired outputs as a static optimization problem, which is solved using AD and a derivative based optimization algorithm. Finally, we test our proposed method using a flight maneuver trajectory to verify its performance.
Huang, Yanping; Zhang, Qinqin; Thorell, Mariana Rossi; An, Lin; Durbin, Mary; Laron, Michal; Sharma, Utkarsh; Gregori, Giovanni; Rosenfeld, Philip J.; Wang, Ruikang K
2014-01-01
Background and Objective To demonstrate the feasibility of using a 1050 nm swept-source OCT (SS-OCT) system to achieve noninvasive retinal vasculature imaging in human eyes. Materials and Methods Volumetric datasets were acquired using a ZEISS 1 µm SS-OCT prototype that operated at an A-line rate of 100 kHz. A scanning protocol designed to allow for motion contrast processing, referred to as OCT angiography or optical microangiography (OMAG), was used to scan ~3 mm × 3 mm area in the central macular region of the retina within ~4.5 seconds. Intensity differentiation based OMAG algorithm was used to extract 3-D retinal functional microvasculature information. Results Intensity signal differentiation generated capillary-level resolution en face OMAG images of the retina. The parafoveal capillaries were clearly visible, thereby allowing visualization of the foveal avascular zone (FAZ) in normal subjects. Conclusion The capability of OMAG to produce retinal vascular images was demonstrated using the ZEISS 1 µm SS-OCT prototype. This technique can potentially have clinical value for studying retinal vasculature abnormalities. PMID:25230403
Yu Zhang
2015-10-01
Full Text Available In this article, we begin with the non-homogeneous model for the non-differentiable heat flow, which is described using the local fractional vector calculus, from the first law of thermodynamics in fractal media point view. We employ the local fractional variational iteration algorithm II to solve the fractal heat equations. The obtained results show the non-differentiable behaviors of temperature fields of fractal heat flow defined on Cantor sets.
Baldwin, C; Eliassi-Rad, T; Abdulla, G; Critchlow, T
2003-04-16
As scientific data sets grow exponentially in size, the need for scalable algorithms that heuristically partition the data increases. In this paper, we describe the three-step evolution of a hierarchical partitioning algorithm for large-scale spatio-temporal scientific data sets generated by massive simulations. The first version of our algorithm uses a simple top-down partitioning technique, which divides the data by using a four-way bisection of the spatio-temporal space. The shortcomings of this algorithm lead to the second version of our partitioning algorithm, which uses a bottom-up approach. In this version, a partition hierarchy is constructed by systematically agglomerating the underlying Cartesian grid that is placed on the data. Finally, the third version of our algorithm utilizes the intrinsic topology of the data given in the original scientific problem to build the partition hierarchy in a bottom-up fashion. Specifically, the topology is used to heuristically agglomerate the data at each level of the partition hierarchy. Despite the growing complexity in our algorithms, the third version of our algorithm builds partition hierarchies in less time and is able to build trees for larger size data sets as compared to the previous two versions.
Li Honglin
2009-03-01
Full Text Available Abstract Background Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. Results The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105–112. Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 Å to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 ± 0.18 seconds per molecule renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. Conclusion On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms
Nelson, Benjamin E; Payne, Matthew J
2013-01-01
In the 20+ years of Doppler observations of stars, scientists have uncovered a diverse population of extrasolar multi-planet systems. A common technique for characterizing the orbital elements of these planets is Markov chain Monte Carlo (MCMC), using a Keplerian model with random walk proposals and paired with the Metropolis-Hastings algorithm. For approximately a couple of dozen planetary systems with Doppler observations, there are strong planet-planet interactions due to the system being in or near a mean-motion resonance (MMR). An N-body model is often required to accurately describe these systems. Further computational difficulties arise from exploring a high-dimensional parameter space ($\\sim$7 x number of planets) that can have complex parameter correlations. To surmount these challenges, we introduce a differential evolution MCMC (DEMCMC) applied to radial velocity data while incorporating self-consistent N-body integrations. Our Radial velocity Using N-body DEMCMC (RUN DMC) algorithm improves upon t...
Operational Solution of Non-Integer Ordinary and Evolution-Type Partial Differential Equations
Konstantin V. Zhukovsky
2016-12-01
Full Text Available A method for the solution of linear differential equations (DE of non-integer order and of partial differential equations (PDE by means of inverse differential operators is proposed. The solutions of non-integer order ordinary differential equations are obtained with recourse to the integral transforms and the exponent operators. The generalized forms of Laguerre and Hermite orthogonal polynomials as members of more general Appèl polynomial family are used to find the solutions. Operational definitions of these polynomials are used in the context of the operational approach. Special functions are employed to write solutions of DE in convolution form. Some linear partial differential equations (PDE are also explored by the operational method. The Schrödinger and the Black–Scholes-like evolution equations and solved with the help of the operational technique. Examples of the solution of DE of non-integer order and of PDE are considered with various initial functions, such as polynomial, exponential, and their combinations.
Huang, Ding-jiang; Ivanova, Nataliya M.
2016-02-01
In this paper, we explain in more details the modern treatment of the problem of group classification of (systems of) partial differential equations (PDEs) from the algorithmic point of view. More precisely, we revise the classical Lie algorithm of construction of symmetries of differential equations, describe the group classification algorithm and discuss the process of reduction of (systems of) PDEs to (systems of) equations with smaller number of independent variables in order to construct invariant solutions. The group classification algorithm and reduction process are illustrated by the example of the generalized Zakharov-Kuznetsov (GZK) equations of form ut +(F (u)) xxx +(G (u)) xyy +(H (u)) x = 0. As a result, a complete group classification of the GZK equations is performed and a number of new interesting nonlinear invariant models which have non-trivial invariance algebras are obtained. Lie symmetry reductions and exact solutions for two important invariant models, i.e., the classical and modified Zakharov-Kuznetsov equations, are constructed. The algorithmic framework for group analysis of differential equations presented in this paper can also be applied to other nonlinear PDEs.
Yanfei Zhong
2017-08-01
Full Text Available Hyperspectral images and light detection and ranging (LiDAR data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI, gray-level co-occurrence matrix (GLCM and digital surface model (DSM are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC, support vector machine (SVM and multinomial logistic regression (MLR are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF. The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.
Integrable nonlinear evolution partial differential equations in 4 + 2 and 3 + 1 dimensions.
Fokas, A S
2006-05-19
The derivation and solution of integrable nonlinear evolution partial differential equations in three spatial dimensions has been the holy grail in the field of integrability since the late 1970s. The celebrated Korteweg-de Vries and nonlinear Schrödinger equations, as well as the Kadomtsev-Petviashvili (KP) and Davey-Stewartson (DS) equations, are prototypical examples of integrable evolution equations in one and two spatial dimensions, respectively. Do there exist integrable analogs of these equations in three spatial dimensions? In what follows, I present a positive answer to this question. In particular, I first present integrable generalizations of the KP and DS equations, which are formulated in four spatial dimensions and which have the novelty that they involve complex time. I then impose the requirement of real time, which implies a reduction to three spatial dimensions. I also present a method of solution.
Beyer, Horst Reinhard
2007-01-01
The present volume is self-contained and introduces to the treatment of linear and nonlinear (quasi-linear) abstract evolution equations by methods from the theory of strongly continuous semigroups. The theoretical part is accessible to graduate students with basic knowledge in functional analysis. Only some examples require more specialized knowledge from the spectral theory of linear, self-adjoint operators in Hilbert spaces. Particular stress is on equations of the hyperbolic type since considerably less often treated in the literature. Also, evolution equations from fundamental physics need to be compatible with the theory of special relativity and therefore are of hyperbolic type. Throughout, detailed applications are given to hyperbolic partial differential equations occurring in problems of current theoretical physics, in particular to Hermitian hyperbolic systems. This volume is thus also of interest to readers from theoretical physics.
Chounghyun Seong
2015-02-01
Full Text Available Hydrologic Simulation Program-Fortran (HSPF model calibration is typically done manually due to the lack of an automated calibration tool as well as the difficulty of balancing objective functions to be considered. This paper discusses the development and demonstration of an automated calibration tool for HSPF (HSPF-SCE. HSPF-SCE was developed using the open source software “R”. The tool employs the Shuffled Complex Evolution optimization algorithm (SCE-UA to produce a pool of qualified calibration parameter sets from which the modeler chooses a single set of calibrated parameters. Six calibration criteria specified in the Expert System for the Calibration of HSPF (HSPEXP decision support tool were combined to develop a single, composite objective function for HSPF-SCE. The HSPF-SCE tool was demonstrated, and automated and manually calibrated model performance were compared using three Virginia watersheds, where HSPF models had been previously prepared for bacteria total daily maximum load (TMDL development. The example applications demonstrate that HSPF-SCE can be an effective tool for calibrating HSPF.
Barley, Mark H; Turner, Nicolas J; Goodacre, Royston
2017-06-19
In directed evolution (DE) the assessment of candidate enzymes and their modification is essential. In this study we have investigated genetic algorithms (GAs) in this context and conducted a systematic study of the behavior of GAs on 20 fitness landscapes (FLs) of varying complexity. This has allowed the tuning of the GAs to be explored. On the basis of this study, recommendations for the best GA settings to use for a GA-directed high-throughput experimental program (in which populations and the number of generations is necessarily low) are reported. The FLs were based upon simple linear models and were characterized by the behavior of the GA on the landscape as demonstrated by stall plots and the footprints and adhesion of candidate solutions, which highlighted local optima (LOs). In order to maximize progress of the GA and to reduce the chances of becoming stuck in a LO it was best to use: 1) a large number of generations, 2) high populations, 3) removal of duplicate sequences (clones), 4) double mutation, and 5) high selection pressure (the two best individuals go to the next generation), and 6) to consider using a designed sequence as the starting point of the GA run. We believe that these recommendations might be appropriate starting points for studies employing GAs within DE experiments. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Zhan-ping Song
2015-01-01
Full Text Available Since the geological bodies where tunnels are located have uncertain and complex characteristics, the inverse problem plays an important role in geotechnical engineering. In order to improve the accuracy and speed of surrounding rock identification, the back analysis objective function with usage of the displacement and stress monitoring data has been constructed, with a hybrid algorithm proposed. An extreme learning machine (ELM is employed with optimal architecture trained by the difference evolution (DE arithmetic. First, the three-dimensional numerical simulation is used in the creation of training and testing samples for ELM model construction. Second, the nonlinear relationship between rock parameters and displacement is constructed by numerical simulation. Finally, the geophysics parameters are obtained by DE optimization arithmetic taking into consideration the monitoring data including both displacement and pressure. This method had been applied in the Fusong highway tunnel in Fusong City of China’s Jilin Province, with a good effect obtained. It takes full advantage of DE and ELM and has both calculation speed and precision in the back analysis.
Vanzeler, Francisco Joclean Alves
1999-06-01
In this work, we extract the elastic stiffness and mass density from an multi azimuthal qP-wave reflection coefficients at an interface separating two anisotropic media with monoclinic symmetry with at least one of its planes of symmetry parallel to the interface. This objective was reach by forward and inverse modeling. We calculate the q-P-wave reflection for three models (I, II, III) of anisotropic equivalent medium: isotropic medium above a TIH medium; TIV medium above a TIH medium; and orthorhombic medium above a TIH medium. The TIH medium is equivalent an isotropic fractured medium with equivalent elastic stiffness and mass density calculated by the Hudson formulation. The reflection coefficients used was on its exact form and was generated for models I, II and III in multi-azimuthal/incidence angles and contaminated by gaussian noise. In the inverse modeling we work with GA and with DE algorithms to calculate the inversion parameter (5 elastic stiffness and mass density for bottom media and Vs of upper isotropic media) by minimization of 12 norm of difference between the true and synthetic reflection coefficient. We assume that we know the parameter of the upper media of the three models, except Vs for model one in especial case of inversion of upper media.The parameter to be determined by inverse modeling are parametrized in model space for values that is in according with the value of the observed velocity of propagation of elastic waves in the earth crust, and the resolution of measure, and constraints of elastic stability of the solid media. The GA and DE algorithms reached good inversion to the models with at least three azimuthal angles, (0 deg C, 45 deg C and 90 deg C) and incidence angles of 34 deg C for model I, and 50 deg C inverted only by GA for models II and III; and the especial case take by DE that need at least 44 deg C to invert the model I with the Vs of the upper media. From this results we can see the potential to determine from q
Crouch, P. E.; Grossman, Robert
1992-01-01
This note is concerned with the explicit symbolic computation of expressions involving differential operators and their actions on functions. The derivation of specialized numerical algorithms, the explicit symbolic computation of integrals of motion, and the explicit computation of normal forms for nonlinear systems all require such computations. More precisely, if R = k(x(sub 1),...,x(sub N)), where k = R or C, F denotes a differential operator with coefficients from R, and g member of R, we describe data structures and algorithms for efficiently computing g. The basic idea is to impose a multiplicative structure on the vector space with basis the set of finite rooted trees and whose nodes are labeled with the coefficients of the differential operators. Cancellations of two trees with r + 1 nodes translates into cancellation of O(N(exp r)) expressions involving the coefficient functions and their derivatives.
Differential Evolution-Based PID Control of Nonlinear Full-Car Electrohydraulic Suspensions
Jimoh O. Pedro
2013-01-01
Full Text Available This paper presents a differential-evolution- (DE- optimized, independent multiloop proportional-integral-derivative (PID controller design for full-car nonlinear, electrohydraulic suspension systems. The multiloop PID control stabilises the actuator via force feedback and also improves the system performance. Controller gains are computed using manual tuning and through DE optimization to minimise a performance index, which addresses suspension travel, road holding, vehicle handling, ride comfort, and power consumption constraints. Simulation results showed superior performance of the DE-optimized PID-controlled active vehicle suspension system (AVSS over the manually tuned PID-controlled AVSS and the passive vehicle suspension system (PVSS.
Xinhua Liu
2017-07-01
Full Text Available In order to improve the response performance of a proportion integration differentiation (PID controller for magnetorheological fluids (MRF brake and to reduce the braking fluctuation rate, an improved fruit fly optimization algorithm for PID controller parameters tuning of MRF brake is proposed. A data acquisition system for MRF brake is designed and the transfer function of MRF brake is identified. Moreover, an improved fruit fly optimization algorithm (IFOA through integration of PID control strategy and cloud model algorithm is proposed to design a PID controller for MRF brake. Finally, the simulation and experiment are carried out. The results show that IFOA, with a faster response output and no overshoot, is superior to the conventional PID and fruit fly optimization algorithm (FOA PID controller.
Santra, Tapesh; Delatola, Eleni Ioanna
2016-07-01
Presence of considerable noise and missing data points make analysis of mass-spectrometry (MS) based proteomic data a challenging task. The missing values in MS data are caused by the inability of MS machines to reliably detect proteins whose abundances fall below the detection limit. We developed a Bayesian algorithm that exploits this knowledge and uses missing data points as a complementary source of information to the observed protein intensities in order to find differentially expressed proteins by analysing MS based proteomic data. We compared its accuracy with many other methods using several simulated datasets. It consistently outperformed other methods. We then used it to analyse proteomic screens of a breast cancer (BC) patient cohort. It revealed large differences between the proteomic landscapes of triple negative and Luminal A, which are the most and least aggressive types of BC. Unexpectedly, majority of these differences could be attributed to the direct transcriptional activity of only seven transcription factors some of which are known to be inactive in triple negative BC. We also identified two new proteins which significantly correlated with the survival of BC patients, and therefore may have potential diagnostic/prognostic values.
Algorithms to solve coupled systems of differential equations in terms of power series
Ablinger, Jakob; Schneider, Carsten [Johannes Kepler Univ., Linz (Austria). Research Inst. for Symbolic Computation; Behring, Arnd; Bluemlein, Johannes; Freitas, Abilio de [Deutsches Elektronen-Synchrotron (DESY), Zeuthen (Germany)
2016-08-15
Using integration by parts relations, Feynman integrals can be represented in terms of coupled systems of differential equations. In the following we suppose that the unknown Feynman integrals can be given in power series representations, and that sufficiently many initial values of the integrals are given. Then there exist algorithms that decide constructively if the coefficients of their power series representations can be given within the class of nested sums over hypergeometric products. In this article we work out the calculation steps that solve this problem. First, we present a successful tactic that has been applied recently to challenging problems coming from massive 3-loop Feynman integrals. Here our main tool is to solve scalar linear recurrences within the class of nested sums over hypergeometric products. Second, we will present a new variation of this tactic which relies on more involved summation technologies but succeeds in reducing the problem to solve scalar recurrences with lower recurrence orders. The article works out the different challenges of this new tactic and demonstrates how they can be treated efficiently with our existing summation technologies.
THE ONSET OF DIFFERENTIATION AND INTERNAL EVOLUTION: THE CASE OF 21 LUTETIA
Formisano, M.; Turrini, D.; Federico, C.; Capaccioni, F.; De Sanctis, M. C., E-mail: michelangelo.formisano@iaps.inaf.it [INAF-IAPS, Via del Fosso del Cavaliere 100, I-00133 Roma (Italy)
2013-06-10
Asteroid 21 Lutetia, seen by the Rosetta spacecraft, plays a crucial role in the reconstruction of primordial phases of planetary objects. Its high bulk density and its primitive chondritic crust suggest that Lutetia could be partially differentiated. We developed a numerical code, also used for studying the geophysical history of Vesta, to explore several scenarios of internal evolution of Lutetia. These scenarios differ in the strength of their radiogenic sources and in their global post-sintering porosity. The only significant heat source for partial differentiation is {sup 26}Al; the other possible sources ({sup 60}Fe, accretion, and differentiation) are negligible. In scenarios in which Lutetia completed its accretion in less than 0.7 Myr from the injection of {sup 26}Al in the solar nebula and for post-sintering values of macroporosity not exceeding 30% by volume, the asteroid experienced only partial differentiation. The formation of the proto-core, a structure enriched in metals and also containing pristine silicates, requires 1-4 Myr and the size of the proto-core varies from 6-30 km.
Duo ePeng
2014-11-01
Full Text Available Sucrose transporters (SUTs are essential for the export and efficient movement of sucrose from source leaves to sink organs in plants. The angiosperm SUT family was previously classified into three or four distinct groups, Types I, II (subgroup IIB and III, with dicot-specific Type I and monocot-specific Type IIB functioning in phloem loading. To shed light on the underlying drivers of SUT evolution, Bayesian phylogenetic inference was undertaken using 41 sequenced plant genomes, including seven basal lineages at key evolutionary junctures. Our analysis supports four phylogenetically and structurally distinct SUT subfamilies, originating from two ancient groups (AG1 and AG2 that diverged early during terrestrial colonization. In both AG1 and AG2, multiple intron acquisition events in the progenitor vascular plant established the gene structures of modern SUTs. Tonoplastic Type III and plasmalemmal Type II represent evolutionarily conserved descendants of AG1 and AG2, respectively. Type I and Type IIB were previously thought to evolve after the dicot-monocot split. We show, however, that divergence of Type I from Type III SUT predated basal angiosperms, likely associated with evolution of vascular cambium and phloem transport. Type I SUT was subsequently lost in monocots along with vascular cambium, and independent evolution of Type IIB coincided with modified monocot vasculature. Both Type I and Type IIB underwent lineage-specific expansion. In multiple unrelated taxa, the newly-derived SUTs exhibit biased expression in reproductive tissues, suggesting a functional link between phloem loading and reproductive fitness. Convergent evolution of Type I and Type IIB for SUT function in phloem loading and reproductive organs supports the idea that differential vascular development in dicots and monocots is a strong driver for SUT family evolution in angiosperms.
Lobato, Fran Sérgio; Machado, Vinicius Silvério; Steffen, Valder
2016-07-01
The mathematical modeling of physical and biologic systems represents an interesting alternative to study the behavior of these phenomena. In this context, the development of mathematical models to simulate the dynamic behavior of tumors is configured as an important theme in the current days. Among the advantages resulting from using these models is their application to optimization and inverse problem approaches. Traditionally, the formulated Optimal Control Problem (OCP) has the objective of minimizing the size of tumor cells by the end of the treatment. In this case an important aspect is not considered, namely, the optimal concentrations of drugs may affect the patients' health significantly. In this sense, the present work has the objective of obtaining an optimal protocol for drug administration to patients with cancer, through the minimization of both the cancerous cells concentration and the prescribed drug concentration. The resolution of this multi-objective problem is obtained through the Multi-objective Optimization Differential Evolution (MODE) algorithm. The Pareto's Curve obtained supplies a set of optimal protocols from which an optimal strategy for drug administration can be chosen, according to a given criterion.
Pena-Cristóbal, Maite; Otero-Rey, Eva-María; Tomás, Inmaculada; Blanco-Carrión, Andrés
2016-01-01
Objectives To determine the diagnostic value of diascopy and other non-invasive clinical aids on recent differential diagnosis algorithms of oral mucosal pigmentations affecting subjects of any age. Material and Methods Data Sources: this systematic review was conducted by searching PubMed, Scopus, Dentistry & Oral Sciences Source and the Cochrane Library (2000-2015); Study Selection: two reviewers independently selected all types of English articles describing differential diagnosis algorithms of oral pigmentations and checked the references of finally included papers; Data Extraction: one reviewer performed the data extraction and quality assessment based on previously defined fields while the other reviewer checked their validity. Results Data Synthesis: eight narrative reviews and one single case report met the inclusion criteria. Diascopy was used on six algorithms (66.67%) and X-ray was included once (11.11%; 44.44% with text mentions); these were considered helpful tools in the diagnosis of intravascular and exogenous pigmentations, respectively. Surface rubbing was described once in the text (11.11%). Conclusions Diascopy was the most applied method followed by X-ray and surface rubbing. The limited scope of these procedures only makes them useful when a positive result is obtained, turning biopsy into the most recommended technique when diagnosis cannot be established on clinical grounds alone. Key words:Algorithm, differential diagnosis, flow chart, oral mucosa, oral pigmentation, systematic review. PMID:27703615
Huang Su-Yun
2011-05-01
Full Text Available Abstract Background With the completion of the international HapMap project, many studies have been conducted to investigate the association between complex diseases and haplotype variants. Such haplotype-based association studies, however, often face two difficulties; one is the large number of haplotype configurations in the chromosome region under study, and the other is the ambiguity in haplotype phase when only genotype data are observed. The latter complexity may be handled based on an EM algorithm with family data incorporated, whereas the former can be more problematic, especially when haplotypes of rare frequencies are involved. Here based on family data we propose to cluster long haplotypes of linked SNPs in a biological sense, so that the number of haplotypes can be reduced and the power of statistical tests of association can be increased. Results In this paper we employ family genotype data and combine a clustering scheme with a likelihood ratio statistic to test the association between quantitative phenotypes and haplotype variants. Haplotypes are first grouped based on their evolutionary closeness to establish a set containing core haplotypes. Then, we construct for each family the transmission and non-transmission phase in terms of these core haplotypes, taking into account simultaneously the phase ambiguity as weights. The likelihood ratio test (LRT is next conducted with these weighted and clustered haplotypes to test for association with disease. This combination of evolution-guided haplotype clustering and weighted assignment in LRT is able, via its core-coding system, to incorporate into analysis both haplotype phase ambiguity and transmission uncertainty. Simulation studies show that this proposed procedure is more informative and powerful than three family-based association tests, FAMHAP, FBAT, and an LRT with a group consisting exclusively of rare haplotypes. Conclusions The proposed procedure takes into account the
Ahadi Arif Nugraha
2015-03-01
Full Text Available Salah satu aspek penting dalam perencanaan infrastruktur jaringan seluler adalah Base Transceiver Station (BTS yang merupakan sebuah pemancar dan penerima sinyal telephone seluler. Di satu sisi, peningkatan jumlah menara memang akan mendukung tercapainya pemenuhan kebutuhan masyarakat terhadap layanan telekomunikasi. Namun di sisi lain, penempatan menara yang tanpa perencanaan serta koordinasi yang tepat akan menimbulkan jumlah menara yang berlebih sehingga dapat mengganggu estetika lingkungan, tata ruang suatu wilayah, dan radiasi gelombang radio yang tidak terkontrol sehingga sangat mengganggu. Berdasarkan permasalahan diatas, maka dapat diselesaikan dengan cara menyusun suatu master plan yang lengkap dan rinci tentang penataan lokasi menara di Kabupaten Mojokerto untuk lima tahun mendatang. Penataan lokasi menara dilakukan dengan menggunakan algoritma Differential Evolution (DE untuk menemukan solusi penataan menara yang baik berdasarkan luas cakupan area sel yang dihasilkan, kemudian menggunakan software MapInfo sebagai media visualisasi peta lokasi penempatan menara telekomunikasi. Dalam perancangan menara BTS tahun 2019, Kabupaten Mojokerto membutuhkan 106 menara BTS 2G dan 36 menara BTS 3G. Penempatan menara BTS 2G dan 3G menggunakan algoritma differential evolution mampu mengoptimalkan 2,94% dari luas wilayah Kabupaten Mojokerto
Shahamatnia Ehsan
2016-01-01
Full Text Available Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO, solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO, a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.
孙成富; 张亚红; 陈剑洪; 陈礼青
2013-01-01
在差分进化算法的优化过程中,不断生成更优的解并采用达尔文的“适者生存”思想进行择优保留,这样的遗弃会导致个体有效成分缺失,并失去对新空间的探索开发能力,降低种群多样性,进而使算法早熟收敛并陷入局部最优,因此需要改进差分进化算法并权衡算法的空间探索和开发能力,提高解的精确度和算法收敛速度.为此,基于高斯扰动和免疫搜索策略的差分进化算法被提出.首先,通过生物免疫系统的信息处理机制实现自适应地修正差分进化算法中的缩放因子和交叉因子,以满足优化过程中对这两个参数的取值要求；然后,通过基于高斯扰动的交叉操作算子增加种群的多样性,扩展算法的探索空间,以避免陷入局部最优,进而提高算法的性能.实验结果表明,该优化算法具有良好的寻优性能.%During the evolution process of differential evolution algorithm, good solutions are generated and the 'survival of the fittest' theory of Darwin is employed to select the better solutions, which results in failures of the abandoned individual's effective component and the reduction of population diversity. Thus the differential evolution algorithm is not able to explore new space and traps in local optima. So the differential evolution algorithm has been shown to have certain weaknesses, especially if the global optimum should be located using a limited number of function evaluations. In order to remedy these defects of the differential evolution algorithm mentioned above, weighting space exploration and exploitation is employed for improving it to enhance the convergence speed and solution quality. In this paper,improved differential evolution algorithm based on Gaussian disturbance and immune search startegy is proposed to solve the global optimization problems. Our approach combines several features of previous evolution algorithms in a unique manner. In the novel approach
Sex-differential selection and the evolution of X inactivation strategies.
Tim Connallon
2013-04-01
Full Text Available X inactivation--the transcriptional silencing of one X chromosome copy per female somatic cell--is universal among therian mammals, yet the choice of which X to silence exhibits considerable variation among species. X inactivation strategies can range from strict paternally inherited X inactivation (PXI, which renders females haploid for all maternally inherited alleles, to unbiased random X inactivation (RXI, which equalizes expression of maternally and paternally inherited alleles in each female tissue. However, the underlying evolutionary processes that might account for this observed diversity of X inactivation strategies remain unclear. We present a theoretical population genetic analysis of X inactivation evolution and specifically consider how conditions of dominance, linkage, recombination, and sex-differential selection each influence evolutionary trajectories of X inactivation. The results indicate that a single, critical interaction between allelic dominance and sex-differential selection can select for a broad and continuous range of X inactivation strategies, including unequal rates of inactivation between maternally and paternally inherited X chromosomes. RXI is favored over complete PXI as long as alleles deleterious to female fitness are sufficiently recessive, and the criteria for RXI evolution is considerably more restrictive when fitness variation is sexually antagonistic (i.e., alleles deleterious to females are beneficial to males relative to variation that is deleterious to both sexes. Evolutionary transitions from PXI to RXI also generally increase mean relative female fitness at the expense of decreased male fitness. These results provide a theoretical framework for predicting and interpreting the evolution of chromosome-wide expression of X-linked genes and lead to several useful predictions that could motivate future studies of allele-specific gene expression variation.
B. Thamaraikannan
2014-01-01
Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.
Algebraic dynamics solution and algebraic dynamics algorithm of Burgers equations
2008-01-01
Algebraic dynamics solution and algebraic dynamics algorithm of nonlinear partial differential evolution equations in the functional space are applied to Burgers equation. The results indicate that the approach is effective for analytical solutions to Burgers equation, and the algorithm for numerical solutions of Burgers equation is more stable, with higher precision than other existing finite difference algo-rithms.
A Compound Algorithm of Denoising Using Second-Order and Fourth-Order Partial Differential Equations
Qianshun Chang; Xuecheng Tai; Lily Xing
2009-01-01
In this paper, we propose a compound algorithm for the image restoration. The algorithm is a convex combination of the ROF model and the LLT model with a parameter function 6. The numerical experiments demonstrate that our compound algorithm is efficient and preserves the main advantages of the two models. In particular, the errors of the compound algorithm in L2 norm between the exact images and corresponding restored images are the smallest among the three models. For images with strong noises, the restored images of the compound algorithm are the best in the corresponding restored images. The proposed algorithm combines the fixed point method, an improved AMG method and the Krylov acceleration. It is found that the combination of these methods is efficient and robust in the image restoration.
XUEGuangtao; SHIHua; YOUJinyuan; YAOWensheng
2003-01-01
Mobile peer-to-peer media streaming systems are expected to become as popular as the peer-to-peer file sharing systems. In this paper, we study two key problems arising from mobile peer-to-peer media streaming: the stability of interconnection between supplying peers and requesting peers in mobile peer-to-peer streaming system; and fast capacity amplification of the entire mobile peer-to-peer streaming system. We use the Stable group algorithm to characterize user mobility in mobile ad hoc networks. Based on the stable group, we then propose a distributed Stable-group differentiated admission control algorithm (SGDACp2p), which leads to fast amplifying the system's total streaming capacity using its self-growing. At last, the extensive simulation results are presented to compare between the SGDACp2p and traditional methods to prove the superiority of the algorithm.
Optimum Synthesis of Mechanism for single- and hybrid-tasks using Differential Evolution
Penunuri, F; Villanueva, C; Pech-Oy, D
2011-01-01
In this document the optimal dimensional synthesis for planar mechanisms using differential evo- lution (DE) is shown. Four study cases are presented: in the first case, the synthesis of a mechanism for hybrid-tasks, considering path generation, function generation, and motion generation, is car- ried out. The second and third cases deal with path generation with and without prescribed timing. Finally, the synthesis of an Ackerman's mechanism is performed. The order defect problem is addressed by manipulating individuals instead of penalizing or discretizing the searching space for the parameters, as was proposed by other authors. A new technique which consists of applying a transformation in order to satisfy the Grashof and crank conditions to generate an initial elitist population is introduced. As a result, the evolutionary algorithm increases its efficiency.
Amjad, M.; Salam, Z.; Ishaque, K.
2014-04-01
In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.
Ika Ayu Fajarwati
2012-09-01
Full Text Available Vehicle Routing Problem (VRP merupakan permasalahan optimasi kombinatorial kompleks yang memiliki peranan penting dalam manajemen sistem distribusi dengan tujuan meminimalkan biaya yang diperlukan, dimana penentuan biaya berkaitan dengan jarak dari rute yang ditempuh oleh armada distribusi. Ciri dari VRP yaitu penggunaan armada dengan kapasitas tertentu dan kegiatannya berpusat pada satu titik depot untuk melayani pelanggan pada titik-titik tertentu dengan jumlah permintaan yang diketahui. Kasus distribusi yang menggabungkan aktifitas pengiriman dan pengambilan produk termasuk dalam salah satu jenis VRP yaitu Vehicle Routing Problem Delivery and Pick-Up (VRP-DP. Banyak metode yang dapat digunakan untuk menyelesaikan permasalahan VRP-DP, salah satunya adalah metode optimasi metaheuristik yaitu Algoritma Differential Evolution yang akan diperkenalkan dalam penelitian ini. Hasil yang diharapkan nantinya adalah rute distribusi optimal untuk armada perusahaan sehingga menghasilkan jarak tempuh dan tentunya total biaya yang minimal dalam memenuhi semua permintaan pelanggan
Yan, Shaomin; Li, Zhenchong; Wu, Guang
2010-04-01
The understanding of evolutionary mechanism is important, and equally important is to describe the evolutionary process. If so, we would know where the biological evolution will go. At species level, we would know whether and when a species will extinct or be prosperous. At protein level, we would know when a protein family will mutate more. In our previous study, we explored the possibility of using the differential equation to describe the evolution of protein family from influenza A virus based on the assumption that the mutation process is the exchange of entropy between protein family and its environment. In this study, we use the analytical solution of system of differential equations to fit the evolution of matrix protein 1 family from influenza A virus. Because the evolutionary process goes along the time course, it can be described by differential equation. The results show that the evolution of a protein family can be fitted by the analytical solution. With the obtained fitted parameters, we may predict the evolution of matrix protein 1 family from influenza A virus. Our model would be the first step towards the systematical modeling of biological evolution and paves the way for further modeling.
Pearse, Devon E; Hayes, Sean A; Bond, Morgan H; Hanson, Chad V; Anderson, Eric C; Macfarlane, R Bruce; Garza, John Carlos
2009-01-01
Adaptation to novel habitats and phenotypic plasticity can be counteracting forces in evolution, but both are key characteristics of the life history of steelhead/rainbow trout (Oncorhynchus mykiss). Anadromous steelhead reproduce in freshwater river systems and small coastal streams but grow and mature in the ocean. Resident rainbow trout, either sympatric with steelhead or isolated above barrier dams or waterfalls, represent an alternative life-history form that lives entirely in freshwater. We analyzed population genetic data from 1486 anadromous and resident O. mykiss from a small stream in coastal California with multiple barrier waterfalls. Based on data from 18 highly variable microsatellite loci (He = 0.68), we conclude that the resident population above one barrier, Big Creek Falls, is the result of a recent anthropogenic introduction from the anadromous population of O. mykiss below the falls. Furthermore, fish from this above-barrier population occasionally descend over the falls and have established a genetically differentiated below-barrier subpopulation at the base of the falls, which appears to remain reproductively isolated from their now-sympatric anadromous ancestors. These results support a hypothesis of rapid evolution of a purely resident life history in the above-barrier population in response to strong selection against downstream movement.
Golan, Guy; Oksenberg, Adi; Peleg, Zvi
2015-09-01
Wheat is one of the Neolithic founder crops domesticated ~10 500 years ago. Following the domestication episode, its evolution under domestication has resulted in various genetic modifications. Grain weight, embryo weight, and the interaction between those factors were examined among domesticated durum wheat and its direct progenitor, wild emmer wheat. Experimental data show that grain weight has increased over the course of wheat evolution without any parallel change in embryo weight, resulting in a significantly reduced (30%) embryo weight/grain weight ratio in domesticated wheat. The genetic factors associated with these modifications were further investigated using a population of recombinant inbred substitution lines that segregated for chromosome 2A. A cluster of loci affecting grain weight and shape was identified on the long arm of chromosome 2AL. Interestingly, a novel locus controlling embryo weight was mapped on chromosome 2AS, on which the wild emmer allele promotes heavier embryos and greater seedling vigour. To the best of our knowledge, this is the first report of a QTL for embryo weight in wheat. The results suggest a differential selection of grain and embryo weight during the evolution of domesticated wheat. It is argued that conscious selection by early farmers favouring larger grains and smaller embryos appears to have resulted in a significant change in endosperm weight/embryo weight ratio in the domesticated wheat. Exposing the genetic factors associated with endosperm and embryo size improves our understanding of the evolutionary dynamics of wheat under domestication and is likely to be useful for future wheat-breeding efforts.
Vesterstrøm, Jacob Svaneborg; Thomsen, Rene
2004-01-01
in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally...
Identification of time-varying nonlinear systems using differential evolution algorithm
Perisic, Nevena; Green, Peter L; Worden, Keith;
2013-01-01
Online monitoring of modal and physical parameters which change due to damage progression and aging of mechanical and structural systems is important for the condition and health monitoring of these systems. Usually, only the limited number of imperfect, noisy system state measurements is availab...
Amin Qorbani
2011-12-01
Full Text Available Fractal Image Compression is a well-known problem which is in the class of NP-Hard problems.Quantum Evolutionary Algorithm is a novel optimization algorithm which uses a probabilisticrepresentation for solutions and is highly suitable for combinatorial problems like Knapsack problem.Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for thiskind of problems yet. This paper improves QEA whit change population size and used it in fractal imagecompression. Utilizing the self-similarity property of a natural image, the partitioned iterated functionsystem (PIFS will be found to encode an image through Quantum Evolutionary Algorithm (QEA methodExperimental results show that our method has a better performance than GA and conventional fractalimage compression algorithms.
Hoang, TY
1994-01-01
A real-time, high-rate precision navigation Kalman filter algorithm is developed and analyzed. This Navigation algorithm blends various navigation data collected during terminal area approach of an instrumented helicopter. Navigation data collected include helicopter position and velocity from a global position system in differential mode (DGPS) as well as helicopter velocity and attitude from an inertial navigation system (INS). The goal of the Navigation algorithm is to increase the DGPS accuracy while producing navigational data at the 64 Hertz INS update rate. It is important to note that while the data was post flight processed, the Navigation algorithm was designed for real-time analysis. The design of the Navigation algorithm resulted in a nine-state Kalman filter. The Kalman filter's state matrix contains position, velocity, and velocity bias components. The filter updates positional readings with DGPS position, INS velocity, and velocity bias information. In addition, the filter incorporates a sporadic data rejection scheme. This relatively simple model met and exceeded the ten meter absolute positional requirement. The Navigation algorithm results were compared with truth data derived from a laser tracker. The helicopter flight profile included terminal glideslope angles of 3, 6, and 9 degrees. Two flight segments extracted during each terminal approach were used to evaluate the Navigation algorithm. The first segment recorded small dynamic maneuver in the lateral plane while motion in the vertical plane was recorded by the second segment. The longitudinal, lateral, and vertical averaged positional accuracies for all three glideslope approaches are as follows (mean plus or minus two standard deviations in meters): longitudinal (-0.03 plus or minus 1.41), lateral (-1.29 plus or minus 2.36), and vertical (-0.76 plus or minus 2.05).
Evolution of a magnetic field in a differentially rotating radiative zone
Gaurat, Mathieu; Lignières, François; Gastine, Thomas
2015-01-01
Recent spectropolarimetric surveys of main-sequence intermediate-mass stars have exhibited a dichotomy in the distribution of the observed magnetic field between the kG dipoles of Ap/Bp stars and the sub-Gauss magnetism of Vega and Sirius. We would like to test whether this dichotomy is linked to the stability versus instability of large-scale magnetic configurations in differentially rotating radiative zones. We computed the axisymmetric magnetic field obtained from the evolution of a dipolar field threading a differentially rotating shell. A full parameter study including various density profiles and initial and boundary conditions was performed with a 2D numerical code. We then focused on the ratio between the toroidal and poloidal components of the magnetic field and discuss the stability of the configurations dominated by the toroidal component using local stability criteria and insights from recent 3D numerical simulations. The numerical results and a simple model show that the ratio between the toroida...
Differential Evolution for Task Assignment Problem%求解任务指派问题的差异演化算法磁
刘家骏
2015-01-01
建立了任务指派问题的数学模型，采用差异演化算法对其进行求解，给出了差异演化算法求解该问题的具体方案，对不同的任务指派问题算例进行了仿真实验。结果表明，算法可以有效、快速地找到任务指派问题的最优解。%Task assignment problem is a typical NP problem .Differential evolution is used to solve the task assignment problem .The model of task assignment problem is formulated and the detailed solution for solving task assignment problem based on differential evolution is illuminated .The results from the experiments on different task assignment problem in‐stances show that this algorithm is able to find good solutions quickly .
Solving Quadratic Assignment Problem Based on Differential Evolution%差异演化算法求解二次分配问题
杨卿誉; 王志刚
2011-01-01
二次分配问题是典型的NP难题.建立了二次分配问题的数学模型.设计了基于差异演化算法的新方法对其进行求解.给出了差异演化算法求解该问题的具体方案.对不同的二次分配问题算例进行了仿真实验.结果表明,算法可以有效、快速地找到二次分配问题的最优解.%Quadratic assignment problem is a typical NP problem. The model of quadratic assignment problem was formulated. A new strategy based on differential evolution was designed to solve the quadratic assignment problem and the detailed solution for solving quadratic assignment problem based on differential evolution was illuminated. The results from the experiments on different quadratic assignment problem instances show that this algorithm is able to find good solutions quickly.
Fixation times in differentiation and evolution in the presence of bottlenecks, deserts, and oases.
Chou, Tom; Wang, Yu
2015-05-01
Cellular differentiation and evolution are stochastic processes that can involve multiple types (or states) of particles moving on a complex, high-dimensional state-space or "fitness" landscape. Cells of each specific type can thus be quantified by their population at a corresponding node within a network of states. Their dynamics across the state-space network involve genotypic or phenotypic transitions that can occur upon cell division, such as during symmetric or asymmetric cell differentiation, or upon spontaneous mutation. Here, we use a general multi-type branching processes to study first passage time statistics for a single cell to appear in a specific state. Our approach readily allows for nonexponentially distributed waiting times between transitions, reflecting, e.g., the cell cycle. For simplicity, we restrict most of our detailed analysis to exponentially distributed waiting times (Poisson processes). We present results for a sequential evolutionary process in which L successive transitions propel a population from a "wild-type" state to a given "terminally differentiated," "resistant," or "cancerous" state. Analytic and numeric results are also found for first passage times across an evolutionary chain containing a node with increased death or proliferation rate, representing a desert/bottleneck or an oasis. Processes involving cell proliferation are shown to be "nonlinear" (even though mean-field equations for the expected particle numbers are linear) resulting in first passage time statistics that depend on the position of the bottleneck or oasis. Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations (such as the fixed-population and mean-field assumptions) in evaluating fixation times.
Guangyu Chen
2014-01-01
Full Text Available An improved differential evolution (DE method based on the dynamic search strategy (IDEBDSS is proposed to solve dynamic economic dispatch problem with valve-point effects in this paper. The proposed method combines the DE algorithm with the dynamic search strategy, which improves the performance of the algorithm. DE is the main optimizer in the method proposed. While chaotic sequences are applied to obtain the dynamic parameter settings in DE, dynamic search strategy which consists of two steps, global search strategy and local search strategy, is used to improve algorithm efficiency. To accelerate convergence, a new infeasible solution handing method is adopted in the local search strategy; meanwhile, an orthogonal crossover (OX operator is added to the global search strategy to enhance the optimization search ability. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by three test systems, and the simulation results reveal that the IDEBDSS method can obtain better solutions with higher efficiency than the standard DE and other methods reported in the recent literature.
黄映; 李扬; 高赐威
2011-01-01
在多目标电网规划问题中,综合考虑经济性、安全可靠性和环境影响等因素后,提出了非支配排序差分进化算法.以电网投资、运行维护费用、网损费用、线路走廊面积最小为目标建立了多目标电网规划模型.非支配排序差分进化算法将Paret0非支配排序法与差分进化算法相结合,采用动态调整策略调整差分进化算法控制参数,改进了个体拥挤比较机制,提高了算法的全局搜索能力和种群多样性,并基于模糊集理论选取最优折衷解.Garver-6节点和Garver-18节点系统算例结果表明,该算法可以有效生成分布均匀的Pareto最优解集,在求解多目标电网规划问题中具有可行性和优越性.%Considering the factors in multi-objective power network planning such as economy, security and reliability as well as environment influences, a non-dominated sorting differential evolution algorithm is proposed. Taking minimized investment for power network, operation and maintenance costs, network loss cost and line corridor as objectives, a multi-objective power network planning model is built. The non-dominated sorting differential evolution algorithm integrates Pareto non-dominated sorting algorithm with differential evolution algorithm and the control parameters of differential evolution are regulated by dynamic adjustment strategy; the crowding comparison mechanism of individuals is modified to improve the global search ability and population diversity, and the optimal compromise solution is chosen according to fuzzy set theory. Numerical results of Garver 6-bus system and Garver 18-bus system show that the proposed algorithm is better than non-dominated sorting genetic algorithm-II (NSGA-II) and can effectively generate optimal Pareto solution set, so it is of feasibility and superiority in solving multi-objective power network planning.
Yazdi, Ebrahim
2010-01-01
In this paper, a simple Neural controller has been used to achieve stable walking in a NAO biped robot, with 22 degrees of freedom that implemented in a virtual physics-based simulation environment of Robocup soccer simulation environment. The algorithm uses a Matsuoka base neural oscillator to generate control signal for the biped robot. To find the best angular trajectory and optimize network parameters, a new population-based search algorithm, called the Harmony Search (HS) algorithm, has been used. The algorithm conceptualized a group of musicians together trying to search for better state of harmony. Simulation results demonstrate that the modification of the step period and the walking motion due to the sensory feedback signals improves the stability of the walking motion.
An efficient algorithm for solving nonlinear system of differential equations and applications
Mustafa GÜLSU
2015-10-01
Full Text Available In this article, we apply Chebyshev collocation method to obtain the numerical solutions of nonlinear systems of differential equations. This method transforms the nonlinear systems of differential equation to nonlinear systems of algebraic equations. The convergence of the numerical method are given and their applicability is illustrated with some examples.
Vasiliu, Daniel; Clamons, Samuel; McDonough, Molly; Rabe, Brian; Saha, Margaret
2015-01-01
Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED). Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.
Daniel Vasiliu
Full Text Available Global gene expression analysis using microarrays and, more recently, RNA-seq, has allowed investigators to understand biological processes at a system level. However, the identification of differentially expressed genes in experiments with small sample size, high dimensionality, and high variance remains challenging, limiting the usability of these tens of thousands of publicly available, and possibly many more unpublished, gene expression datasets. We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized Euclidean distance (PED. Our method uses PED to build a classifier on the experimental data to rank genes by importance. In place of cross-validation, which is required by most similar methods but not reliable for experiments with small sample size, we use a simulation-based approach to additively build a list of differentially expressed genes from the rank-ordered list. Our simulation-based approach maintains a low false discovery rate while maximizing the number of differentially expressed genes identified, a feature critical for downstream pathway analysis. We apply our method to microarray data from an experiment perturbing the Notch signaling pathway in Xenopus laevis embryos. This dataset was chosen because it showed very little differential expression according to limma, a powerful and widely-used method for microarray analysis. Our method was able to detect a significant number of differentially expressed genes in this dataset and suggest future directions for investigation. Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality.
Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.
Warmflash, Aryeh; Francois, Paul; Siggia, Eric D
2012-10-01
The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.
Sunaguchi, Naoki [Faculty of Science and Technology, Gunma University, Kiryu, Gunma 376-8515 (Japan); Yuasa, Tetsuya [Graduate School of Engineering and Science, Yamagata University, Yonezawa, Yamagata 992-8510 (Japan); Gupta, Rajiv [Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114 (United States); Ando, Masami [Research Institute for Science and Technology, Tokyo University of Science, Noda, Chiba 278-8510 (Japan)
2015-12-21
The main focus of this paper is reconstruction of tomographic phase-contrast image from a set of projections. We propose an efficient reconstruction algorithm for differential phase-contrast computed tomography that can considerably reduce the number of projections required for reconstruction. The key result underlying this research is a projection theorem that states that the second derivative of the projection set is linearly related to the Laplacian of the tomographic image. The proposed algorithm first reconstructs the Laplacian image of the phase-shift distribution from the second-derivative of the projections using total variation regularization. The second step is to obtain the phase-shift distribution by solving a Poisson equation whose source is the Laplacian image previously reconstructed under the Dirichlet condition. We demonstrate the efficacy of this algorithm using both synthetically generated simulation data and projection data acquired experimentally at a synchrotron. The experimental phase data were acquired from a human coronary artery specimen using dark-field-imaging optics pioneered by our group. Our results demonstrate that the proposed algorithm can reduce the number of projections to approximately 33% as compared with the conventional filtered backprojection method, without any detrimental effect on the image quality.
A Throughput-Driven Scheduling Algorithm of Differentiated Service for Web Cluster
无
2006-01-01
Requests distribution is an key technology for Web cluster server. This paper presents a throughput-driven scheduling algorithm (TDSA). The algorithm adopts the throughput of cluster back-ends to evaluate their load and employs the neural network model to predict the future load so that the scheduling system features a self-learning capability and good adaptability to the change of load. Moreover, it separates static requests from dynamic requests to make full use of the CPU resources and takes the locality of requests into account to improve the cache hit ratio. Experimental results from the testing tool of WebBenchTM show better performance for Web cluster server with TDSA than that with traditional scheduling algorithms.
Raj, Dibyendu; Ghosh, Esha; Mukherjee, Avik K; Nozaki, Tomoyoshi; Ganguly, Sandipan
2014-02-10
Giardia lamblia is a unicellular, early branching eukaryote causing giardiasis, one of the most common human enteric diseases. Giardia, a microaerophilic protozoan parasite has to build up mechanisms to protect themselves against oxidative stress within the human gut (oxygen concentration 60 μM) to establish its pathogenesis. G. lamblia is devoid of the conventional mechanisms of the oxidative stress management system, including superoxide dismutase, catalase, peroxidase, and glutathione cycling, which are present in most eukaryotes. NADH oxidase is a major component of the electron transport chain of G. lamblia, which in concurrence with disulfide reductase, protects oxygen-labile proteins such as pyruvate: ferredoxin oxidoreductase against oxidative stress by sustaining a reduced intracellular environment. It also contains the arginine dihydrolase pathway, which occurs in a number of anaerobic prokaryotes, includes substrate level phosphorylation and adequately active to make a major contribution to ATP production. To study differential gene expression under three types of oxidative stress, a Giardia genomic DNA array was constructed and hybridized with labeled cDNA of cells with or without stress. The transcriptomic data has been analyzed and further validated using real time PCR. We identified that out of 9216 genes represented on the array, more than 200 genes encoded proteins with functions in metabolism, oxidative stress management, signaling, reproduction and cell division, programmed cell death and cytoskeleton. We recognized genes modulated by at least ≥ 2 fold at a significant time point in response to oxidative stress. The study has highlighted the genes that are differentially expressed during the three experimental conditions which regulate the stress management pathway differently to achieve redox homeostasis. Identification of some unique genes in oxidative stress regulation may help in new drug designing for this common enteric parasite prone to
An efficient algorithm for the parallel solution of high-dimensional differential equations
Klus, Stefan; Liu, Cong; Dellnitz, Michael
2010-01-01
The study of high-dimensional differential equations is challenging and difficult due to the analytical and computational intractability. Here, we significantly improve the speed of waveform relaxation (WR), a method to simulate high-dimensional differential-algebraic equations. This new method termed adaptive waveform relaxation (AWR) is tested on a communication network example. Further we analyze different heuristics for computing graph partitions tailored to adaptive waveform relaxation.
Wong, Yin Mei; Wilkie, Joshua
2006-01-01
Since the introduction of the Black-Scholes model stochastic processes have played an increasingly important role in mathematical finance. In many cases prices, volatility and other quantities can be modeled using stochastic ordinary differential equations. Available methods for solving such equations have until recently been markedly inferior to analogous methods for deterministic ordinary differential equations. Recently, a number of methods which employ variable stepsizes to control local ...
Fast Algorithm of Numerical Solutions for Strong Nonlinear Partial Differential Equations
Tongjing Liu
2014-07-01
Full Text Available Because of a high mobility ratio in the chemical and gas flooding for oil reservoirs, the problems of numerical dispersion and low calculation efficiency also exist in the common methods, such as IMPES and adaptive implicit methods. Therefore, the original calculation process, “one-step calculation for pressure and multistep calculation for saturation,” was improved by introducing a velocity item and using the fractional flow in a direction to calculate the saturation. Based on these developments, a new algorithm of numerical solution for “one-step calculation for pressure, one-step calculation for velocity, and multi-step calculation for fractional flow and saturation” was obtained, and the convergence condition for the calculation of saturation was also proposed. The simulation result of a typical theoretical model shows that the nonconvergence occurred for about 6,000 times in the conventional algorithm of IMPES, and a high fluctuation was observed in the calculation steps. However, the calculation step of the fast algorithm was stabilized for 0.5 d, indicating that the fast algorithm can avoid the nonconvergence caused by the saturation that was directly calculated by pressure. This has an important reference value in the numerical simulations of chemical and gas flooding for oil reservoirs.
刁鸣; 王小兰; 高洪元
2016-01-01
为了有效求解连续优化问题，基于差分进化算法和头脑风暴优化算法的智能演进原理，提出一种新的全局搜索算法，即差分头脑风暴算法。通过4个经典的基准函数对该算法进行测试，并将该算法应用于频谱感知这个认知无线电领域的热点问题，提出基于差分头脑风暴的协作式频谱感知算法。使用差分头脑风暴算法、头脑风暴算法、混合蛙跳算法以及粒子群算法进行仿真对比。仿真结果表明，所提出的算法基于设计的创新方程，具有很强的全局收敛能力，能够显著改进头脑风暴算法的性能；基于差分头脑风暴的频谱感知检测概率比其他算法都高，且收敛速度比头脑风暴算法提高至少3倍。%In order to solve continuous optimization problems effectively, a novel global search method named differ⁃ential brain storm optimization ( DBSO) algorithm was proposed based on the intelligent evolutionary principles of brain storm optimization (BSO) algorithm and differential evolution (DE) algorithm. The proposed algorithm was tested with four classic benchmark functions, and applied to hot spectrum sensing issue in the domain of cognitive radio, and a cooperative spectrum sensing method was proposed based on DBSO algorithm. Comparison was conduc⁃ted between DBSO algorithm, BSO algorithm, shuffled frog leaping algorithm ( SFLA) and particle swarm optimiza⁃tion ( PSO) algorithm. The simulation results of the benchmark functions show that in terms of convergence preci⁃sion, the presented DBSO algorithm based on new creating equations outperforms other 3 intelligent algorithms;compared with these three intelligent spectrum sensing algorithms, the detection probability of DBSO is higher, and the convergence speed of the DBSO increases 3 times compared to BSO algorithm.
Coronary artery segmentation in X-ray angiogram using Gabor filters and differential evolution
Cervantes S, F.; Hernandez A, A.; Cruz A, I. [Centro de Investigacion en Matematicas, A. C., Jalisco s/n, Col. Valenciana, 36240 Guanajuato, Gto. (Mexico); Solorio M, S. [IMSS, Unidad de Investigacion, UMAE Hospital de Especialidades No. 1 del Centro Medico Nacional del Bajio, 37260 Leon, Guanajuato (Mexico); Cordova F, T. [Universidad de Guanajuato, Departamento de Ingenieria Fisica, 37150 Leon, Guanajuato (Mexico); Avina C, J. G., E-mail: ivan.cruz@cimat.mx [Universidad de Guanajuato, Departamento de Electronica, 36885 Salamanca, Guanajuato (Mexico)
2016-10-15
Segmentation of coronary arteries in X-ray angiograms represents an essential task for computer-aided diagnosis, since it can help cardiologists in diagnosing and monitoring vascular abnormalities. Due to the main disadvantages of the X-ray angiograms are the nonuniform illumination, and the weak contrast between blood vessels and image background, different vessel enhancement methods have been introduced. In this paper, a novel method for blood vessel enhancement based on Gabor filters tuned using the optimization strategy of Differential evolution (De) is proposed. Because the Gabor filters are governed by three different parameters, the optimal selection of those parameters is highly desirable in order to maximize the vessel detection rate while reducing the computational cost of the training stage. To obtain the optimal set of parameters for the Gabor filters, the area (Az) under the receiver operating characteristic curve is used as objective function. In the experimental results, the proposed method obtained the highest detection performance with Az = 0.956 using a test set of 60 angiograms, and Az = 0.934 with a training set of 20 angiograms compared with different state-of-the-art vessel detection methods. In addition, the experimental results in terms of computational time have also shown that the proposed method can be highly suitable for clinical decision support. (Author)
A software for parameter optimization with Differential Evolution Entirely Parallel method
Konstantin Kozlov
2016-08-01
Full Text Available Summary. Differential Evolution Entirely Parallel (DEEP package is a software for finding unknown real and integer parameters in dynamical models of biological processes by minimizing one or even several objective functions that measure the deviation of model solution from data. Numerical solutions provided by the most efficient global optimization methods are often problem-specific and cannot be easily adapted to other tasks. In contrast, DEEP allows a user to describe both mathematical model and objective function in any programming language, such as R, Octave or Python and others. Being implemented in C, DEEP demonstrates as good performance as the top three methods from CEC-2014 (Competition on evolutionary computation benchmark and was successfully applied to several biological problems. Availability. DEEP method is an open source and free software distributed under the terms of GPL licence version 3. The sources are available at http://deepmethod.sourceforge.net/ and binary packages for Fedora GNU/Linux are provided for RPM package manager at https://build.opensuse.org/project/repositories/home:mackoel:compbio.
Planetary eclipse mapping of CoRoT-2a. Evolution, differential rotation, and spot migration
Huber, K F; Wolter, U; Schmitt, J H M M
2010-01-01
The lightcurve of CoRoT-2 shows substantial rotational modulation and deformations of the planet's transit profiles caused by starspots. We consistently model the entire lightcurve, including both rotational modulation and transits, stretching over approximately 30 stellar rotations and 79 transits. The spot distribution and its evolution on the noneclipsed and eclipsed surface sections are presented and analyzed, making use of the high resolution achievable under the transit path. We measure the average surface brightness on the eclipsed section to be (5\\pm1) % lower than on the noneclipsed section. Adopting a solar spot contrast, the spot coverage on the entire surface reaches up to 19 % and a maximum of almost 40 % on the eclipsed section. Features under the transit path, i.e. close to the equator, rotate with a period close to 4.55 days. Significantly higher rotation periods are found for features on the noneclipsed section indicating a differential rotation of $\\Delta \\Omega > 0.1$. Spotted and unspotted...
LO Peg: surface differential rotation, flares, and spot-topographic evolution
Karmakar, Subhajeet; Savanov, I S; Taş, G; Pandey, S B; Misra, K; Joshi, S; Dmitrienko, E S; Sakamoto, T; Gehrels, N; Okajima, T
2016-01-01
Using the wealth of ~24 yr multiband data, we present an in-depth study of the star-spot cycles, surface differential rotations (SDR), optical flares, evolution of star-spot distributions, and coronal activities on the surface of young, single, main-sequence, ultrafast rotator (UFR) LO Peg. From the long-term V -band photometry, we derive rotational period of LO Peg to be 0.4231 +/- 0.0001 d. Using the seasonal variations on the rotational period, the SDR pattern is investigated, and shows a solar-like pattern of SDR. A cyclic pattern with period of ~2.7 yr appears to be present in rotational period variation. During the observations, 20 optical flares are detected with a flare frequency of 1 flare per two days and with flare energy of 10^{31-34} erg. The surface coverage of cool spots is found to be in the range of 9-26 per cent. It appears that the high- and low-latitude spots are interchanging their positions. Quasi-simultaneous observations in X-ray, UV, and optical photometric bands show a signature of a...
Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading
Shangkun Deng
2014-01-01
Full Text Available Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL with differential evolution (DE for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI, while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.
Long-term density evolution through semi-analytical and differential algebra techniques
Wittig, Alexander; Colombo, Camilla; Armellin, Roberto
2017-08-01
This paper introduces and combines for the first time two techniques to allow long-term density propagation in astrodynamics. First, we introduce an efficient method for the propagation of phase space densities based on differential algebra (DA) techniques. Second, this DA density propagator is used in combination with a DA implementation of the averaged orbital dynamics through semi-analytical methods. This approach combines the power of orbit averaging with the efficiency of DA techniques. While the DA-based method for the propagation of densities introduced in this paper is independent of the dynamical system under consideration, the particular combination of DA techniques with averaged equations of motion yields a fast and accurate technique to propagate large clouds of initial conditions and their associated probability density functions very efficiently for long time. This enables the study of the long-term behavior of particles subjected to the given dynamics. To demonstrate the effectiveness of the proposed approach, the evolution of a cloud of high area-to-mass objects in Medium Earth Orbit is reproduced considering the effects of solar radiation pressure, the Earth's oblateness and luni-solar perturbations. The method can propagate 10,000 random fragments and their density for 1 year within a few seconds on a common desktop PC.
Minakhi Rout
2014-01-01
Full Text Available To alleviate the limitations of statistical based methods of forecasting of exchange rates, soft and evolutionary computing based techniques have been introduced in the literature. To further the research in this direction this paper proposes a simple but promising hybrid prediction model by suitably combining an adaptive autoregressive moving average (ARMA architecture and differential evolution (DE based training of its feed-forward and feed-back parameters. Simple statistical features are extracted for each exchange rate using a sliding window of past data and are employed as input to the prediction model for training its internal coefficients using DE optimization strategy. The prediction efficiency is validated using past exchange rates not used for training purpose. Simulation results using real life data are presented for three different exchange rates for one–fifteen months’ ahead predictions. The results of the developed model are compared with other four competitive methods such as ARMA-particle swarm optimization (PSO, ARMA-cat swarm optimization (CSO, ARMA-bacterial foraging optimization (BFO and ARMA-forward backward least mean square (FBLMS. The derivative based ARMA-FBLMS forecasting model exhibits worst prediction performance of the exchange rates. Comparisons of different performance measures including the training time of the all three evolutionary computing based models demonstrate that the proposed ARMA-DE exchange rate prediction model possesses superior short and long range prediction potentiality compared to others.
Optimal layout design of obstacles for panic evacuation using differential evolution
Zhao, Yongxiang; Li, Meifang; Lu, Xin; Tian, Lijun; Yu, Zhiyong; Huang, Kai; Wang, Yana; Li, Ting
2017-01-01
To improve the pedestrian outflow in panic situations by suitably placing an obstacle in front of the exit, it is vital to understand the physical mechanism behind the evacuation efficiency enhancement. In this paper, a robust differential evolution is firstly employed to optimize the geometrical parameters of different shaped obstacles in order to achieve an optimal evacuation efficiency. Moreover, it is found that all the geometrical parameters of obstacles could markedly influence the evacuation efficiency of pedestrians, and the best way for achieving an optimal pedestrian outflow is to slightly shift the obstacle from the center of the exit which is consistent with findings of extant literature. Most importantly, by analyzing the profiles of density, velocity and specific flow, as well as the spatial distribution of crowd pressure, we have proven that placing an obstacle in panic situations does not reduce or absorb the pressure in the region of exit, on the contrary, promotes the pressure to a much higher level, hence the physical mechanism behind the evacuation efficiency enhancement is not a pressure decrease in the region of exit, but a significant reduction of high density region by effective separation in space which finally causes the increasing of escape speed and evacuation outflow. Finally, it is clearly demonstrated that the panel-like obstacle is considerably more robust and stable than the pillar-like obstacle to guarantee the enhancement of evacuation efficiency under different initial pedestrian distributions, different initial crowd densities as well as different desired velocities.
Nhat-Duc Hoang
2015-01-01
Full Text Available In construction management, the task of planning project schedules with consideration of labor utilization is very crucial. However, the commonly used critical path method (CPM does not inherently take into account this issue. Consequently, the labor utilization of the project schedule derived from the CPM method often has substantial low ebbs and high peaks. This research proposes a model to obtain project schedule with the least fluctuation in labor demand while still satisfying the project deadline and maintain the project cost. The Differential Evolution (DE, a fast and efficient metaheuristic, is employed to search for the most desirable solution of project execution among numerous combinations of activities’ crew sizes and start times. Furthermore, seven DE’s mutation strategies have also been employed for solving the optimization at hand. Experiment results point out that the Target-to-Best 1 and a new hybrid mutation strategy can attain the best solution of project schedule with the least fluctuation in labor demand. Accordingly, the proposed framework can be an effective tool to assist decision-makers in the project planning phase.
Using Differential Evolution to Optimize Learning from Signals and Enhance Network Security
Harmer, Paul K [Air Force Institute of Technology; Temple, Michael A [Air Force Institute of Technology; Buckner, Mark A [ORNL; Farquhar, Ethan [ORNL
2011-01-01
Computer and communication network attacks are commonly orchestrated through Wireless Access Points (WAPs). This paper summarizes proof-of-concept research activity aimed at developing a physical layer Radio Frequency (RF) air monitoring capability to limit unauthorizedWAP access and mprove network security. This is done using Differential Evolution (DE) to optimize the performance of a Learning from Signals (LFS) classifier implemented with RF Distinct Native Attribute (RF-DNA) fingerprints. Performance of the resultant DE-optimized LFS classifier is demonstrated using 802.11a WiFi devices under the most challenging conditions of intra-manufacturer classification, i.e., using emissions of like-model devices that only differ in serial number. Using identical classifier input features, performance of the DE-optimized LFS classifier is assessed relative to a Multiple Discriminant Analysis / Maximum Likelihood (MDA/ML) classifier that has been used for previous demonstrations. The comparative assessment is made using both Time Domain (TD) and Spectral Domain (SD) fingerprint features. For all combinations of classifier type, feature type, and signal-to-noise ratio considered, results show that the DEoptimized LFS classifier with TD features is uperior and provides up to 20% improvement in classification accuracy with proper selection of DE parameters.
Extended Kalman smoother with differential evolution technique for denoising of ECG signal.
Panigrahy, D; Sahu, P K
2016-09-01
Electrocardiogram (ECG) signal gives a lot of information on the physiology of heart. In reality, noise from various sources interfere with the ECG signal. To get the correct information on physiology of the heart, noise cancellation of the ECG signal is required. In this paper, the effectiveness of extended Kalman smoother (EKS) with the differential evolution (DE) technique for noise cancellation of the ECG signal is investigated. DE is used as an automatic parameter selection method for the selection of ten optimized components of the ECG signal, and those are used to create the ECG signal according to the real ECG signal. These parameters are used by the EKS for the development of the state equation and also for initialization of the parameters of EKS. EKS framework is used for denoising the ECG signal from the single channel. The effectiveness of proposed noise cancellation technique has been evaluated by adding white, colored Gaussian noise and real muscle artifact noise at different SNR to some visually clean ECG signals from the MIT-BIH arrhythmia database. The proposed noise cancellation technique of ECG signal shows better signal to noise ratio (SNR) improvement, lesser mean square error (MSE) and percent of distortion (PRD) compared to other well-known methods.
Integrated model of multiple kernel learning and differential evolution for EUR/USD trading.
Deng, Shangkun; Sakurai, Akito
2014-01-01
Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits.
Lin, Ying Chih; Lu, Chin Lung; Chang, Hwan-You; Tang, Chuan Yi
2005-01-01
In the study of genome rearrangement, the block-interchanges have been proposed recently as a new kind of global rearrangement events affecting a genome by swapping two nonintersecting segments of any length. The so-called block-interchange distance problem, which is equivalent to the sorting-by-block-interchange problem, is to find a minimum series of block-interchanges for transforming one chromosome into another. In this paper, we study this problem by considering the circular chromosomes and propose a Omicron(deltan) time algorithm for solving it by making use of permutation groups in algebra, where n is the length of the circular chromosome and delta is the minimum number of block-interchanges required for the transformation, which can be calculated in Omicron(n) time in advance. Moreover, we obtain analogous results by extending our algorithm to linear chromosomes. Finally, we have implemented our algorithm and applied it to the circular genomic sequences of three human vibrio pathogens for predicting their evolutionary relationships. Consequently, our experimental results coincide with the previous ones obtained by others using a different comparative genomics approach, which implies that the block-interchange events seem to play a significant role in the evolution of vibrio species.
Ferrauto, Tomassino; Parisi, Domenico; Di Stefano, Gabriele; Baldassarre, Gianluca
2013-01-01
Organisms that live in groups, from microbial symbionts to social insects and schooling fish, exhibit a number of highly efficient cooperative behaviors, often based on role taking and specialization. These behaviors are relevant not only for the biologist but also for the engineer interested in decentralized collective robotics. We address these phenomena by carrying out experiments with groups of two simulated robots controlled by neural networks whose connection weights are evolved by using genetic algorithms. These algorithms and controllers are well suited to autonomously find solutions for decentralized collective robotic tasks based on principles of self-organization. The article first presents a taxonomy of role-taking and specialization mechanisms related to evolved neural network controllers. Then it introduces two cooperation tasks, which can be accomplished by either role taking or specialization, and uses these tasks to compare four different genetic algorithms to evaluate their capacity to evolve a suitable behavioral strategy, which depends on the task demands. Interestingly, only one of the four algorithms, which appears to have more biological plausibility, is capable of evolving role taking or specialization when they are needed. The results are relevant for both collective robotics and biology, as they can provide useful hints on the different processes that can lead to the emergence of specialization in robots and organisms.
Kiesewetter, Simon; Drummond, Peter D.
2017-03-01
A variance reduction method for stochastic integration of Fokker-Planck equations is derived. This unifies the cumulant hierarchy and stochastic equation approaches to obtaining moments, giving a performance superior to either. We show that the brute force method of reducing sampling error by just using more trajectories in a sampled stochastic equation is not the best approach. The alternative of using a hierarchy of moment equations is also not optimal, as it may converge to erroneous answers. Instead, through Bayesian conditioning of the stochastic noise on the requirement that moment equations are satisfied, we obtain improved results with reduced sampling errors for a given number of stochastic trajectories. The method used here converges faster in time-step than Ito-Euler algorithms. This parallel optimized sampling (POS) algorithm is illustrated by several examples, including a bistable nonlinear oscillator case where moment hierarchies fail to converge.
Engwerda, Jacob
2015-01-01
This note deals with solving scalar coupled algebraic Riccati equations. These equations arise in finding linear feedback Nash equilibria of the scalar N-player affine quadratic differential game. A numerical procedure is provided to compute all the stabilizing solutions. The main idea is to reformu
Evolution of Ideas: A Novel Memetic Algorithm Based on Semantic Networks
Baydin, Atilim Gunes
2012-01-01
This paper presents a new type of evolutionary algorithm (EA) based on the concept of "meme", where the individuals forming the population are represented by semantic networks and the fitness measure is defined as a function of the represented knowledge. Our work can be classified as a novel memetic algorithm (MA), given that (1) it is the units of culture, or information, that are undergoing variation, transmission, and selection, very close to the original sense of memetics as it was introduced by Dawkins; and (2) this is different from existing MA, where the idea of memetics has been utilized as a means of local refinement by individual learning after classical global sampling of EA. The individual pieces of information are represented as simple semantic networks that are directed graphs of concepts and binary relations, going through variation by memetic versions of operators such as crossover and mutation, which utilize knowledge from commonsense knowledge bases. In evaluating this introductory work, as ...
Kim, Chang-Goo; Ostriker, Eve C.
2017-09-01
We introduce TIGRESS, a novel framework for multi-physics numerical simulations of the star-forming interstellar medium (ISM) implemented in the Athena MHD code. The algorithms of TIGRESS are designed to spatially and temporally resolve key physical features, including: (1) the gravitational collapse and ongoing accretion of gas that leads to star formation in clusters; (2) the explosions of supernovae (SNe), both near their progenitor birth sites and from runaway OB stars, with time delays relative to star formation determined by population synthesis; (3) explicit evolution of SN remnants prior to the onset of cooling, which leads to the creation of the hot ISM; (4) photoelectric heating of the warm and cold phases of the ISM that tracks the time-dependent ambient FUV field from the young cluster population; (5) large-scale galactic differential rotation, which leads to epicyclic motion and shears out overdense structures, limiting large-scale gravitational collapse; (6) accurate evolution of magnetic fields, which can be important for vertical support of the ISM disk as well as angular momentum transport. We present tests of the newly implemented physics modules, and demonstrate application of TIGRESS in a fiducial model representing the solar neighborhood environment. We use a resolution study to demonstrate convergence and evaluate the minimum resolution {{Δ }}x required to correctly recover several ISM properties, including the star formation rate, wind mass-loss rate, disk scale height, turbulent and Alfvénic velocity dispersions, and volume fractions of warm and hot phases. For the solar neighborhood model, all these ISM properties are converged at {{Δ }}x≤slant 8 {pc}.
Ren Ziwu
2016-04-01
Full Text Available A humanoid manipulator produces significantly reactive forces against a humanoid body when it operates in a rapid and continuous reaction environment (e.g., playing baseball, ping-pong etc.. This not only disturbs the balance and stability of the humanoid robot, but also influences its operation precision. To solve this problem, a novel approach, which is able to generate a minimum-acceleration and continuous acceleration trajectory for the humanoid manipulator, is presented in this paper. By this method, the whole trajectory of humanoid manipulation is divided into two processes, i.e., the operation process and the return process. Moreover, the target operation point is considered as a particular point that should be passed through. As such, the trajectory of each process is described through a quartic polynomial in the joint space, after which the trajectory planning problem for the humanoid manipulator can be formulated as a global constrained optimization problem. In order to alleviate the reactive force, a fitness function that aims to minimize the maximum acceleration of each joint of the manipulator is defined, while differential evolution is employed to determine the joint accelerations of the target operation point. Thus, a trajectory with a minimum-acceleration and continuous acceleration profile is obtained, which can reduce the effect on the body and be favourable for the balance and stability of the humanoid robot to a certain extent. Finally, a humanoid robot with a 7-DOF manipulator for ping-pong playing is employed as an example. Simulation experiment results show the effectiveness of this method for the trajectory planning problem being studied.
M.M. Khader
2015-01-01
Full Text Available In this paper, two efficient numerical methods for solving system of fractional differential equations (SFDEs are considered. The fractional derivative is described in the Caputo sense. The first method is based upon Chebyshev approximations, where the properties of Chebyshev polynomials are utilized to reduce SFDEs to system of algebraic equations. Special attention is given to study the convergence and estimate the error of the presented method. The second method is the fractional finite difference method (FDM, where we implement the Grünwald–Letnikov’s approach. We study the stability of the obtained numerical scheme. The numerical results show that the approaches are easy to implement implement for solving SFDEs. The methods introduce a promising tool for solving many systems of linear and non-linear fractional differential equations. Numerical examples are presented to illustrate the validity and the great potential of both proposed techniques.
Samira Abdi
2012-11-01
The proposed algorithm (MOBBO/DE makes the use of nondominated sorting approach improve the convergence ability efficiently and hence it can generate the promising candidate solutions. It also combines crowding distance to guarantee the diversity of Pareto optimal solutions. The proposed approach is validated using several test functions and some metrics taken from the standard literature on evolutionary multi-objective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.
Genetic algorithm evolution of utility bidding strategies for the competitive marketplace
Richter, C.W. Jr.; Sheble, G.B. [Iowa State Univ., Ames, IA (United States)
1998-02-01
This paper describes an environment in which distribution companies (discos) and generation companies (gencos), buy and sell power via double auctions implemented in a regional commodity exchange. The electric utilities` profits depend on the implementation of a successful bidding strategy. In this research, a genetic algorithm evolves bidding strategies as gencos and discos trade power. A framework in which bidding strategies may be tested and modified is presented. This simulated electric commodity exchange can be used off-line to predict whether bid strategies will be profitable and successful. It can also be used to experimentally verify how bidding behavior affects the competitive electric marketplace.
FSM State-Encoding for Area and Power Minimization Using Simulated Evolution Algorithm
2012-01-01
In this paper we describe the engineering of a non-deterministic iterative heuristic [1] known as simulated evolution(SimE) to solve the well-known NP-hard state assignment problem (SAP). Each assignment of a code to a state isgiven a Goodness value derived from a matrix representation of the desired adjacency graph (DAG) proposed byAmaral et.al [2]. We use the (DAGa) proposed in previous studies to optimize the area, and propose a new DAGpand employ it to reduce the power dissipation. In the...
Zgurovsky, Mikhail Z; Kasyanov, Pavlo O
2011-01-01
Here, the authors present modern mathematical methods to solve problems of differential-operator inclusions and evolution variation inequalities which may occur in fields such as geophysics, aerohydrodynamics, or fluid dynamics. For the first time, they describe the detailed generalization of various approaches to the analysis of fundamentally nonlinear models and provide a toolbox of mathematical equations. These new mathematical methods can be applied to a broad spectrum of problems. Examples of these are phase changes, diffusion of electromagnetic, acoustic, vibro-, hydro- and seismoacousti
Xu, Feng; Dong, Bo; Hu, Xiaofang; Xiao, Yu; Wang, Yang
2017-09-01
A new sparse tomography method for observing the rapid internal microstructure evolution of material, called the Algebraic Filtered-Back-Projection and Total Variation Minimization (AFBP-TVM) iteration sparse reconstruction algorithm, was proposed in this paper. The new algorithm was developed by combining the two techniques of the Algebraic Reconstruction Technique (ART) and the Filtered-Back-Projection (FBP) on the basis of analysis in linear space. A series of numerical reconstruction experiments were conducted to validate the new algorithm. The results indicated the new algorithm can obtain satisfactory reconstruction images from 1/6 of the projections that were used in traditional algorithms. So the time spent on projection acquisition process can be reduced to 1/6 of that in traditional tomography method. The quality of images reconstructed by new algorithm was better than other algorithms, which was evaluated by three quantitative parameters. The normalized average absolute distance criterion and the normalized mean square criterion, which were used to evaluate the relative error of the reconstruction results (smaller value means better quality of reconstruction), decreased from 0.3758 to 0.1272 and from 0.1832 to 0.0894 respectively. The standardized covariance criterion, which was used to evaluate the similarity level (greater value means higher accuracy of reconstruction), increased from 92.72% to 99.30%. Finally, the new algorithm was validated under actual experimental conditions. The results indicated that the AFBP-TVM algorithm obtained better reconstruction quality than other algorithms. It meant that the AFBP-TVM algorithm may be a suitable method for in situ investigation on material's rapid internal microstructure evolution in extreme complex environment.
Density functional theory and evolution algorithm calculations of elastic properties of AlON
Batyrev, I. G.; Taylor, D. E.; Gazonas, G. A.; McCauley, J. W. [U.S. Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005 (United States)
2014-01-14
Different models for aluminum oxynitride (AlON) were calculated using density functional theory and optimized using an evolutionary algorithm. Evolutionary algorithm and density functional theory (DFT) calculations starting from several models of AlON with different Al or O vacancy locations and different positions for the N atoms relative to the vacancy were carried out. The results show that the constant anion model [McCauley et al., J. Eur. Ceram. Soc. 29(2), 223 (2009)] with a random distribution of N atoms not adjacent to the Al vacancy has the lowest energy configuration. The lowest energy structure is in a reasonable agreement with experimental X-ray diffraction spectra. The optimized structure of a 55 atom unit cell was used to construct 220 and 440 atom models for simulation cells using DFT with a Gaussian basis set. Cubic elastic constant predictions were found to approach the experimentally determined AlON single crystal elastic constants as the model size increased from 55 to 440 atoms. The pressure dependence of the elastic constants found from simulated stress-strain relations were in overall agreement with experimental measurements of polycrystalline and single crystal AlON. Calculated IR intensity and Raman spectra are compared with available experimental data.
Eldin, A.A. Hossam; Refaey, M.A. [Electrical Engineering Department, Alexandria University, Alexandria (Egypt)
2011-01-15
This paper proposes a novel methodology for transformer differential protection, based on wave shape recognition of the discriminating criterion extracted of the instantaneous differential currents. Discrete wavelet transform has been applied to the differential currents due to internal fault and inrush currents. The diagnosis criterion is based on median absolute deviation (MAD) of wavelet coefficients over a specified frequency band. The proposed algorithm is examined using various simulated inrush and internal fault current cases on a power transformer that has been modeled using electromagnetic transients program EMTDC software. Results of evaluation study show that, proposed wavelet based differential protection scheme can discriminate internal faults from inrush currents. (author)
Horiuchi, Youko
2015-12-23
Background Since the development of transcriptome analysis systems, many expression evolution studies characterized evolutionary forces acting on gene expression, without explicit discrimination between global expression differences and tissue specific expression differences. However, different types of gene expression alteration should have different effects on an organism, the evolutionary forces that act on them might be different, and different types of genes might show different types of differential expression between species. To confirm this, we studied differentially expressed (DE) genes among closely related groups that have extensive gene expression atlases, and clarified characteristics of different types of DE genes including the identification of regulating loci for differential expression using expression quantitative loci (eQTL) analysis data. Results We detected differentially expressed (DE) genes between rice subspecies in five homologous tissues that were verified using japonica and indica transcriptome atlases in public databases. Using the transcriptome atlases, we classified DE genes into two types, global DE genes and changed-tissues DE genes. Global type DE genes were not expressed in any tissues in the atlas of one subspecies, however changed-tissues type DE genes were expressed in both subspecies with different tissue specificity. For the five tissues in the two japonica-indica combinations, 4.6 ± 0.8 and 5.9 ± 1.5 % of highly expressed genes were global and changed-tissues DE genes, respectively. Changed-tissues DE genes varied in number between tissues, increasing linearly with the abundance of tissue specifically expressed genes in the tissue. Molecular evolution of global DE genes was rapid, unlike that of changed-tissues DE genes. Based on gene ontology, global and changed-tissues DE genes were different, having no common GO terms. Expression differences of most global DE genes were regulated by cis-eQTLs. Expression
Performance comparison of several optimization algorithms in matched field inversion
ZOU Shixin; YANG Kunde; MA Yuanliang
2004-01-01
Optimization efficiencies and mechanisms of simulated annealing, genetic algorithm, differential evolution and downhill simplex differential evolution are compared and analyzed. Simulated annealing and genetic algorithm use a directed random process to search the parameter space for an optimal solution. They include the ability to avoid local minima, but as no gradient information is used, searches may be relatively inefficient. Differential evolution uses information from a distance and azimuth between individuals of a population to search the parameter space, the initial search is effective, but the search speed decreases quickly because differential information between the individuals of population vanishes. Local downhill simplex and global differential evolution methods are developed separately, and combined to produce a hybrid downhill simplex differential evolution algorithm. The hybrid algorithm is sensitive to gradients of the object function and search of the parameter space is effective. These algorithms are applied to the matched field inversion with synthetic data. Optimal values of the parameters, the final values of object function and inversion time is presented and compared.
Elliptical Antenna Array Synthesis Using Backtracking Search Optimisation Algorithm
Kerim Guney
2016-04-01
Full Text Available The design of the elliptical antenna arrays is relatively new research area in the antenna array community. Backtracking search optimisation algorithm (BSA is employed for the synthesis of elliptical antenna arrays having different number of array elements. For this aim, BSA is used to calculate the optimum angular position and amplitude values of the array elements. BSA is a population-based iterative evolutionary algorithm. The remarkable properties of BSA are that it has a good optimisation performance, simple implementation structure, and few control parameters. The results of BSA are compared with those of self-adaptive differential evolution algorithm, firefly algorithm, biogeography based optimisation algorithm, and genetic algorithm. The results show that BSA can reach better solutions than the compared optimisation algorithms. Iterative performances of BSA are also compared with those of bacterial foraging algorithm and differential search algorithm.
Wright, Thomas; Ward, Jamie
2013-08-01
Sensory substitution is a promising technique for mitigating the loss of a sensory modality. Sensory substitution devices (SSDs) work by converting information from the impaired sense (e.g., vision) into another, intact sense (e.g., audition). However, there are a potentially infinite number of ways of converting images into sounds, and it is important that the conversion takes into account the limits of human perception and other user-related factors (e.g., whether the sounds are pleasant to listen to). The device explored here is termed "polyglot" because it generates a very large set of solutions. Specifically, we adapt a procedure that has been in widespread use in the design of technology but has rarely been used as a tool to explore perception-namely, interactive genetic algorithms. In this procedure, a very large range of potential sensory substitution devices can be explored by creating a set of "genes" with different allelic variants (e.g., different ways of translating luminance into loudness). The most successful devices are then "bred" together, and we statistically explore the characteristics of the selected-for traits after multiple generations. The aim of the present study is to produce design guidelines for a better SSD. In three experiments, we vary the way that the fitness of the device is computed: by asking the user to rate the auditory aesthetics of different devices (Experiment 1), and by measuring the ability of participants to match sounds to images (Experiment 2) and the ability to perceptually discriminate between two sounds derived from similar images (Experiment 3). In each case, the traits selected for by the genetic algorithm represent the ideal SSD for that task. Taken together, these traits can guide the design of a better SSD.
Enhanced Algorithm for Differentiated Service Based on Probability%基于概率的增强型区分服务算法
特日根; 孙永雄; 李雄飞; 张月莹
2011-01-01
For fair shortcomings of current existing algorithms of the Proportional Delay Differentiated(PDD) services model in the relative differentiated, the enhanced algorithm for differentiated service based on probability(WPPLQ) for the mobile location services platform is proposed. This algorithm in calculating the waiting time is optimized to make it more accurate, and by calculating the packet size to determine the service response time, so that the scheduling algorithm is more fair. By the NS-2 simulation platform, the algorithm is simulated and tested. It is verified that the PLQ algorithm and the WPPLQ algorithm are in line with performance requirements which grading the quality of service and feasibilities of differentiated service in PDD model. And it show that WPPLQ algorithm has higher fairness.%相对区分服务中成比例延迟区分(PDD)服务模型算法的公平性不高.为此,提出一种基于概率的增强型区分服务算法WPPLQ.该算法对等待时间的计算进行优化,利用数据包大小确定服务响应时间,适用于移动定位服务平台.在NS-2模拟器上的仿真结果表明,PLQ和WPPLQ均符合PDD区分服务模型的性能要求,在高带宽情况下,WPPLQ具有更高的公平性.
F. F. Ngwane
2015-01-01
Full Text Available We propose a block hybrid trigonometrically fitted (BHT method, whose coefficients are functions of the frequency and the step-size for directly solving general second-order initial value problems (IVPs, including systems arising from the semidiscretization of hyperbolic Partial Differential Equations (PDEs, such as the Telegraph equation. The BHT is formulated from eight discrete hybrid formulas which are provided by a continuous two-step hybrid trigonometrically fitted method with two off-grid points. The BHT is implemented in a block-by-block fashion; in this way, the method does not suffer from the disadvantages of requiring starting values and predictors which are inherent in predictor-corrector methods. The stability property of the BHT is discussed and the performance of the method is demonstrated on some numerical examples to show accuracy and efficiency advantages.
An algorithm for computing thick target differential p-Li neutron yields near threshold
Lee, C. L.; Zhou, X.-L.
1999-06-01
The 7Li(p,n)7Be reaction is a good source of neutrons for accelerator boron neutron capture therapy (BNCT). Both reactor and accelerator neutron sources produce fast neutrons, which must be moderated since BNCT uses epithermal neutrons. Near-threshold BNCT uses proton energies only tens of keV above the reaction threshold, which reduces the thick target neutron yield but also produces neutrons closer to epithermal energies, so that less moderation is required. Accurate methods for calculating near-threshold differential neutron yields from thick targets of lithium, as well as certain low weight lithium compounds, were developed for BNCT source design. Neutron yields for proton beams up to 2.8 MeV will be presented. Good agreement with yields from several targets will be demonstrated.
Fang-Rong Yan
2014-01-01
Full Text Available Population pharmacokinetic (PPK models play a pivotal role in quantitative pharmacology study, which are classically analyzed by nonlinear mixed-effects models based on ordinary differential equations. This paper describes the implementation of SDEs in population pharmacokinetic models, where parameters are estimated by a novel approximation of likelihood function. This approximation is constructed by combining the MCMC method used in nonlinear mixed-effects modeling with the extended Kalman filter used in SDE models. The analysis and simulation results show that the performance of the approximation of likelihood function for mixed-effects SDEs model and analysis of population pharmacokinetic data is reliable. The results suggest that the proposed method is feasible for the analysis of population pharmacokinetic data.
Learning Qualitative Differential Equation models: a survey of algorithms and applications.
Pang, Wei; Coghill, George M
2010-03-01
Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives.
宋丽娜; 王维国
2012-01-01
By constructing the iterative formula with a so-called convergence-control parameter, the generalized two-dimensional differential transform method is improved. With the enhanced technique, the nonlinear fractional Kolmogorov-Petrovskii-Piskunov equations are dealt analytically and approximate solutions are derived. The results show that the employed approach is a promising tool for solving many nonlinear fractional partial differential equations. The algorithm described in this work is expected to be employed to solve more problems in fractional calculus.
Song, Li-Na; Wang, Wei-Guo
2012-08-01
By constructing the iterative formula with a so-called convergence-control parameter, the generalized two-dimensional differential transform method is improved. With the enhanced technique, the nonlinear fractional Kolmogorov-Petrovskii-Piskunov equations are dealt analytically and approximate solutions are derived. The results show that the employed approach is a promising tool for solving many nonlinear fractional partial differential equations. The algorithm described in this work is expected to be employed to solve more problems in fractional calculus.
PCA algorithm for detection, localisation and evolution of damages in gearbox bearings
Pirra, M; Gandino, E; Garibaldi, L; Machorro-Lopez, J M [Dipartimento di Meccanica, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino (Italy); Torri, A, E-mail: luigi.garibaldi@polito.it [Avio S.p.A., Strada del Drosso 145, 10135 Torino (Italy)
2011-07-19
A fundamental aspect when dealing with rolling element bearings, which often represent a key component in rotating machineries, consists in correctly identifying a degraded behaviour of a bearing with a reasonable level of confidence. This is one of the main requirements a health and usage monitoring system (HUMS) should have. This paper introduces a monitoring technique for the diagnosis of bearing faults based on Principal Component Analysis (PCA). This method overcomes the problem of acquiring data under different environmental conditions (hardly biasing the data) and allows accurate damage recognition, also assuring a rather low number of False Alarms (FA). In addition, a novel criterion is proposed in order to isolate the area in which the faulty bearing stands. Another useful feature of this PCA-based method concerns the capability to observe an increasing trend in the evolution of bearing degradation. The described technique is tested on an industrial rig (designed by Avio S.p.A.), consisting of a full size aeroengine gearbox. Healthy and variously damaged bearings, such as with an inner or rolling element fault, are set up and vibration signals are collected and processed in order to properly detect a fault. Finally, data collected from a test rig assembled by the Dynamics and Identification Research Group (DIRG) are used to demonstrate that the proposed method is able to correctly detect and to classify different levels of the same type of fault and also to localise it.
E. G. Mendelevich
2014-01-01
Full Text Available As of now, somatization of disorders when its physical symptoms may be caused by mental, psychological, or emotional factors is an extremely common event in clinical practice. The somatization is considered to mean the conversion of mental stress to somatic symptoms. The mechanisms and specific features of somatization disorders within generalized anxiety disorder and panic disorder are of interest. One of the theories about the causes of somatization disorders proposes that somatization is as a way of avoiding psychological stress: it serves to protect from psychological pain, which allows the label of a psychiatric diagnosis to be circumvented in the majority of cases. Some patients unconsciously substitute theexperienced symptoms of anxiety or depression for the development of physical symptoms; anxiety disorders are one of the most common mental disorders. Among various forms of anxiety disorders, panic attacks and generalized anxiety disorders are most often met with neurologists. In some patients, the clinical picture is characterized by a concurrence of both the permanent and paroxysmal symptoms of anxiety, which is not only important to analyze the individual course of the disease, but also responsible for the addressness of a therapeutic approach to a greater extent.There are data of clinical trials and current algorithms for the therapy of generalized anxiety and related disorders reflecting the complexity of a therapeutic approach. The trials of the therapeutic effectiveness of these or those psychopharmacological agents demonstrate the importance of different groups of drugs in relation to the clinical features of detectable disorders. A number of trials indicate that the use of benzodiazepines (such as phenazepam may be considered irreplaceable as an emergency care on an exacerbation of anxiety and exceptional therapeutic orientation by using selective antidepressants not always can solve diverse clinical problems.
Beaulieu, J. P.; Sasselov, D. D.
1996-01-01
Abstract: We present a differential study of 500 Magellanic Cepheids with 3 million measurements obtained as a by-product of the EROS microlensing survey. The data-set is unbiased and provides an excellent basis for a differential analysis between LMC and SMC. We investigate the pulsational properti
Li, Jing; Hong, Wenxue
2014-12-01
The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.
Sengupta, Partho P; Huang, Yen-Min; Bansal, Manish; Ashrafi, Ali; Fisher, Matt; Shameer, Khader; Gall, Walt; Dudley, Joel T
2016-06-01
Associating a patient's profile with the memories of prototypical patients built through previous repeat clinical experience is a key process in clinical judgment. We hypothesized that a similar process using a cognitive computing tool would be well suited for learning and recalling multidimensional attributes of speckle tracking echocardiography data sets derived from patients with known constrictive pericarditis and restrictive cardiomyopathy. Clinical and echocardiographic data of 50 patients with constrictive pericarditis and 44 with restrictive cardiomyopathy were used for developing an associative memory classifier-based machine-learning algorithm. The speckle tracking echocardiography data were normalized in reference to 47 controls with no structural heart disease, and the diagnostic area under the receiver operating characteristic curve of the associative memory classifier was evaluated for differentiating constrictive pericarditis from restrictive cardiomyopathy. Using only speckle tracking echocardiography variables, associative memory classifier achieved a diagnostic area under the curve of 89.2%, which improved to 96.2% with addition of 4 echocardiographic variables. In comparison, the area under the curve of early diastolic mitral annular velocity and left ventricular longitudinal strain were 82.1% and 63.7%, respectively. Furthermore, the associative memory classifier demonstrated greater accuracy and shorter learning curves than other machine-learning approaches, with accuracy asymptotically approaching 90% after a training fraction of 0.3 and remaining flat at higher training fractions. This study demonstrates feasibility of a cognitive machine-learning approach for learning and recalling patterns observed during echocardiographic evaluations. Incorporation of machine-learning algorithms in cardiac imaging may aid standardized assessments and support the quality of interpretations, particularly for novice readers with limited experience. © 2016
Riediger Michael L. B.
2005-01-01
Full Text Available In this paper, we consider the issue of blind detection of Alamouti-type differential space-time (ST modulation in static Rayleigh fading channels. We focus our attention on a π / 2 -shifted BPSK constellation, introducing a novel transformation to the received signal such that this binary ST modulation, which has a second-order transmit diversity, is equivalent to QPSK modulation with second-order receive diversity. This equivalent representation allows us to apply a low-complexity detection technique specifically designed for receive diversity, namely, scalar multiple-symbol differential detection (MSDD. To further increase receiver performance, we apply an iterative expectation-maximization (EM algorithm which performs joint channel estimation and sequence detection. This algorithm uses minimum mean square estimation to obtain channel estimates and the maximum-likelihood principle to detect the transmitted sequence, followed by differential decoding. With receiver complexity proportional to the observation window length, our receiver can achieve the performance of a coherent maximal ratio combining receiver (with differential decoding in as few as a single EM receiver iteration, provided that the window size of the initial MSDD is sufficiently long. To further demonstrate that the MSDD is a vital part of this receiver setup, we show that an initial ST conventional differential detector would lead to strange convergence behavior in the EM algorithm.
M Ann Mongan
Full Text Available Genome-wide gene expression profiling has become standard for assessing potential liabilities as well as for elucidating mechanisms of toxicity of drug candidates under development. Analysis of microarray data is often challenging due to the lack of a statistical model that is amenable to biological variation in a small number of samples. Here we present a novel non-parametric algorithm that requires minimal assumptions about the data distribution. Our method for determining differential expression consists of two steps: 1 We apply a nominal threshold on fold change and platform p-value to designate whether a gene is differentially expressed in each treated and control sample relative to the averaged control pool, and 2 We compared the number of samples satisfying criteria in step 1 between the treated and control groups to estimate the statistical significance based on a null distribution established by sample permutations. The method captures group effect without being too sensitive to anomalies as it allows tolerance for potential non-responders in the treatment group and outliers in the control group. Performance and results of this method were compared with the Significant Analysis of Microarrays (SAM method. These two methods were applied to investigate hepatic transcriptional responses of wild-type (PXR(+/+ and pregnane X receptor-knockout (PXR(-/- mice after 96 h exposure to CMP013, an inhibitor of β-secretase (β-site of amyloid precursor protein cleaving enzyme 1 or BACE1. Our results showed that CMP013 led to transcriptional changes in hallmark PXR-regulated genes and induced a cascade of gene expression changes that explained the hepatomegaly observed only in PXR(+/+ animals. Comparison of concordant expression changes between PXR(+/+ and PXR(-/- mice also suggested a PXR-independent association between CMP013 and perturbations to cellular stress, lipid metabolism, and biliary transport.
Fei Gao
2013-01-01
Full Text Available In this paper, a non-Lyapunov novel approach is proposed to estimate the unknown parameters and orders together for noncommensurate and hyper fractional chaotic systems based on cuckoo search oriented statistically by the differential evolution (CSODE. Firstly, a novel Gaos’ mathematical model is proposed and analyzed in three submodels, not only for the unknown orders and parameters’ identification but also for systems’ reconstruction of fractional chaos systems with time delays or not. Then the problems of fractional-order chaos’ identification are converted into a multiple modal nonnegative functions’ minimization through a proper translation, which takes fractional-orders and parameters as its particular independent variables. And the objective is to find the best combinations of fractional-orders and systematic parameters of fractional order chaotic systems as special independent variables such that the objective function is minimized. Simulations are done to estimate a series of noncommensurate and hyper fractional chaotic systems with the new approaches based on CSODE, the cuckoo search, and Genetic Algorithm, respectively. The experiments’ results show that the proposed identification mechanism based on CSODE for fractional orders and parameters is a successful method for fractional-order chaotic systems, with the advantages of high precision and robustness.
How to Speed up Optimization? Opposite-Center Learning and Its Application to Differential Evolution
Xu, H.; Erdbrink, C.D.; Krzhizhanovskaya, V.V.
2015-01-01
This paper introduces a new sampling technique called Opposite-Center Learning (OCL) intended for convergence speed-up of meta-heuristic optimization algorithms. It comprises an extension of Opposition-Based Learning (OBL), a simple scheme that manages to boost numerous optimization methods by consi
Zhang, Huifeng; Lei, Xiaohui; Wang, Chao; Yue, Dong; Xie, Xiangpeng
2017-01-01
Since wind power is integrated into the thermal power operation system, dynamic economic emission dispatch (DEED) has become a new challenge due to its uncertain characteristics. This paper proposes an adaptive grid based multi-objective Cauchy differential evolution (AGB-MOCDE) for solving stochastic DEED with wind power uncertainty. To properly deal with wind power uncertainty, some scenarios are generated to simulate those possible situations by dividing the uncertainty domain into different intervals, the probability of each interval can be calculated using the cumulative distribution function, and a stochastic DEED model can be formulated under different scenarios. For enhancing the optimization efficiency, Cauchy mutation operation is utilized to improve differential evolution by adjusting the population diversity during the population evolution process, and an adaptive grid is constructed for retaining diversity distribution of Pareto front. With consideration of large number of generated scenarios, the reduction mechanism is carried out to decrease the scenarios number with covariance relationships, which can greatly decrease the computational complexity. Moreover, the constraint-handling technique is also utilized to deal with the system load balance while considering transmission loss among thermal units and wind farms, all the constraint limits can be satisfied under the permitted accuracy. After the proposed method is simulated on three test systems, the obtained results reveal that in comparison with other alternatives, the proposed AGB-MOCDE can optimize the DEED problem while handling all constraint limits, and the optimal scheme of stochastic DEED can decrease the conservation of interval optimization, which can provide a more valuable optimal scheme for real-world applications.
模拟细菌菌落进化过程的群体智能算法%New Swarm Intelligence Algorithm Simulating Evolution Process of Bacterial Colony
李明
2013-01-01
针对常规群体智能算法缺乏进化能力,存在易于陷入早熟收敛的不足,将问题的解空间视为细菌培养液,在其中放置单个或少量细菌个体,模拟细菌菌落的生长进化过程,提出一种新的群体智能算法.该算法本身具有进化机制,并且能够自然结束,从而为优化算法提出了一种新的结束准则.通过仿真实验验证了算法的有效性,同时仿真实验结果还表明通过简单改进,算法可以达到全局最优.%Traditional swarm intelligence algorithms lack of evolution ability and are easy to fall into premature convergence. Therefore, a new kind of swarm intelligence algorithm, called bacterial colony optimization (BCO) algorithm, was proposed. The solution space of the problem was considered as a certain culture medium. A single bacterium or a few bacteria were placed randomly in the space. The BCO algorithm was designed through simulating the evolution process of the bacterial colony. The BCO itself has a certain evolutionary mechanism and could be terminated naturally, which has given a new termination criterion for swarm intelligence algorithms. A series of simulation experiments on three test functions verify the effectiveness of the BCO algorithm. The simulation results show that the BCO algorithm can converge to the global optimization solution.
Differential evolution of MAGE genes based on expression pattern and selection pressure.
Qi Zhao
Full Text Available Starting from publicly-accessible datasets, we have utilized comparative and phylogenetic genome analyses to characterize the evolution of the human MAGE gene family. Our characterization of genomic structures in representative genomes of primates, rodents, carnivora, and macroscelidea indicates that both Type I and Type II MAGE genes have undergone lineage-specific evolution. The restricted expression pattern in germ cells of Type I MAGE orthologs is observed throughout evolutionary history. Unlike Type II MAGEs that have conserved promoter sequences, Type I MAGEs lack promoter conservation, suggesting that epigenetic regulation is a central mechanism for controlling their expression. Codon analysis shows that Type I but not Type II MAGE genes have been under positive selection. The combination of genomic and expression analysis suggests that Type 1 MAGE promoters and genes continue to evolve in the hominin lineage, perhaps towards functional diversification or acquiring additional specific functions, and that selection pressure at codon level is associated with expression spectrum.
A new approach to investigation of evolution differential equations in Banach spaces
Alber, Y I
1993-01-01
and that $B$ is dense in $H$. The stabilization of solutions of evolution equations has been proven either in the sense of weak convergence in $B$ or in the norm of $H$ space, and only asymptotic estimates of stabilization rate have been obtained [15]. In the present paper we consider equations of type (0.1) without conditions (0.2) and establish stabilization with both
Differential stepwise evolution of SARS coronavirus functional proteins in different host species
Tang Xianchun
2009-03-01
Full Text Available Abstract Background SARS coronavirus (SARS-CoV was identified as the etiological agent of SARS, and extensive investigations indicated that it originated from an animal source (probably bats and was recently introduced into the human population via wildlife animals from wet markets in southern China. Previous studies revealed that the spike (S protein of SARS had experienced adaptive evolution, but whether other functional proteins of SARS have undergone adaptive evolution is not known. Results We employed several methods to investigate selective pressure among different SARS-CoV groups representing different epidemic periods and hosts. Our results suggest that most functional proteins of SARS-CoV have experienced a stepwise adaptive evolutionary pathway. Similar to previous studies, the spike protein underwent strong positive selection in the early and middle phases, and became stabilized in the late phase. In addition, the replicase experienced positive selection only in human patients, whereas assembly proteins experienced positive selection mainly in the middle and late phases. No positive selection was found in any proteins of bat SARS-like-CoV. Furthermore, specific amino acid sites that may be the targets of positive selection in each group are identified. Conclusion This extensive evolutionary analysis revealed the stepwise evolution of different functional proteins of SARS-CoVs at different epidemic stages and different hosts. These results support the hypothesis that SARS-CoV originated from bats and that the spill over into civets and humans were more recent events.
Frederickson, P. O.; Wessel, W. R.
1979-01-01
Certain physical processes are modeled by partial differential equations which are parabolic over part of the domain and elliptic over the remainder. A family of semi-implicit algorithms which are well suited to initial-boundary value problems of this mixed type is discussed. One important feature of these algorithms is the use of an approximate inverse for the solution of the implicit linear system. A strong error analysis results in an estimate of the total error as a function of approximate inverse error e and time step h.
基于知识进化算法的读者满意度评价%Evaluation of the Reader's Satisfaction Based on the Knowledge Evolution Algorithm
吴凤娟
2011-01-01
Trough combining evolutionary epistemology theory with biological evolution theory, this paper presents a knowledge evolution algorithm. The basic principle and realizing ways is also given. The evaluating function of the knowledge is created too. When the algorithm is used in the evaluation of the reader's satisfaction in a library, we can obtain successful experiment results. The experiment shows that the knowledge evolution algorithm can promote both the quantity and the quality of the knowledge, which is an effective assistant means to make a correct decision.%通过结合知识进化论与生物进化论思想,提出了知识进化算法,给出该算法的基本原理和实现途径,并创建了知识的评价函数.把该算法用于图书馆读者满意度评价实例中,可获得成功的试验结果.这表明知识进化算法可促进知识的量与质的提升,为正确决策提供有效的辅助手段.
1995-01-01
John Locke offered what he considered a sound a priori argument that Mind must come first, must be the original Cause, not merely an Effect: If, then, there must be something eternal, let us see what sort of Being it must be. And to that it is very obvious to Reason, that it must necessarily be a cogitative Being. For it is as impossible to conceive that ever bare incogitative Matter should produce a thinking intelligent Being, as that nothing should of itself produce Matter. Let us suppose a...
Wang, L.; Wang, T. G.; Wu, J. H.; Cheng, G. P.
2016-09-01
A novel multi-objective optimization algorithm incorporating evolution strategies and vector mechanisms, referred as VD-MOEA, is proposed and applied in aerodynamic- structural integrated design of wind turbine blade. In the algorithm, a set of uniformly distributed vectors is constructed to guide population in moving forward to the Pareto front rapidly and maintain population diversity with high efficiency. For example, two- and three- objective designs of 1.5MW wind turbine blade are subsequently carried out for the optimization objectives of maximum annual energy production, minimum blade mass, and minimum extreme root thrust. The results show that the Pareto optimal solutions can be obtained in one single simulation run and uniformly distributed in the objective space, maximally maintaining the population diversity. In comparison to conventional evolution algorithms, VD-MOEA displays dramatic improvement of algorithm performance in both convergence and diversity preservation for handling complex problems of multi-variables, multi-objectives and multi-constraints. This provides a reliable high-performance optimization approach for the aerodynamic-structural integrated design of wind turbine blade.
Cheng, Mulin, E-mail: mulinch@caltech.edu; Hou, Thomas Y., E-mail: hou@cms.caltech.edu; Zhang, Zhiwen, E-mail: zhangzw@caltech.edu
2013-06-01
We propose a dynamically bi-orthogonal method (DyBO) to solve time dependent stochastic partial differential equations (SPDEs). The objective of our method is to exploit some intrinsic sparse structure in the stochastic solution by constructing the sparsest representation of the stochastic solution via a bi-orthogonal basis. It is well-known that the Karhunen–Loeve expansion (KLE) minimizes the total mean squared error and gives the sparsest representation of stochastic solutions. However, the computation of the KL expansion could be quite expensive since we need to form a covariance matrix and solve a large-scale eigenvalue problem. The main contribution of this paper is that we derive an equivalent system that governs the evolution of the spatial and stochastic basis in the KL expansion. Unlike other reduced model methods, our method constructs the reduced basis on-the-fly without the need to form the covariance matrix or to compute its eigendecomposition. In the first part of our paper, we introduce the derivation of the dynamically bi-orthogonal formulation for SPDEs, discuss several theoretical issues, such as the dynamic bi-orthogonality preservation and some preliminary error analysis of the DyBO method. We also give some numerical implementation details of the DyBO methods, including the representation of stochastic basis and techniques to deal with eigenvalue crossing. In the second part of our paper [11], we will present an adaptive strategy to dynamically remove or add modes, perform a detailed complexity analysis, and discuss various generalizations of this approach. An extensive range of numerical experiments will be provided in both parts to demonstrate the effectiveness of the DyBO method.