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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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...
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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...
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.
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.
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.
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.
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.
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.
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.
Li, Zhi; Chen, Weidong; Lian, Feiyu; Ge, Hongyi; Guan, Aihong
2017-01-01
Quantitative analysis of component mixtures is an important application of terahertz time-domain spectroscopy (THz-TDS) and has attracted broad interest in recent research. Although the accuracy of quantitative analysis using THz-TDS is affected by a host of factors, wavelength selection from the sample's THz absorption spectrum is the most crucial component. The raw spectrum consists of signals from the sample and scattering and other random disturbances that can critically influence the quantitative accuracy. For precise quantitative analysis using THz-TDS, the signal from the sample needs to be retained while the scattering and other noise sources are eliminated. In this paper, a novel wavelength selection method based on differential evolution (DE) is investigated. By performing quantitative experiments on a series of binary amino acid mixtures using THz-TDS, we demonstrate the efficacy of the DE-based wavelength selection method, which yields an error rate below 5%.
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.
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)
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.
Manonmani, N; Subbiah, V; Sivakumar, L
2015-01-01
The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs) supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs). The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.
N. Manonmani
2015-01-01
Full Text Available The key objective of wind turbine development is to ensure that output power is continuously increased. It is authenticated that wind turbines (WTs supply the necessary reactive power to the grid at the time of fault and after fault to aid the flowing grid voltage. At this juncture, this paper introduces a novel heuristic based controller module employing differential evolution and neural network architecture to improve the low-voltage ride-through rate of grid-connected wind turbines, which are connected along with doubly fed induction generators (DFIGs. The traditional crowbar-based systems were basically applied to secure the rotor-side converter during the occurrence of grid faults. This traditional controller is found not to satisfy the desired requirement, since DFIG during the connection of crowbar acts like a squirrel cage module and absorbs the reactive power from the grid. This limitation is taken care of in this paper by introducing heuristic controllers that remove the usage of crowbar and ensure that wind turbines supply necessary reactive power to the grid during faults. The controller is designed in this paper to enhance the DFIG converter during the grid fault and this controller takes care of the ride-through fault without employing any other hardware modules. The paper introduces a double wavelet neural network controller which is appropriately tuned employing differential evolution. To validate the proposed controller module, a case study of wind farm with 1.5 MW wind turbines connected to a 25 kV distribution system exporting power to a 120 kV grid through a 30 km 25 kV feeder is carried out by simulation.
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.
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.
Hesheng Tang; Yu Su; Jiao Wang
2015-08-01
The paper describes a procedure for the uncertainty quantification (UQ) using evidence theory in buckling analysis of semi-rigid jointed frame structures under mixed epistemic–aleatory uncertainty. The design uncertainties (geometrical, material, strength, and manufacturing) are often prevalent in engineering applications. Due to lack of knowledge or incomplete, inaccurate, unclear information in the modeling, simulation, measurement, and design, there are limitations in using only one framework (probability theory) to quantify uncertainty in a system because of the impreciseness of data or knowledge. Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. Unfortunately, propagation of an evidence theory representation for uncertainty through a model is more computationally demanding than propagation of a probabilistic representation for uncertainty. In order to alleviate the computational difficulties in the evidence theory based UQ analysis, a differential evolution-based computational strategy for propagation of epistemic uncertainty in a system with evidence theory is presented here. A UQ analysis for the buckling load of steel-plane frames with semi-rigid connections is given herein to demonstrate accuracy and efficiency of the proposed method.
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.
Differential evolution based on the node degree of its complex network: Initial study
Skanderova, Lenka; Zelinka, Ivan
2016-06-01
In this paper is reported our progress in the synthesis of two partially different areas of research: complex networks and evolutionary computation. Ideas and results reported and mentioned here are based on our previous results and experiments. The main core of our participation is an evolutionary algorithm performance improvement by means of complex network use. Complex network is related to the evolutionary dynamics and reflect it. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). Only the main ideas and results are reported here, for more details it is recommended to read related literature of our previous research and results.
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.
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.
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.
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...
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.
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
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.
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.
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.
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
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。
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.
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.
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.
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...
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara
2015-09-01
Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the
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.
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.
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...
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.
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.
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.
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.
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...
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.
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...
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.
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.
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.
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.
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.
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.
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。
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...
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...
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.
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....
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
Community Detection Based on Differential Evolution by Using Modularity Density%基于模块密度的差分进化社区发现技术
刘彩虹
2016-01-01
提出了一种基于模块密度的差分进化社区发现算法(community detection based on differential evolutionary algorithm,CDDEA).在CDDEA算法中,通过调节一个参数可以识别出不同层次的社区结构.在真实世界网络和计算机人工合成网络上的实验表明,CDDEA能够有效探测复杂网络中的社区结构.
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...
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.
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 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.
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.
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.
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...
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.
陈科尹; 邹湘军; 彭红星; 覃德泽
2016-01-01
According to the cumbersome and adaptive deficiencies of the picking robot kinematic inverse solution process , a picking robot kinematic inverse solution seeking method was presented based on the differential evolution algorithm . This method first utilizes picking robot motion positive equation to construct the objective function for solving picking robot motion inverse solution , and then uses the generalization and self-adaptive of the differential evolution algorithm to opti-mize the objective function , thus the picking robot kinematic inverse solution was solved .And in order to analyze the per-formance of this methodology , the traditional picking robot kinematic inverse solution and the picking robot kinematic in-verse solution based on the differential evolution algorithm were also respectively tested , which verified the effectiveness and robustness of the method in this paper .%针对传统的采摘机器人运动反解求法的求解过程过于繁琐及自适应性等不足，提出了一种基于微分进化的采摘机器人运动反解求取方法。该方法首先利用采摘机器人运动正解方程构造出求解采摘机器人运动反解的目标函数，然后运用微分进化算法的泛化性和自适应性对该目标函数进行优化处理，从而求解出采摘机器人运动反解。同时，为了分析该方法的性能，还分别对传统的采摘机器人运动反解和基于微分进化的采摘机器人运动反解进行了对比试验，从而验证了该方法的有效性和鲁棒性。
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.
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.
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 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....
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.
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.
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...
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.
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.
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.
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.
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.
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.
徐涛; 郭威; 吕宗磊
2016-01-01
Traditional airport noise prediction models are insufficient for their high modeling cost and poor practicability. In this paper, the time series phase space reconstruction theory is introduced, and a novel integrated airport noise prediction model based on fast extreme learning machine and differential evolution is proposed. In the proposed model, the airport noise time series is reconstructed based on the phase space reconstruction theory, and the fast extreme learning machine is used to model the reconstructed phase space vector. Meanwhile, an improved differential evolution algorithm is adopted to search for the optimal parameter combination of phase space reconstruction parameter and model parameter simultaneously. The whole modeling process of the integrated prediction model is very simple and efficient without any manual intervention. Experimental results demonstrate that the proposed model can track the variation tendency of airport noise well and can achieve much more accurate prediction results than its counterparts.%该文针对传统机场噪声预测模型存在的建模成本高、实用性差的不足，引入时间序列相空间重构理论，提出一种新的基于快速极限学习机和差分进化算法的机场噪声一体化预测模型。该模型利用相空间重构理论对机场噪声时间序列进行重构，并使用快速极限学习机对重构的相空间矢量进行学习建模，同时采用改进的差分进化算法实现对重构参数和模型参数的同步优化选择，整个建模过程简洁高效，无需人工干预。实验结果表明，该一体化预测模型能较好地跟踪机场噪声的变化趋势，且具有较同类模型更小的预测误差。
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.
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.
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.
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.
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.
Differential geometry based multiscale models.
Wei, Guo-Wei
2010-08-01
Large chemical and biological systems such as fuel cells, ion channels, molecular motors, and viruses are of great importance to the scientific community and public health. Typically, these complex systems in conjunction with their aquatic environment pose a fabulous challenge to theoretical description, simulation, and prediction. In this work, we propose a differential geometry based multiscale paradigm to model complex macromolecular systems, and to put macroscopic and microscopic descriptions on an equal footing. In our approach, the differential geometry theory of surfaces and geometric measure theory are employed as a natural means to couple the macroscopic continuum mechanical description of the aquatic environment with the microscopic discrete atomistic description of the macromolecule. Multiscale free energy functionals, or multiscale action functionals are constructed as a unified framework to derive the governing equations for the dynamics of different scales and different descriptions. Two types of aqueous macromolecular complexes, ones that are near equilibrium and others that are far from equilibrium, are considered in our formulations. We show that generalized Navier-Stokes equations for the fluid dynamics, generalized Poisson equations or generalized Poisson-Boltzmann equations for electrostatic interactions, and Newton's equation for the molecular dynamics can be derived by the least action principle. These equations are coupled through the continuum-discrete interface whose dynamics is governed by potential driven geometric flows. Comparison is given to classical descriptions of the fluid and electrostatic interactions without geometric flow based micro-macro interfaces. The detailed balance of forces is emphasized in the present work. We further extend the proposed multiscale paradigm to micro-macro analysis of electrohydrodynamics, electrophoresis, fuel cells, and ion channels. We derive generalized Poisson-Nernst-Planck equations that are
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.
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.
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.
Micro-droplet based directed evolution outperforms conventional laboratory evolution
Sjostrom, Staffan L.; Huang, Mingtao; Nielsen, Jens
2014-01-01
are confined in microfluidic droplets to prevent the phenotype, e.g. secreted enzymes, from leaking between cells. The method was benchmarked against and found to significantly outperform conventional adaptive laboratory evolution (ALE) in enriching enzyme producing cells. It was furthermore applied to enrich......We present droplet adaptive laboratory evolution (DrALE), a directed evolution method used to improve industrial enzyme producing microorganisms for e.g. feedstock digestion. DrALE is based linking a desired phenotype to growth rate allowing only desired cells to proliferate. Single cells...
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.
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...
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.
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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
CNEM: Cluster Based Network Evolution Model
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
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.
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.
许斌; 亓晋; 印溪; 王野; 常瑞云
2016-01-01
移动互联网技术的普及使人们不再满足于单一功能的服务,而更倾向于按需定制的个性化服务或服务组合.提出了一种应用于Web服务组合的多策略离散差分进化(multi-strategy discrete differential evolution,MDDE)算法.该算法采用随机选择框架,调用具有不同特性的变异策略,是一种搜索能力和收敛速度均衡的离散差分进化算法.实验结果表明,MDDE算法在求解Web服务组合优化问题中比原始DE算法的收敛精度更高,稳定性更好.
Partial evolution based local adiabatic quantum search
Sun Jie; Lu Song-Feng; Liu Fang; Yang Li-Ping
2012-01-01
Recently,Zhang and Lu provided a quantum search algorithm based on partial adiabatic evolution,which beats the time bound of local adiabatic search when the number of marked items in the unsorted database is larger than one.Later,they found that the above two adiabatic search algorithms had the same time complexity when there is only one marked item in the database.In the present paper,following the idea of Roland and Cerf [Roland J and Cerf N J 2002Phys.Rev.A 65 042308],if within the small symmetric evolution interval defined by Zhang et al.,a local adiabatic evolution is performed instead of the original “global” one,this “new” algorithm exhibits slightly better performance,although they are progressively equivalent with M increasing.In addition,the proof of the optimality for this partial evolution based local adiabatic search when M =1 is also presented.Two other special cases of the adiabatic algorithm obtained by appropriately tuning the evolution interval of partial adiabatic evolution based quantum search,which are found to have the same phenomenon above,are also discussed.
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.
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.
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.
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.
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.
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
薛晓岑; 向文国; 吕剑虹
2014-01-01
针对热工过程的非线性辨识问题，提出了一种基于差分进化算法（ DE ）的径向基函数神经网络（ RBFNN）模型设计方法。该方法将DE算法的种群分解为几组并行的子种群，每组子种群对应于一类隐节点数相同的RBF网络。在RBFNN的学习过程中进行多子种群并行优化，从而实现RBF网络结构与参数的同时调整。算法可以利用热工对象的输入输出数据，自动设计出满足误差精度要求且结构较小的RBFNN模型。然后将该算法应用于热工对象的辨识，对于单输入单输出系统，得到的RBFNN模型只需1个隐节点。对于多输入单输出系统，RBF网络也仅需较少的隐层节点。仿真结果表明，用该方法设计的RBFNN模型结构简单，且辨识误差小，具有较好的泛化能力。%For the nonlinear identification of thermal process, a new radial basis function neural net-work ( RBFNN) design method is proposed based on the differential evolution algorithm ( DE) .In the method, the population in the DE algorithm is divided into several parallel subpopulations, and each subpopulation corresponds to a class of RBF network solutions with the same hidden nodes.In the RBFNN learning process, the network structure and parameters are adjusted simultaneously through the parallel optimization of the subpopulations.Under the given error limit, the algorithm can design an RBF model automatically with fewer hidden nodes according to thermal input and out-put data.Then, the algorithm is used to identify nonlinear thermal processes.For single-input sin-gle-output system identification, only one node is required in the RBFNN hidden layer.For multi-in-put single-output system identification, the RBFNN model also requires less hidden nodes.The sim-ulation results show that the proposed approach can achieve the given identification accuracy with fe-wer hidden nodes, and has good generalization ability.
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.
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。
李树平; 谢少荣; 程军; 李恒宇; 李超; 罗均
2011-01-01
确定了仿生眼的目标工作空间,并以雅可比矩阵条件数最大值定义了最差灵巧度.利用差分进化的全局寻优能力对最差灵巧度的最大值进行优化,采用动态缩放因子解决优化过程中的早熟问题,增强了全局搜索能力.在满足仿生眼特殊结构要求并使目标工作空间内任意姿态都具有较好灵巧度的情况下,确定了合理的结构参数.最后实验表明参数优化后的仿生眼实物很好地满足了设计要求.%The target working space of bionic eye was firstly determined. The worst dexterity for bionic eye with the maximum condition number of Jacobian matrix was defined. Then, a differential evolution ( DE) algorithm was used to optimize the maximum of the worst dexterity index. Dynamic scaling factor was determined according to the overcome premature evolution and enhance the probability of finding global optimum. Reasonable structural parameters were chosen in the case of satisfying the special requirements of bionic eye structure and making arbitrary posture dexterity of target working space better. Finally, the experimental results show that bionic eye with the optimized parameters are good coinciding with the design requirements.
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%.
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.
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.
A data base for galaxy evolution modeling
Leitherer, C; Alloin, D; FritzVonAlvensleben, U; Gallagher, JS; Huchra, JP; Matteucci, F; OConnell, RW; Beckman, JE; Bertelli, GP; Bica, E; Boisson, C; Bonatto, C; Bothun, GD; Bressan, A; Brodie, JP; Bruzual, G; Burstein, D; Buser, R; Caldwell, N; Casuso, E; Cervino, M; Charlot, S; Chavez, M; Chiosi, C; Christian, CA; Cuisinier, F; Dallier, R; deKoter, A; Delisle, S; Diaz, AI; Dopita, MA; Dorman, B; Fagotto, F; Fanelli, MN; Fioc, M; GarciaVargas, ML; Girardi, L; Goldader, JD; Hardy, E; Heckman, TM; Iglesias, J; Jablonka, P; Joly, M; Jones, L; Kurth, O; Lancon, A; Lejeune, T; Loxen, J; Maeder, A; Malagnini, ML; Marigo, P; MasHesse, JM; Meynet, G; Moller, CS; Molla, ML; Morossi, C; Nasi, E; Nichols, JS; Odegaard, KJR; Parker, JWM; Pastoriza, MG; Peletier, R; Robert, C; RoccaVolmerange, B; Schaerer, D; Schmidt, A; Schmitt, HR; Schommer, RA; Schmutz, W; Silva, L; Stasinska, G; Sutherland, RS; Tantalo, R; Traat, P; Vallenari, A; Vazdekis, A; Walborn, NR; Worthey, G
1996-01-01
This paper represents a collective effort to provide an extensive electronic data base useful for the interpretation of the spectra and evolution of galaxies. A broad variety of empirical and theoretical data is discussed here, and the data are made fully available in the AAS CD-ROM Series, Vol. 7.
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.
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.
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.
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.
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
Evolution of microcomputer-based medical instrumentation.
Tompkins, Willis J
2009-01-01
This paper provides a historical review of the evolution of the technologies that led to modern microcomputer-based medical instrumentation. I review the history of the microprocessor-based system because of the importance of the microprocessor in the design of modern medical instruments. I then give some examples of medical instruments in which the microprocessor has played a key role and in some cases has even empowered us to develop new instruments that were not possible before. I include a discussion of the role of the microprocessor-based personal computer in development of medical instruments.
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
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.
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.
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
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.
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
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.
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.
Optimal Overlay of Ligands with Flexible Bonds Using Differential Evolution
Kristensen, Thomas Greve; Pedersen, Christian Storm
2009-01-01
might improve the quality of the search by taking all of these into account. This can be done by generating a meta-structure which summarizes the active ligands and use this meta-structure for querying the database. In this paper we propose a method for making such a meta-structure by making a multiple...... of the two implementations on a data set from a previous study in the field and conclude that the DE based implementation outperforms the NM based implementation....
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.
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....
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...
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.
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.
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.
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.
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...
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
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.
Average Gait Differential Image Based Human Recognition
Jinyan Chen
2014-01-01
Full Text Available The difference between adjacent frames of human walking contains useful information for human gait identification. Based on the previous idea a silhouettes difference based human gait recognition method named as average gait differential image (AGDI is proposed in this paper. The AGDI is generated by the accumulation of the silhouettes difference between adjacent frames. The advantage of this method lies in that as a feature image it can preserve both the kinetic and static information of walking. Comparing to gait energy image (GEI, AGDI is more fit to representation the variation of silhouettes during walking. Two-dimensional principal component analysis (2DPCA is used to extract features from the AGDI. Experiments on CASIA dataset show that AGDI has better identification and verification performance than GEI. Comparing to PCA, 2DPCA is a more efficient and less memory storage consumption feature extraction method in gait based recognition.
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.
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
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.
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.
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)
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.
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.
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.
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 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...
Neuron-Based Heredity and Human Evolution
Don Marshall Gash
2015-06-01
Full Text Available Abstract:Abstract: It is widely recognized that human evolution has been driven by two systems of heredity: one DNA-based and the other based on the transmission of behaviorally acquired information via nervous system functions. The genetic system is ancient, going back to the appearance of life on Earth. It is responsible for the evolutionary processes described by Darwin. By comparison, the nervous system is relatively newly minted and in its highest form, responsible for ideation and mind-to-mind transmission of information. Here the informational capabilities and functions of the two systems are compared. While employing quite different mechanisms for encoding, storing and transmission of information, both systems perform these generic hereditary functions. Three additional features of neuron-based heredity in humans are identified: the ability to transfer hereditary information to other members of their population, not just progeny; a selection process for the information being transferred; and a profoundly shorter time span for creation and dissemination of survival-enhancing information in a population. The mechanisms underlying neuron-based heredity involve hippocampal neurogenesis and memory and learning processes modifying and creating new neural assemblages changing brain structure and functions. A fundamental process in rewiring brain circuitry is through increased neural activity (use strengthening and increasing the number of synaptic connections. Decreased activity in circuitry (disuse leads to loss of synapses. Use and disuse modifying an organ to bring about new modes of living, habits and functions are processes are in line with Neolamarckian concepts of evolution (Packard, 1901. Evidence is presented of bipartite evolutionary processes – Darwinian and Neolamarckian – driving human descent from a common ancestor shared with the great apes.
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.
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.
[The motive force of evolution based on the principle of organismal adjustment evolution.].
Cao, Jia-Shu
2010-08-01
From the analysis of the existing problems of the prevalent theories of evolution, this paper discussed the motive force of evolution based on the knowledge of the principle of organismal adjustment evolution to get a new understanding of the evolution mechanism. In the guide of Schrodinger's theory - "life feeds on negative entropy", the author proposed that "negative entropy flow" actually includes material flow, energy flow and information flow, and the "negative entropy flow" is the motive force for living and development. By modifying my own theory of principle of organismal adjustment evolution (not adaptation evolution), a new theory of "regulation system of organismal adjustment evolution involved in DNA, RNA and protein interacting with environment" is proposed. According to the view that phylogenetic development is the "integral" of individual development, the difference of negative entropy flow between organisms and environment is considered to be a motive force for evolution, which is a new understanding of the mechanism of evolution. Based on such understanding, evolution is regarded as "a changing process that one subsystem passes all or part of its genetic information to the next generation in a larger system, and during the adaptation process produces some new elements, stops some old ones, and thereby lasts in the larger system". Some other controversial questions related to evolution are also discussed.
Grbner bases in difference-differential modules and difference-differential dimension polynomials
Franz; WINKLER
2008-01-01
In this paper we extend the theory of Grbner bases to difference-differential modules and present a new algorithmic approach for computing the Hilbert function of a finitely generated difference-differential module equipped with the natural filtration. We present and verify algorithms for construct-ing these Grbner bases counterparts. To this aim we introduce the concept of "generalized term order" on Nm ×Zn and on difference-differential modules. Using Grbner bases on difference-differential mod-ules we present a direct and algorithmic approach to computing the difference-differential dimension polynomials of a difference-differential module and of a system of linear partial difference-differential equations.
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.
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.
Facial Beautification Method Based on Age Evolution
CHEN Yan; DING Shou-hong; HU Gan-le; MA Li-zhuang
2013-01-01
This paper proposes a new facial beautification method using facial rejuvenation based on the age evolution. Traditional facial beautification methods only focus on the color of skin and deformation and do the transformation based on an experimental standard of beauty. Our method achieves the beauty effect by making facial image looks younger, which is different from traditional methods and is more reasonable than them. Firstly, we decompose the image into different layers and get a detail layer. Secondly, we get an age-related parameter:the standard deviation of the Gaussian distribution that the detail layer follows, and the support vector machine (SVM) regression is used to fit a function about the age and the standard deviation. Thirdly, we use this function to estimate the age of input image and generate a new detail layer with a new standard deviation which is calculated by decreasing the age. Lastly, we combine the original layers and the new detail layer to get a new face image. Experimental results show that this algo-rithm can make facial image become more beautiful by facial rejuvenation. The proposed method opens up a new way about facial beautification, and there are great potentials for applications.
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.
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
Systems Engineering Education Based on Evolutional Project-Based Learning
Inoue, Masahiro; Hasegawa, Hiroshi
The knowledge and skills in systems engineering including project management are necessary for engineers who are engaged in planning and developing systems. Experiences of project execution are necessary for understanding systems engineering. Challenge is how to teach systems engineering to students who have scarce project experiences. In the education, giving the experience including a real experience and a pseudo-experience will be indispensable. In this paper, systems engineering education by evolutional Project-Based Learning (PBL) is designed and evaluated. In curriculum, exercises and lectures are executed alternately and evolutionally in three steps of PBLs ; Workshop of System Thinking, mathematical knowledge and technique are delivered in the first step PBL. Techniques of systems engineering are provided in the second step PBL. Finally project management is obtained in the third step PBL. Execution and evaluation of the education show that the Evolutional Project-Based Learning of systems engineering is effective not only to improve knowledge and experience of students but also to motivate students to study systems engineering.
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.
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.
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.
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.
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.
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.
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.
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.
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...
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.
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
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.
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.
A resonance based model of biological evolution
Damasco, Achille; Giuliani, Alessandro
2017-04-01
We propose a coarse grained physical model of evolution. The proposed model 'at least in principle' is amenable of an experimental verification even if this looks as a conundrum: evolution is a unique historical process and the tape cannot be reversed and played again. Nevertheless, we can imagine a phenomenological scenario tailored upon state transitions in physical chemistry in which different agents of evolution play the role of the elements of a state transition like thermal noise or resonance effects. The abstract model we propose can be of help for sketching hypotheses and getting rid of some well-known features of natural history like the so-called Cambrian explosion. The possibility of an experimental proof of the model is discussed as well.
An implicit evolution scheme for active contours and surfaces based on IIR filtering.
Delibasis, Konstantinos K; Asvestas, Pantelis A; Kechriniotis, Aristides I; Matsopoulos, George K
2014-05-01
In this work, we present an approach for implementing an implicit scheme for the numerical solution of the partial differential equation of the evolution of an active contour/surface. The proposed scheme is applicable to any variant of the traditional active contour (AC), irrespectively of the calculation of the image-based force field and it is readily applicable to explicitly parameterized active surfaces (AS). The proposed approach is formulated as an infinite impulse response (IIR) filtering of the coordinates of the contour/surface points. The poles of the filter are determined by the parameters controlling the shape of the active contour/surface. We show that the proposed IIR-based implicit evolution scheme has very low complexity. Furthermore, the proposed scheme is numerically stable, thus it allows the convergence of the AC/AS with significantly fewer iterations than the explicit evolution scheme. It also possesses the separability property along the two parameters of the AS, thus it may be applied to deformable surfaces, without the need to store and invert large sparse matrices. We implemented the proposed IIR-based implicit evolution scheme in the Vector Field Convolution (VFC) AC/AS using synthetic and clinical volumetric data. We compared the segmentation results with those of the explicit AC/AS evolution, in terms of accuracy and efficiency. Results show that the VFC AC/AS with the proposed IIR-based implicit evolution scheme achieves the same segmentation results with the explicit scheme, with considerably less computation time.
Adaptive Game Level Creation through Rank-based Interactive Evolution
Liapis, Antonios; Martínez, Héctor Pérez; Togelius, Julian
2013-01-01
This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as f...
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.
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.
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.
A Line Based Visualization of Code Evolution
Voinea, S.L.; Telea, A.; Wijk, J.J. van
2005-01-01
The source code of software systems changes many times during the system lifecycle. We study how developers can get insight in these changes in order to understand the project context and the product artifacts. For this we propose new techniques for code evolution representation and visualization in
Evolution of Web-based International Marketing
Rask, Morten
2002-01-01
Companies that have been doing international business do not usually make the transition from traditional marketers to full-blown Web marketers in one sharp step. In our study of Danish firms, we found that in terms of the evolution of their Web strategies, these Danish companies went through three...
Blind Equalization Based on Evolution Strategies
SongYu; ZhangXianda; 等
1997-01-01
Conventional blind equalization algorithms suffer from ill convergence to local minima and slow convergence speed.This paper proposes a novel blind equalization algorithm.using random search methods-evolution strategies and existing cost functions,Simulation results verify the fast and global convergence of the proposed algorithm.
A File Based Visualization of Software Evolution
Voinea, S.L.; Telea, A.
2006-01-01
Software Configuration Management systems are important instruments for supporting development of large software projects. They accumulate large amounts of evolution data that can be used for process accounting and auditing. We study how visualization can help developers and managers to get insight
2013-01-01
Identification of the unknown parameters and orders of fractional chaotic systems is of vital significance in controlling and synchronization of fractional-order chaotic systems. In this paper, a non-Lyapunov novel approach is proposed to estimate the unknown parameters and orders together for non-commensurate and hyper fractional chaotic systems based on cuckoo search oriented statistically the differential evolution (CSODE). Firstly, a novel Gao's mathematical model is put and analysed in t...
Leung, Nelson; Abdelhafez, Mohamed; Koch, Jens; Schuster, David
2017-04-01
We implement a quantum optimal control algorithm based on automatic differentiation and harness the acceleration afforded by graphics processing units (GPUs). Automatic differentiation allows us to specify advanced optimization criteria and incorporate them in the optimization process with ease. We show that the use of GPUs can speedup calculations by more than an order of magnitude. Our strategy facilitates efficient numerical simulations on affordable desktop computers and exploration of a host of optimization constraints and system parameters relevant to real-life experiments. We demonstrate optimization of quantum evolution based on fine-grained evaluation of performance at each intermediate time step, thus enabling more intricate control on the evolution path, suppression of departures from the truncated model subspace, as well as minimization of the physical time needed to perform high-fidelity state preparation and unitary gates.
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.
Evolution based on chromosome affinity from a network perspective
Monteiro, R. L. S.; Fontoura, J. R. A.; Carneiro, T. K. G.; Moret, M. A.; Pereira, H. B. B.
2014-06-01
Recent studies have focused on models to simulate the complex phenomenon of evolution of species. Several studies have been performed with theoretical models based on Darwin's theories to associate them with the actual evolution of species. However, none of the existing models include the affinity between individuals using network properties. In this paper, we present a new model based on the concept of affinity. The model is used to simulate the evolution of species in an ecosystem composed of individuals and their relationships. We propose an evolutive algorithm that incorporates the degree centrality and efficiency network properties to perform the crossover process and to obtain the network topology objective, respectively. Using a real network as a starting point, we simulate its evolution and compare its results with the results of 5788 computer-generated networks.
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.
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.
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.
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.
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.
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.
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
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.
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.
Modulation of DNA base excision repair during neuronal differentiation
Sykora, Peter; Yang, Jenq-Lin; Ferrarelli, Leslie K
2013-01-01
Neurons are terminally differentiated cells with a high rate of metabolism and multiple biological properties distinct from their undifferentiated precursors. Previous studies showed that nucleotide excision DNA repair is downregulated in postmitotic muscle cells and neurons. Here, we characterize...... DNA damage susceptibility and base excision DNA repair (BER) capacity in undifferentiated and differentiated human neural cells. The results show that undifferentiated human SH-SY5Y neuroblastoma cells are less sensitive to oxidative damage than their differentiated counterparts, in part because...
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.
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...
Evolution of Web-based International Marketing
Rask, Morten
2002-01-01
Companies that have been doing international business do not usually make the transition from traditional marketers to full-blown Web marketers in one sharp step. In our study of Danish firms, we found that in terms of the evolution of their Web strategies, these Danish companies went through three...... stages. We call these stages: 1) the Electronic Brochure, 2) the Electronic Manual, and 3) the Electronic Store. Moving from the Brochure to the Manual to the Store stage entails an increasing intensity of interaction between the company's website and its customers. Companies have to go through...
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.
Quantum Key Distribution Network Based on Differential Phase Shift
WANG Wan-Ying; WANG Chuan; WEN Kai; LONG Gui-Lu
2007-01-01
Using a series of quantum correlated photon pairs, we propose a theoretical scheme for any-to-any multi-user quantum key distribution network based on differential phase shift. The differential phase shift and the different detection time slots ensure the security of our scheme against eavesdropping. We discuss the security under the intercept-resend attack and the source replacement attack.
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...
Digital differential confocal microscopy based on spatial shift transformation.
Liu, J; Wang, Y; Liu, C; Wilson, T; Wang, H; Tan, J
2014-11-01
Differential confocal microscopy is a particularly powerful surface profilometry technique in industrial metrology due to its high axial sensitivity and insensitivity to noise. However, the practical implementation of the technique requires the accurate positioning of point detectors in three-dimensions. We describe a simple alternative based on spatial transformation of a through-focus series of images obtained from a homemade beam scanning confocal microscope. This digital differential confocal microscopy approach is described and compared with the traditional Differential confocal microscopy approach. The ease of use of the digital differential confocal microscopy system is illustrated by performing measurements on a 3D standard specimen.
Differential evolution based method for total transfer capability ...
user
2 Department of Physics, Pondicherry Engineering College, Puducherry, INDIA, ..... and simple Numerical optimizer, Biennial conference of the North American ... K. Manivannan obtained his Ph. D. from Indian Institute of Madras, India and ...
A framework for Internet service evolution based on active object
HU Hua; ZHANG Yang
2006-01-01
The wide use of Internet Service in distributed computing and e-business has made the evolution of Internet Service to be one of the most prevalent research fields in software development domain. Traditional methods for software development cannot adapt to the challenge of Internet Service oriented software development. In this paper, we propose a new paradigm for the evolution of Internet Service with active objects from the characteristics of Internet Service and principles of active objects. The paradigm uses an automatic monitoring mechanism of active object to detect and process evolution requirement in system based on Internet Service.
Adaptive Game Level Creation through Rank-based Interactive Evolution
Liapis, Antonios; Martínez, Héctor Pérez; Togelius, Julian
2013-01-01
as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using...... artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches....
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.
李静; 洪文学
2014-01-01
模式识别问题中特征提取和特征选择是一个重要问题.基于向量的几何代数表示方法,提出了一种新的几何代数片积系数特征提取方法,并对其中存在的维数升高问题进行了研究,提出了改进的微分进化特征选择方法.本文分类器采用线性判别分析,以公开的乳腺癌生物医学数据集进行10折交叉验证(10 CV),得到的分类结果超过了96％,优于原始特征和传统特征提取方法下的分类性能.%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.
Zabrina L Brumme
2007-07-01
Full Text Available Despite the formidable mutational capacity and sequence diversity of HIV-1, evidence suggests that viral evolution in response to specific selective pressures follows generally predictable mutational pathways. Population-based analyses of clinically derived HIV sequences may be used to identify immune escape mutations in viral genes; however, prior attempts to identify such mutations have been complicated by the inability to discriminate active immune selection from virus founder effects. Furthermore, the association between mutations arising under in vivo immune selection and disease progression for highly variable pathogens such as HIV-1 remains incompletely understood. We applied a viral lineage-corrected analytical method to investigate HLA class I-associated sequence imprinting in HIV protease, reverse transcriptase (RT, Vpr, and Nef in a large cohort of chronically infected, antiretrovirally naïve individuals. A total of 478 unique HLA-associated polymorphisms were observed and organized into a series of "escape maps," which identify known and putative cytotoxic T lymphocyte (CTL epitopes under selection pressure in vivo. Our data indicate that pathways to immune escape are predictable based on host HLA class I profile, and that epitope anchor residues are not the preferred sites of CTL escape. Results reveal differential contributions of immune imprinting to viral gene diversity, with Nef exhibiting far greater evidence for HLA class I-mediated selection compared to other genes. Moreover, these data reveal a significant, dose-dependent inverse correlation between HLA-associated polymorphisms and HIV disease stage as estimated by CD4(+ T cell count. Identification of specific sites and patterns of HLA-associated polymorphisms across HIV protease, RT, Vpr, and Nef illuminates regions of the genes encoding these products under active immune selection pressure in vivo. The high density of HLA-associated polymorphisms in Nef compared to other
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
Preserving Differential Privacy in Degree-Correlation based Graph Generation
Yue Wang
2013-08-01
Full Text Available Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dKgraph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model.
Preserving Differential Privacy in Degree-Correlation based Graph Generation.
Wang, Yue; Wu, Xintao
2013-08-01
Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as cluster coefficient often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabular data. In this paper, we study the problem of enforcing edge differential privacy in graph generation. The idea is to enforce differential privacy on graph model parameters learned from the original network and then generate the graphs for releasing using the graph model with the private parameters. In particular, we develop a differential privacy preserving graph generator based on the dK-graph generation model. We first derive from the original graph various parameters (i.e., degree correlations) used in the dK-graph model, then enforce edge differential privacy on the learned parameters, and finally use the dK-graph model with the perturbed parameters to generate graphs. For the 2K-graph model, we enforce the edge differential privacy by calibrating noise based on the smooth sensitivity, rather than the global sensitivity. By doing this, we achieve the strict differential privacy guarantee with smaller magnitude noise. We conduct experiments on four real networks and compare the performance of our private dK-graph models with the stochastic Kronecker graph generation model in terms of utility and privacy tradeoff. Empirical evaluations show the developed private dK-graph generation models significantly outperform the approach based on the stochastic Kronecker generation model.
STRUCTURAL EVOLUTION IN BIORENEWABLE SOY BASED POLYURETHANES
Deepa Puthanparambil; Casey Kimball; Shaw Ling Hsu; Zhiyong Ren
2009-01-01
Spectroscopic studies have revealed that the amount of polyureas formed and the kinetics of their formation in soy based polyurethane systems are considerably different from traditional systems employing ethylene oxide-propylene oxide (EO-PO) based polyols. The aggregation of polyureas was characterized by the hydrogen bonds formed utilizing FTIR spectroscopy. This study offered the opportunity to assign the previously undefined infrared features. The structural transformation is reflected in the segmental relaxation kinetics characterized by spin-spin diffusion most conveniently measured using low field NMR. The reaction kinetics and the products formed are directly related to the hydrophobic nature of the soy based polyols and its inability to disperse water.
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.
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...
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.
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...
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Differential Regulatory Analysis Based on Coexpression Network in Cancer Research
Junyi Li
2016-01-01
Full Text Available With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA based on gene coexpression network (GCN increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.
Horiuchi, Youko
2015-01-01
Abstract 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
Differential modulation based on space-time block codes
李正权; 胡光锐
2004-01-01
A differential modulation scheme using space-time block codes is put forward. Compared with other schemes,our scheme has lower computational complexity and has a simpler decoder. In the case of three or four transmitter antennas, our scheme has a higher rate a higher coding gain and a lower bit error rate for a given rate. Then we made simulations for space-time block codes as well as group codes in the case of two, three, four and five transmit antennas. The simulations prove that using two transmit antennas, one receive antenna and code rate of 4 bits/s/Hz, the differential STBC method outperform the differential group codes method by 4 dB. Useing three, four and five transmit antennas,one receive antenna, and code rate of 3 bits/s/Hz are adopted, the differential STBC method outperform the differential group codes method by 5 dB, 6.5 dB and 7 dB, respectively. In other words, the differential modulation scheme based on space-time block code is better than the corresponding differential modulation scheme
Covariance analysis of differential drag-based satellite cluster flight
Ben-Yaacov, Ohad; Ivantsov, Anatoly; Gurfil, Pini
2016-06-01
One possibility for satellite cluster flight is to control relative distances using differential drag. The idea is to increase or decrease the drag acceleration on each satellite by changing its attitude, and use the resulting small differential acceleration as a controller. The most significant advantage of the differential drag concept is that it enables cluster flight without consuming fuel. However, any drag-based control algorithm must cope with significant aerodynamical and mechanical uncertainties. The goal of the current paper is to develop a method for examination of the differential drag-based cluster flight performance in the presence of noise and uncertainties. In particular, the differential drag control law is examined under measurement noise, drag uncertainties, and initial condition-related uncertainties. The method used for uncertainty quantification is the Linear Covariance Analysis, which enables us to propagate the augmented state and filter covariance without propagating the state itself. Validation using a Monte-Carlo simulation is provided. The results show that all uncertainties have relatively small effect on the inter-satellite distance, even in the long term, which validates the robustness of the used differential drag controller.
Fully Digital Chaotic Differential Equation-based Systems And Methods
Radwan, Ahmed Gomaa Ahmed
2012-09-06
Various embodiments are provided for fully digital chaotic differential equation-based systems and methods. In one embodiment, among others, a digital circuit includes digital state registers and one or more digital logic modules configured to obtain a first value from two or more of the digital state registers; determine a second value based upon the obtained first values and a chaotic differential equation; and provide the second value to set a state of one of the plurality of digital state registers. In another embodiment, a digital circuit includes digital state registers, digital logic modules configured to obtain outputs from a subset of the digital shift registers and to provide the input based upon a chaotic differential equation for setting a state of at least one of the subset of digital shift registers, and a digital clock configured to provide a clock signal for operating the digital shift registers.
A differentiation-based phylogeny of cancer subtypes.
Markus Riester
2010-05-01
Full Text Available Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors.
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.
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.
A Novel Software Evolution Model Based on Software Networks
Pan, Weifeng; Li, Bing; Ma, Yutao; Liu, Jing
Many published papers analyzed the forming mechanisms and evolution laws of OO software systems from software reuse, software pattern, etc. There, however, have been fewer models so far merely built on the software components such as methods, classes, etc. and their interactions. In this paper, a novel Software Evolution Model based on Software Networks (called SEM-SN) is proposed. It uses software network at class level to represent software systems, and uses software network’s dynamical generating process to simulate activities in real software development process such as new classes’ dynamical creations and their dynamical interactions with already existing classes. It also introduces the concept of node/edge ageing to describe the decaying of classes with time. Empirical results on eight open-source Object-Oriented (OO) software systems demonstrate that SCM-SN roughly describes the evolution process of software systems and the emergence of their complex network characteristics.
于世英; 吴晓辉; 何海涛; 王倩
2012-01-01
为了克服传统板形控制中产品质量差、控制速度慢、生成效率低,以及静态影响矩阵控制信息不足等缺点,将云自适应差分算法(CADE)优化的BP神经网络应用到板形控制中,建立板形预测神经网络,并在离线状态下,根据板形轧制的历史数据和板形调控机构中的关键影响因素建立动态影响矩阵表.在线轧制过程中只需要与板形控制关键影响因素对应的动态影响矩阵表和板形识别变化量,就可以很快得到主要板形控制手段的控制量.该方法避免了神经网络的在线训练,提高了板形的控制速度和轧制精度.仿真实验表明,该方法稳定性好,控制精度高,适合用于板形的在线控制.%In order to solve the problems of poor product quality, slow control, low production efficiency and the control information lack of static influence matrix in the pattern control of flatness. This paper used the Cloud Adaptive Differential Evolution ( CADE) algorithm to optimize the BP neural network and then applied the CADE-BP network to the flatness control and flatness prediction. According to the historical rolling data and the key effective factors of flatness control mechanism, the empirical value tables of effective matrix were established in an offline state, and the corresponding adjustment parameters could be quickly calculated on line based on the empirical value tables of effective matrix and flatness recognition result. This method avoided the neural network online training, and improved the speed of flatness control and rolling precision. The results show that the method has better stability and control accuracy, suitable for on-line flatness control.
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
Homotopy-based methods for fractional differential equations
Ateş, Inan
2017-01-01
The intention of this thesis is two-fold. The first aim is to describe and apply, series-based, numerical methods to fractional differential equation models. For this, it is needed to distinguish between space-fractional and time-fractional derivatives. The second goal of this thesis is to give a
Bai, Shirong; Skodje, Rex T
2017-08-17
A new approach is presented for simulating the time-evolution of chemically reactive systems. This method provides an alternative to conventional modeling of mass-action kinetics that involves solving differential equations for the species concentrations. The method presented here avoids the need to solve the rate equations by switching to a representation based on chemical pathways. In the Sum Over Histories Representation (or SOHR) method, any time-dependent kinetic observable, such as concentration, is written as a linear combination of probabilities for chemical pathways leading to a desired outcome. In this work, an iterative method is introduced that allows the time-dependent pathway probabilities to be generated from a knowledge of the elementary rate coefficients, thus avoiding the pitfalls involved in solving the differential equations of kinetics. The method is successfully applied to the model Lotka-Volterra system and to a realistic H2 combustion model.
Structure analysis of growing network based on partial differential equations
Junbo JIA
2016-04-01
Full Text Available The topological structure is one of the most important contents in the complex network research. Therein the node degree and the degree distribution are the most basic characteristic quantities to describe topological structure. In order to calculate the degree distribution, first of all, the node degree is considered as a continuous variable. Then, according to the Markov Property of growing network, the cumulative distribution function's evolution equation with time can be obtained. Finally, the partial differential equation (PDE model can be established through distortion processing. Taking the growing network with preferential and random attachment mechanism as an example, the PDE model is obtained. The analytic expression of degree distribution is obtained when this model is solved. Besides, the degree function over time is the same as the characteristic line of PDE. At last, the model is simulated. This PDE method of changing the degree distribution calculation into problem of solving PDE makes the structure analysis more accurate.
Location-based reliability differentiated service for wireless sensor networks
Yong ZENG; Jianfeng MA
2009-01-01
Designing reliability differentiated services for missions with different reliability requirements has become a hot topic in wireless sensor networks. Combined with a location-based routing mechanism, a quantified model without full network topology is proposed to evaluate reliability. By introducing a virtual reference point, the data transfer is limited in a specified area. The reliability function of the area is given. A detailed analysis shows that the function increases quadratically with the distance between the source node and the reference node. A reliability differentiated service mechanism is then proposed. The simulation results show the efficiency of the proposed mechanism.
Differentiation-Based Analysis of Environmental Management and Corporate Performance
SHAN Dong-ming; MU Xin
2007-01-01
By building a duopoly model based on product differentiation, both of the clean firm's and the dirty firm's performances are studied under the assumptions that consumers have different preferences for the product environmental attributes, and that the product cost increases with the environmental attribute. The analysis results show that under either the case with no environmental regulation or that with a tariff levied on the dirty product, the clean firm would always get more profit. In addition, the stricter the regulation is, the more profit the clean firm would obtain. This can verify that from the view of product differentiation, a firm could improve its corporate competitiveness with environmental management.
Partitioning-based mechanisms under personalized differential privacy
Li, Haoran; Xiong, Li; Ji, Zhanglong; Jiang, Xiaoqian
2017-01-01
Differential privacy has recently emerged in private statistical aggregate analysis as one of the strongest privacy guarantees. A limitation of the model is that it provides the same privacy protection for all individuals in the database. However, it is common that data owners may have different privacy preferences for their data. Consequently, a global differential privacy parameter may provide excessive privacy protection for some users, while insufficient for others. In this paper, we propose two partitioning-based mechanisms, privacy-aware and utility-based partitioning, to handle personalized differential privacy parameters for each individual in a dataset while maximizing utility of the differentially private computation. The privacy-aware partitioning is to minimize the privacy budget waste, while utility-based partitioning is to maximize the utility for a given aggregate analysis. We also develop a t-round partitioning to take full advantage of remaining privacy budgets. Extensive experiments using real datasets show the effectiveness of our partitioning mechanisms. PMID:28932827
Proteinuria: The diagnostic strategy based on urine proteins differentiation
Stojimirović Biljana B.
2004-01-01
Full Text Available Basal glomerular membrane represents mechanical and electrical barrier for passing of the plasma proteins. Mechanical barrier is composed of cylindrical pores and filtration fissure, and negative layer charge in exterior and interior side of basal glomerular membrane, made of heparan sulphate and sialoglicoproteine, provides certain electrical barrier. Diagnostic strategy based on different serum and urine proteins enables the differentiation of various types of proteinuria. Depending on etiology of proteinuria it can be prerenal, renal and postrenal. By analyzing albumin, armicroglobulin, immunoglobulin G and armacroglobulin, together with total protein in urine, it is possible to detect and differentiate causes of prerenal, renal (glomerular, tubular, glomerulo-tubular and postrenal proteinuria. The adequate and early differentiation of proteinuria type is of an immense diagnostic and therapeutic importance.
Efficient image compression scheme based on differential coding
Zhu, Li; Wang, Guoyou; Liu, Ying
2007-11-01
Embedded zerotree (EZW) and Set Partitioning in Hierarchical Trees (SPIHT) coding, introduced by J.M. Shapiro and Amir Said, are very effective and being used in many fields widely. In this study, brief explanation of the principles of SPIHT was first provided, and then, some improvement of SPIHT algorithm according to experiments was introduced. 1) For redundancy among the coefficients in the wavelet region, we propose differential method to reduce it during coding. 2) Meanwhile, based on characteristic of the coefficients' distribution in subband, we adjust sorting pass and optimize differential coding, in order to reduce the redundancy coding in each subband. 3) The image coding result, calculated by certain threshold, shows that through differential coding, the rate of compression get higher, and the quality of reconstructed image have been get raised greatly, when bpp (bit per pixel)=0.5, PSNR (Peak Signal to Noise Ratio) of reconstructed image exceeds that of standard SPIHT by 0.2~0.4db.
A line-based visualization of code evolution
Voinea, SL Lucian; Telea, AC Alexandru; Wijk, van, M.N.
2005-01-01
The source code of software systems changes many times during the system lifecycle. We study how developers can get insight in these changes in order to understand the project context and the product artifacts. For this we propose new techniques for code evolution representation and visualization interaction from a version-centric perspective. Central to our approach is a line-based display of the changing code, where each file version is shown as a column and the horizontal axis shows time. ...
Femtosecond nonlinear polarization evolution based on cascade quadratic nonlinearities.
Liu, X; Ilday, F O; Beckwitt, K; Wise, F W
2000-09-15
We experimentally demonstrate that one can exploit nonlinear phase shifts produced in type I phase-mismatched second-harmonic generation to produce intensity-dependent polarization evolution with 100-fs pulses. An amplitude modulator based on nonlinear polarization rotation provides passive amplitude-modulation depth of up to ~50%. Applications of the amplitude and phase modulations to mode locking of femtosecond bulk and fiber lasers are promising and are discussed.
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
SIGNUM: A Matlab, TIN-based landscape evolution model
Refice, A.; Giachetta, E.; Capolongo, D.
2012-08-01
Several numerical landscape evolution models (LEMs) have been developed to date, and many are available as open source codes. Most are written in efficient programming languages such as Fortran or C, but often require additional code efforts to plug in to more user-friendly data analysis and/or visualization tools to ease interpretation and scientific insight. In this paper, we present an effort to port a common core of accepted physical principles governing landscape evolution directly into a high-level language and data analysis environment such as Matlab. SIGNUM (acronym for Simple Integrated Geomorphological Numerical Model) is an independent and self-contained Matlab, TIN-based landscape evolution model, built to simulate topography development at various space and time scales. SIGNUM is presently capable of simulating hillslope processes such as linear and nonlinear diffusion, fluvial incision into bedrock, spatially varying surface uplift which can be used to simulate changes in base level, thrust and faulting, as well as effects of climate changes. Although based on accepted and well-known processes and algorithms in its present version, it is built with a modular structure, which allows to easily modify and upgrade the simulated physical processes to suite virtually any user needs. The code is conceived as an open-source project, and is thus an ideal tool for both research and didactic purposes, thanks to the high-level nature of the Matlab environment and its popularity among the scientific community. In this paper the simulation code is presented together with some simple examples of surface evolution, and guidelines for development of new modules and algorithms are proposed.
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
Differentiation, Outcomes, Transparency, and Value-based Insurance Design.
Fine, Stuart H
2015-07-01
Practitioners in the surgical and procedural specialties must prepare to differentiate themselves and the performance of their care delivery teams through the use of substantive, objective metrics along with the provision of service guarantees. As purchasers of surgical and procedural services move toward outcomes-focused value-based insurance design (VBID) and purchasing, practitioners must move well beyond branding and process measure-focused "Value Based Purchasing" initiatives and be prepared to compete with transparency- not just regionally, but nationally-based upon objectively established outcomes metrics.
Evolution of cooperation driven by social-welfare-based migration
Li, Yan; Ye, Hang; Zhang, Hong
2016-03-01
Individuals' migration behavior may play a significant role in the evolution of cooperation. In reality, individuals' migration behavior may depend on their perceptions of social welfare. To study the relationship between social-welfare-based migration and the evolution of cooperation, we consider an evolutionary prisoner's dilemma game (PDG) in which an individual's migration depends on social welfare but not on the individual's own payoff. By introducing three important social welfare functions (SWFs) that are commonly studied in social science, we find that social-welfare-based migration can promote cooperation under a wide range of parameter values. In addition, these three SWFs have different effects on cooperation, especially through the different spatial patterns formed by migration. Because the relative efficiency of the three SWFs will change if the parameter values are changed, we cannot determine which SWF is optimal for supporting cooperation. We also show that memory capacity, which is needed to evaluate individual welfare, may affect cooperation levels in opposite directions under different SWFs. Our work should be helpful for understanding the evolution of human cooperation and bridging the chasm between studies of social preferences and studies of social cooperation.
Automatic relational database compression scheme design based on swarm evolution
HU Tian-lei; CHEN Gang; LI Xiao-yan; DONG Jin-xiang
2006-01-01
Compression is an intuitive way to boost the performance of a database system. However, compared with other physical database design techniques, compression consumes large amount of CPU power. There is a trade-off between the reduction of disk access and the overhead of CPU processing. Automatic design and adaptive administration of database systems are widely demanded, and the automatic selection of compression schema to compromise the trade-off is very important. In this paper,we present a model with novel techniques to integrate a rapidly convergent agent-based evolution framework, i.e. the SWAF (SWarm Algorithm Framework), into adaptive attribute compression for relational database. The model evolutionally consults statistics of CPU load and IO bandwidth to select compression schemas considering both aspects of the trade-off. We have implemented a prototype model on Oscar RDBMS with experiments highlighting the correctness and efficiency of our techniques.
Structural Identification and Validation in Stochastic Differential Equation based Models
Møller, Jan Kloppenborg; Carstensen, Jacob; Madsen, Henrik
2011-01-01
Stochastic differential equations (SDEs) for ecosystem modelling have attracted increasing attention during recent years. The modelling has mostly been through simulation based experiments. Estimation of parameters in SDEs is, however, possible by combining Kalman filter and likelihood techniques...... as a function of the state variables and global radiation. Further improvements of both the drift and the diffusion term are achieved by comparing simulated densities and data....
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 .
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.
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.
Saturation Detection-Based Blocking Scheme for Transformer Differential Protection
Byung Eun Lee
2014-07-01
Full Text Available This paper describes a current differential relay for transformer protection that operates in conjunction with a core saturation detection-based blocking algorithm. The differential current for the magnetic inrush or over-excitation has a point of inflection at the start and end of each saturation period of the transformer core. At these instants, discontinuities arise in the first-difference function of the differential current. The second- and third-difference functions convert the points of inflection into pulses, the magnitudes of which are large enough to detect core saturation. The blocking signal is activated if the third-difference of the differential current is larger than the threshold and is maintained for one cycle. In addition, a method to discriminate between transformer saturation and current transformer (CT saturation is included. The performance of the proposed blocking scheme was compared with that of a conventional harmonic blocking method. The test results indicate that the proposed scheme successfully discriminates internal faults even with CT saturation from the magnetic inrush, over-excitation, and external faults with CT saturation, and can significantly reduce the operating time delay of the relay.
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.
Battery parameterisation based on differential evolution via a boundary evolution strategy
Yang, Guangya
2013-01-01
Attention has been given to the battery modelling in the electric engineering field following the current development of renewable energy and electrification of transportation. The establishment of the equivalent circuit model of the battery requires data preparation and parameterisation. Besides...
Battery parameterisation based on differential evolution via a boundary evolution strategy
Yang, Guangya
2013-01-01
. The method can parameterise the model without extensive data preparation. In addition, the approach can also estimate the initial SOC and the available capacity. The efficiency of the approach is verified through two battery packs, one is an 8-cell battery module and one from an electrical vehicle.......Attention has been given to the battery modelling in the electric engineering field following the current development of renewable energy and electrification of transportation. The establishment of the equivalent circuit model of the battery requires data preparation and parameterisation. Besides......, as the equivalent circuit model is an abstract map of the battery electric characteristics, the determination of the possible ranges of parameters can be a challenging task. In this paper, an efficient yet easy to implement method is proposed to parameterise the equivalent circuit model of batteries utilising...
The Evolution of Reputation-Based Cooperation in Regular Networks
Tatsuya Sasaki
2017-01-01
Full Text Available Despite recent advances in reputation technologies, it is not clear how reputation systems can affect human cooperation in social networks. Although it is known that two of the major mechanisms in the evolution of cooperation are spatial selection and reputation-based reciprocity, theoretical study of the interplay between both mechanisms remains almost uncharted. Here, we present a new individual-based model for the evolution of reciprocal cooperation between reputation and networks. We comparatively analyze four of the leading moral assessment rules—shunning, image scoring, stern judging, and simple standing—and base the model on the giving game in regular networks for Cooperators, Defectors, and Discriminators. Discriminators rely on a proper moral assessment rule. By using individual-based models, we show that the four assessment rules are differently characterized in terms of how cooperation evolves, depending on the benefit-to-cost ratio, the network-node degree, and the observation and error conditions. Our findings show that the most tolerant rule—simple standing—is the most robust among the four assessment rules in promoting cooperation in regular networks.
A random network based, node attraction facilitated network evolution method
WenJun Zhang
2016-03-01
Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.
Content-based microarray search using differential expression profiles
Thathoo Rahul
2010-12-01
Full Text Available Abstract Background With the expansion of public repositories such as the Gene Expression Omnibus (GEO, we are rapidly cataloging cellular transcriptional responses to diverse experimental conditions. Methods that query these repositories based on gene expression content, rather than textual annotations, may enable more effective experiment retrieval as well as the discovery of novel associations between drugs, diseases, and other perturbations. Results We develop methods to retrieve gene expression experiments that differentially express the same transcriptional programs as a query experiment. Avoiding thresholds, we generate differential expression profiles that include a score for each gene measured in an experiment. We use existing and novel dimension reduction and correlation measures to rank relevant experiments in an entirely data-driven manner, allowing emergent features of the data to drive the results. A combination of matrix decomposition and p-weighted Pearson correlation proves the most suitable for comparing differential expression profiles. We apply this method to index all GEO DataSets, and demonstrate the utility of our approach by identifying pathways and conditions relevant to transcription factors Nanog and FoxO3. Conclusions Content-based gene expression search generates relevant hypotheses for biological inquiry. Experiments across platforms, tissue types, and protocols inform the analysis of new datasets.
Genetic-evolution-based optimization methods for engineering design
Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.
1990-01-01
This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.
River Network Evolution Based on Fluid-Erosion Model
2010-01-01
A new landscape evolution model is proposed which is composed of the shallow water equations for the fluid above the sediment and the mass conservation equation of the sediment. Numerical simulations of the formation of landscape and river network are carried out based on these equations. It is shown that steady patterns of river network are formed for the initial inclinations of slopes within 0.00005 and 0.005. The fractal dimensions of the river network and the exponent of Hack's law are ob...
Genetic-evolution-based optimization methods for engineering design
Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.
1990-01-01
This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.
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.
Differential geometry based solvation model I: Eulerian formulation.
Chen, Zhan; Baker, Nathan A; Wei, G W
2010-11-01
This paper presents a differential geometry based model for the analysis and computation of the equilibrium property of solvation. Differential geometry theory of surfaces is utilized to define and construct smooth interfaces with good stability and differentiability for use in characterizing the solvent-solute boundaries and in generating continuous dielectric functions across the computational domain. A total free energy functional is constructed to couple polar and nonpolar contributions to the salvation process. Geometric measure theory is employed to rigorously convert a Lagrangian formulation of the surface energy into an Eulerian formulation so as to bring all energy terms into an equal footing. By minimizing the total free energy functional, we derive coupled generalized Poisson-Boltzmann equation (GPBE) and generalized geometric flow equation (GGFE) for the electrostatic potential and the construction of realistic solvent-solute boundaries, respectively. By solving the coupled GPBE and GGFE, we obtain the electrostatic potential, the solvent-solute boundary profile, and the smooth dielectric function, and thereby improve the accuracy and stability of implicit solvation calculations. We also design efficient second order numerical schemes for the solution of the GPBE and GGFE. Matrix resulted from the discretization of the GPBE is accelerated with appropriate preconditioners. An alternative direct implicit (ADI) scheme is designed to improve the stability of solving the GGFE. Two iterative approaches are designed to solve the coupled system of nonlinear partial differential equations. Extensive numerical experiments are designed to validate the present theoretical model, test computational methods, and optimize numerical algorithms. Example solvation analysis of both small compounds and proteins are carried out to further demonstrate the accuracy, stability, efficiency and robustness of the present new model and numerical approaches. Comparison is given to
Stochastic Differential Equation-Based Flexible Software Reliability Growth Model
P. K. Kapur
2009-01-01
Full Text Available Several software reliability growth models (SRGMs have been developed by software developers in tracking and measuring the growth of reliability. As the size of software system is large and the number of faults detected during the testing phase becomes large, so the change of the number of faults that are detected and removed through each debugging becomes sufficiently small compared with the initial fault content at the beginning of the testing phase. In such a situation, we can model the software fault detection process as a stochastic process with continuous state space. In this paper, we propose a new software reliability growth model based on Itô type of stochastic differential equation. We consider an SDE-based generalized Erlang model with logistic error detection function. The model is estimated and validated on real-life data sets cited in literature to show its flexibility. The proposed model integrated with the concept of stochastic differential equation performs comparatively better than the existing NHPP-based models.
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
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.
Random number generation based on digital differential chaos
Zidan, Mohammed A.
2012-07-29
In this paper, we present a fully digital differential chaos based random number generator. The output of the digital circuit is proved to be chaotic by calculating the output time series maximum Lyapunov exponent. We introduce a new post processing technique to improve the distribution and statistical properties of the generated data. The post-processed output passes the NIST Sp. 800-22 statistical tests. The system is written in Verilog VHDL and realized on Xilinx Virtex® FPGA. The generator can fit into a very small area and have a maximum throughput of 2.1 Gb/s.
Differential alkylation-based redox proteomics - Lessons learnt
Wojdyla, Katarzyna; Rogowska-Wrzesinska, Adelina
2015-01-01
is a critical evaluation of differential alkylation-based strategies for the analysis of S-nitrosylation and S-sulfenylation. The aim is to assess the current status and to provide insights for future directions in the dynamically evolving field of redox proteomics. To achieve that we collected 35 original......, including the amount of starting material required for analysis. The results of this meta-analysis are the core of this review, complemented by issues related to biological models and sample preparation in redox proteomics, including conditions for free thiol blocking and labelling of target cysteine...
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.
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.
BASE-9: Bayesian Analysis for Stellar Evolution with nine variables
Robinson, Elliot; von Hippel, Ted; Stein, Nathan; Stenning, David; Wagner-Kaiser, Rachel; Si, Shijing; van Dyk, David
2016-08-01
The BASE-9 (Bayesian Analysis for Stellar Evolution with nine variables) software suite recovers star cluster and stellar parameters from photometry and is useful for analyzing single-age, single-metallicity star clusters, binaries, or single stars, and for simulating such systems. BASE-9 uses a Markov chain Monte Carlo (MCMC) technique along with brute force numerical integration to estimate the posterior probability distribution for the age, metallicity, helium abundance, distance modulus, line-of-sight absorption, and parameters of the initial-final mass relation (IFMR) for a cluster, and for the primary mass, secondary mass (if a binary), and cluster probability for every potential cluster member. The MCMC technique is used for the cluster quantities (the first six items listed above) and numerical integration is used for the stellar quantities (the last three items in the above list).
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.
Parameter optimization in differential geometry based solvation models.
Wang, Bao; Wei, G W
2015-10-01
Differential geometry (DG) based solvation models are a new class of variational implicit solvent approaches that are able to avoid unphysical solvent-solute boundary definitions and associated geometric singularities, and dynamically couple polar and non-polar interactions in a self-consistent framework. Our earlier study indicates that DG based non-polar solvation model outperforms other methods in non-polar solvation energy predictions. However, the DG based full solvation model has not shown its superiority in solvation analysis, due to its difficulty in parametrization, which must ensure the stability of the solution of strongly coupled nonlinear Laplace-Beltrami and Poisson-Boltzmann equations. In this work, we introduce new parameter learning algorithms based on perturbation and convex optimization theories to stabilize the numerical solution and thus achieve an optimal parametrization of the DG based solvation models. An interesting feature of the present DG based solvation model is that it provides accurate solvation free energy predictions for both polar and non-polar molecules in a unified formulation. Extensive numerical experiment demonstrates that the present DG based solvation model delivers some of the most accurate predictions of the solvation free energies for a large number of molecules.
Yi-Fei Pu
2013-01-01
Full Text Available The traditional integer-order partial differential equation-based image denoising approaches often blur the edge and complex texture detail; thus, their denoising effects for texture image are not very good. To solve the problem, a fractional partial differential equation-based denoising model for texture image is proposed, which applies a novel mathematical method—fractional calculus to image processing from the view of system evolution. We know from previous studies that fractional-order calculus has some unique properties comparing to integer-order differential calculus that it can nonlinearly enhance complex texture detail during the digital image processing. The goal of the proposed model is to overcome the problems mentioned above by using the properties of fractional differential calculus. It extended traditional integer-order equation to a fractional order and proposed the fractional Green’s formula and the fractional Euler-Lagrange formula for two-dimensional image processing, and then a fractional partial differential equation based denoising model was proposed. The experimental results prove that the abilities of the proposed denoising model to preserve the high-frequency edge and complex texture information are obviously superior to those of traditional integral based algorithms, especially for texture detail rich images.
Molecular Phylogenetic: Organism Taxonomy Method Based on Evolution History
N.L.P Indi Dharmayanti
2011-03-01
Full Text Available Phylogenetic is described as taxonomy classification of an organism based on its evolution history namely its phylogeny and as a part of systematic science that has objective to determine phylogeny of organism according to its characteristic. Phylogenetic analysis from amino acid and protein usually became important area in sequence analysis. Phylogenetic analysis can be used to follow the rapid change of a species such as virus. The phylogenetic evolution tree is a two dimensional of a species graphic that shows relationship among organisms or particularly among their gene sequences. The sequence separation are referred as taxa (singular taxon that is defined as phylogenetically distinct units on the tree. The tree consists of outer branches or leaves that represents taxa and nodes and branch represent correlation among taxa. When the nucleotide sequence from two different organism are similar, they were inferred to be descended from common ancestor. There were three methods which were used in phylogenetic, namely (1 Maximum parsimony, (2 Distance, and (3 Maximum likehoood. Those methods generally are applied to construct the evolutionary tree or the best tree for determine sequence variation in group. Every method is usually used for different analysis and data.
A UML-based metamodel for software evolution process
Jiang, Zuo; Zhou, Wei-Hong; Fu, Zhi-Tao; Xiong, Shun-Qing
2014-04-01
A software evolution process is a set of interrelated software processes under which the corresponding software is evolving. An object-oriented software evolution process meta-model (OO-EPMM), abstract syntax and formal OCL constraint of meta-model are presented in this paper. OO-EPMM can not only represent software development process, but also represent software evolution.
Monteagudo, S.M., E-mail: sm.monteagudo@alumnos.upm.es [Departamento de Ingeniería Civil: Construcción, Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Madrid 28040 (Spain); Moragues, A., E-mail: amoragues@caminos.upm.es [Departamento de Ingeniería Civil: Construcción, Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Madrid 28040 (Spain); Gálvez, J.C., E-mail: jaime.galvez@upm.es [Departamento de Ingeniería Civil: Construcción, Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Madrid 28040 (Spain); Casati, M.J., E-mail: mariajesus.casati@upm.es [Departamento de Vehículos Aeroespaciales, Escuela de Ingeniería Aeronáutica, Universidad Politécnica de Madrid (Spain); Reyes, E., E-mail: encarnacion.reyes@upm.es [Departamento de Ingeniería Civil: Construcción, Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Madrid 28040 (Spain)
2014-09-20
Highlights: • A proposal of hydration degree calculation for blended cement pastes is presented. • The method is based both on the contributions of various authors and on DTA–TG results. • Paste and mortar specimens with BFS, FA and SF mineral admixtures were used. • The evaluation of CH gives information on hydration and pozzolanic reactions. • The assessment of α provides an insight into future strength evolution. - Abstract: The degree of hydration assessment of cement paste from differential thermal and thermogravimetric analysis data has been performed by several authors that have offered a number of proposals for technical application to blended cements. In this paper, two calculation methods are studied in detail. Then, a proposal of the degree of hydration calculation for blended cements, based on the analysis of experimental results of DTA–TG, is presented. The proposed method combines the contributions of the authors and allows straightforward calculation of the degree of hydration from the experimental results. Validation of the methodology was performed by macroscopic and microstructural tests through paste and mortar specimens with blast furnace slag, flying ash and silica fume mineral admixtures bei(g)ng used. Tests of scanning electron microscopy with an energy dispersive analyser on paste specimens, and of mechanical strength on mortar specimens with the same percentages of substitution, were performed. They showed good agreement with the information derived from the differential thermal and thermogravimetric analysis data.
Differential geometry based solvation model. III. Quantum formulation.
Chen, Zhan; Wei, Guo-Wei
2011-11-21
Solvation is of fundamental importance to biomolecular systems. Implicit solvent models, particularly those based on the Poisson-Boltzmann equation for electrostatic analysis, are established approaches for solvation analysis. However, ad hoc solvent-solute interfaces are commonly used in the implicit solvent theory. Recently, we have introduced differential geometry based solvation models which allow the solvent-solute interface to be determined by the variation of a total free energy functional. Atomic fixed partial charges (point charges) are used in our earlier models, which depends on existing molecular mechanical force field software packages for partial charge assignments. As most force field models are parameterized for a certain class of molecules or materials, the use of partial charges limits the accuracy and applicability of our earlier models. Moreover, fixed partial charges do not account for the charge rearrangement during the solvation process. The present work proposes a differential geometry based multiscale solvation model which makes use of the electron density computed directly from the quantum mechanical principle. To this end, we construct a new multiscale total energy functional which consists of not only polar and nonpolar solvation contributions, but also the electronic kinetic and potential energies. By using the Euler-Lagrange variation, we derive a system of three coupled governing equations, i.e., the generalized Poisson-Boltzmann equation for the electrostatic potential, the generalized Laplace-Beltrami equation for the solvent-solute boundary, and the Kohn-Sham equations for the electronic structure. We develop an iterative procedure to solve three coupled equations and to minimize the solvation free energy. The present multiscale model is numerically validated for its stability, consistency and accuracy, and is applied to a few sets of molecules, including a case which is difficult for existing solvation models. Comparison is made
Zhabitskaya, Evgeniya; Zemlyanaya, Elena; Kiselev, Mikhail; Gruzinov, Andrey
2016-02-01
In this work we use an Asynchronous Differential Evolution (ADE) method to estimate parameters of the Separated Form Factor (SFF) model which is used to investigate a structure of drug delivery Phospholipid Transport Nano System (PTNS) unilamellar vesicles by experimental small angle synchrotron X-ray scattering spectra (SAXS). We compare the efficiency of different optimizing procedures (OP) for the search for the SFF-model parameters. It is shown that the probability to find the global solution of this problem by ADE-methods is significantly higher than that by either Nelder-Mead method or a Quasi-Newton method with Davidon-Fletcher-Powell formula. The parallel realization of ADE accelerates the calculations significantly. The speed-up obtained by the parallel realization of ADE and results of the model are presented. The work has been performed under the grant of Russian Scientific Foundation (project No 14-12-00516)
Zhabitskaya Evgeniya
2016-01-01
Full Text Available In this work we use an Asynchronous Differential Evolution (ADE method to estimate parameters of the Separated Form Factor (SFF model which is used to investigate a structure of drug delivery Phospholipid Transport Nano System (PTNS unilamellar vesicles by experimental small angle synchrotron X-ray scattering spectra (SAXS. We compare the efficiency of different optimizing procedures (OP for the search for the SFF-model parameters. It is shown that the probability to find the global solution of this problem by ADE-methods is significantly higher than that by either Nelder-Mead method or a Quasi-Newton method with Davidon-Fletcher-Powell formula. The parallel realization of ADE accelerates the calculations significantly. The speed-up obtained by the parallel realization of ADE and results of the model are presented.
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.
Preparing Biology Teachers to Teach Evolution in a Project-Based Approach
Cook, Kristin; Buck, Gayle; Park Rogers, Meredith
2012-01-01
This study investigates a project-based learning (PBL) approach to teaching evolution to inform efforts in teacher preparation. Data analysis of a secondary biology educator teaching evolution through a PBL approach illuminated: (1) active student voice, which allowed students to reflect on their positioning on evolution and consider multiple…
Murat Osmanoglu
2013-01-01
Full Text Available We have considered linear partial differential algebraic equations (LPDAEs of the form , which has at least one singular matrix of . We have first introduced a uniform differential time index and a differential space index. The initial conditions and boundary conditions of the given system cannot be prescribed for all components of the solution vector here. To overcome this, we introduced these indexes. Furthermore, differential transform method has been given to solve LPDAEs. We have applied this method to a test problem, and numerical solution of the problem has been compared with analytical solution.
Agarwal, Naman; Yoon, Jiho; Garcia-Caurel, Enric; Novikova, Tatiana; Vanel, Jean-Charles; Pierangelo, Angelo; Bykov, Alexander; Popov, Alexey; Meglinski, Igor; Ossikovski, Razvigor
2015-12-01
We show, through visible-range Mueller polarimetry, as well as numerical simulations, that the depolarization in a homogeneous turbid medium consisting of submicron spherical particles follows a parabolic law with the path-length traveled by light through the medium. This result is in full agreement with the phenomenological theory of the fluctuating medium within the framework of the differential Mueller matrix formalism. We further found that the standard deviations of the fluctuating elementary polarization properties of the medium depend linearly on the concentration of particles. These findings are believed to be useful for the phenomenological interpretation of polarimetric experiments, with special emphasis on biomedical applications.
Optimal control of complex networks based on matrix differentiation
Li, Guoqi; Ding, Jie; Wen, Changyun; Pei, Jing
2016-09-01
Finding the key node set to be connected to external control sources so as to minimize the energy for controlling a complex network, known as the minimum-energy control problem, is of critical importance but remains open. We address this critical problem where matrix differentiation is involved. To this end, the differentiation of energy/cost function with respect to the input matrix is obtained based on tensor analysis, and the Hessian matrix is compressed from a fourth-order tensor. Normalized projected gradient method (NPGM) normalized projected trust-region method (NPTM) are proposed with established convergence property. We show that NPGM is more computationally efficient than NPTM. Simulation results demonstrate satisfactory performance of the algorithms, and reveal important insights as well. Two interesting phenomena are observed. One is that the key node set tends to divide elementary paths equally. The other is that the low-degree nodes may be more important than hubs from a control point of view, indicating that controlling hub nodes does not help to lower the control energy. These results suggest a way of achieving optimal control of complex networks, and provide meaningful insights for future researches.
From lanosterol to cholesterol: Structural evolution and differential effects on lipid bilayers
Miao, Ling; Nielsen, Morten; Thewalt, J.
2002-01-01
Cholesterol is an important molecular component of the plasma membranes of mammalian cells. Its precursor in the sterol biosynthetic pathway, lanosterol, has been argued by Konrad Bloch (Bloch, K. 1965. Science. 150:19-28; 1983. CRC Crit Rev. Biochem. 14:47-92; 1994. Blonds in Venetian Paintings......-bilayer membranes. By using deuterium NMR spectroscopy on multilamellar lipid-sterol systems in combination with Monte Carlo simulations of microscopic models of lipid-sterol interactions, we demonstrate that the evolution in the molecular chemistry from lanosterol to cholesterol is manifested in the model lipid-sterol...... membranes by an increase in the ability of the sterols to promote and stabilize a particular membrane phase, the liquid-ordered phase, and to induce collective order in the acyl-chain conformations of lipid molecules. We also discuss the biological relevance of our results, in particular in the context...
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.
Mannakee, Brian K; Gutenkunst, Ryan N
2016-07-01
The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.
Brian K Mannakee
2016-07-01
Full Text Available The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.
Detecting Differential Rotation and Starspot Evolution on the M dwarf GJ 1243 with Kepler
Davenport, James R A; Hawley, Suzanne L
2015-01-01
We present an analysis of the starspots on the active M4 dwarf GJ 1243, using four years of time series photometry from Kepler. A rapid $P = 0.592596\\pm0.00021$ day rotation period is measured due to the $\\sim$2.2\\% starspot-induced flux modulations in the light curve. We first use a light curve modeling approach, using a Monte Carlo Markov Chain sampler to solve for the longitudes and radii of the two spots within 5-day windows of data. Within each window of time the starspots are assumed to be unchanging. Only a weak constraint on the starspot latitudes can be implied from our modeling. The primary spot is found to be very stable over many years. A secondary spot feature is present in three portions of the light curve, decays on 100-500 day timescales, and moves in longitude over time. We interpret this longitude shearing as the signature of differential rotation. Using our models we measure an average shear between the starspots of 0.0047 rad day$^{-1}$, which corresponds to a differential rotation rate of...
Differentiation Mechanism and Evolution of High-level Magma Chamber at Xiangshan,China
无
1992-01-01
The calc-alkaline volcanic magmas,which formed the Mesozoic uraniferous volcanic complex of Xiangshan,resulted from partial melting of the mixture of lower crust and enriched mantle with a high mixing proportion in a specific tectonic setting such as active continental margin or ocean-continent collision zone.The preliminary concentrations of Uand Th occur in low-degree par-tial melts.Only small part of these melts was rapidly extracted and erupted and most intruded into the high-level magma chamber(depth:12-13 km) of the compressed upper lithosphere ,in which occurred a strong differentiation which would resulted in strong preconcentrations of the high-hygromagmaphile elements U and Th associated with strong depletion of the 3-d transition ele-ments Ti,Sc,Co,Zr,etc.At the final stage of subduction of the West-Pacific-Kula plate towards the Asian continental plate,the regional tectonic environment was transformed from a compressive in-to a tensional setting.The strongly differentiated,U(and Th) enriched silicic alkalic magmas in high level magma chamber extensively erupted,extruded and intruded.The hydrothermal fluids released as a result of late volcano-degassing and dewatering during crystallization-solidification of magmas,re-sulted in the remobilization,leaching,migration and reconcentration of uranium ,which had been preconcentrated in volcanic rocks.Therefore,specific regional petrogeochemical criteria are expected for the uraniferous volcanic series.
Jans, Lennart B.O.; Huysse, Wouter C.; Verstraete, Koenraad L. [Ghent University Hospital, Department of Radiology and Medical Imaging, Ghent (Belgium); Jaremko, Jacob L.; Ditchfield, Michael [University of Melbourne Royal Children' s Hospital, Department of Medical Imaging, Melbourne, Vic (Australia)
2011-06-15
To determine if MRI (magnetic resonance imaging) of the femoral condyles in children can differentiate variations in ossification from osteochondritis dissecans (OCD). MRI studies of the knee of 315 patients demonstrated ossification defects of the femoral condyles involving the subchondral bone plate. MRI features categorized the defects as ossification variability (N = 150) or OCD (N = 165). Both groups were compared for age, residual physeal cartilage, site, configuration, 'lesion angle' and associated findings. (a) Ossification variability did not occur in girls >10 year. and boys >13 year., OCD did not occur in children younger than 8 year. (b) Ossification variability was not seen in patients with 10% or less residual physeal cartilage, OCD was rare in patients with 30% or greater residual physeal cartilage. (c) Ossification variability was located in the posterior third of the femoral condyle, OCD occurred most commonly in the middle third. (d) Intracondylar extension was seen in OCD and not in ossification variability. (e) Perilesional oedema was very common with OCD and absent with ossification variability. (f) Lesion angle <105 was a feature of ossification variability. MRI may help differentiate variations in ossification of the femoral condyles from OCD. (orig.)
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.
Bing-Bing LIU; Lars OPGENOORTH; Georg MIEHE; Dong-Yuan ZHANG; Dong-Shi WAN; Chang-Ming ZHAO; Dong-Rui JIA
2013-01-01
Parallel evolution provides an excellent framework to infer the genetic bases of adaptive traits and understand the importance of natural selection in shaping current biodiversity.The upper leaves of the "glasshouse plants" transform into translucent bracts that show numerous adaptions in alpine habitats.It remains unknown whether similar molecular changes occur under the parallel bract evolution of different "glasshouse" species.In this study,we compared the results on phenotypic and physiological differences and presented the results of cDNA-AFLP analyses of transcriptional changes between translucent bracts and normal leaves in Rheum alexandrae.We also examined the homologous candidate genes with the same expression changes between this species and another "glasshouse" species,R.nobile.We found that bracts ofR.alexandrae are similar to those ofR.nobile in anatomical features:chloroplasts have degenerated and chlorophyll contents are greatly reduced,which suggests that foliar photosynthetic functions in bracts of both species have been reduced or totally altered.Among the 6000 transcript-derived fragments (TDFs) in bracts and leaves of R.alexandrae,420 (7％) were differentially expressed (up-or downregulated) between bracts and normal leaves.There were a total of 13 homologous TDFs with the same expression changes between R.alexandrae and the previously studied R.nobile.Except for the two that were not functionally annotated,eight of the homologous TDFs were found to be involved in stress and defense responses whereas the other three were related to photosynthesis.The up-or downregulation of these candidate genes was highly congruent with anatomical characteristics and adaptive functions of the bracts found for "glasshouse" plants.These findings suggested that the "glasshouse" phenotypes may have common molecular bases underlying their parallel evolution of similar adaptive functions and highlighted the importance of the natural selection in producing such
Ioannidis, P.; Schmitt, J. H. M. M.
2016-10-01
We use high accuracy photometric data obtained with the Kepler satellite to monitor the activity modulations of the Kepler-210 planet host star over a time span of more than four years. Following the phenomenology of the star's light curve in combination with a five spot model, we identify six different so-called spot seasons. A characteristic, which is common in the majority of the seasons, is the persistent appearance of spots in a specific range of longitudes on the stellar surface. The most prominent period of the observed activity modulations is different for each season and appears to evolve following a specific pattern, resembling the changes in the sunspot periods during the solar magnetic cycle. Under the hypothesis that the star exhibits solar-like differential rotation, we suggest differential rotation values of Kepler-210 that are similar to or smaller than that of the Sun. Finally, we estimate spot life times between ~60 days and ~90 days, taking into consideration the evolution of the total covered stellar surface computed from our model.
Ioannidis, P
2016-01-01
We use high accuracy photometric data obtained with the Kepler satellite to monitor the activity modulations of the Kepler-210 planet host star over a time span of more than four years. Following the phenomenology of the star's light curve in combination with a five spot model, we identify six different so-called spot seasons. A characteristic, which is common in the majority of the seasons, is the persistent appearance of spots in a specific range of longitudes on the stellar surface. The most prominent period of the observed activity modulations is different for each season and appears to evolve following a specific pattern, resembling the changes in the sunspot periods during the solar magnetic cycle. Under the hypothesis that the star exhibits solar-like differential rotation, we suggest differential rotation values of Kepler-210 that are similar to or smaller than that of the Sun. Finally, we estimate spot life times between 60 days and 90 days, taking into consideration the evolution of the total covere...
Research of differentiated QoS routing in GMPLS-based IP/WDM networks
Wang, YiYun; Zeng, QingJi; Cao, JunWen
2004-04-01
At this point in technology's evolution, the simplicity, elegance, extensibility, and broad compatibility of the Internet protocol suite has made it the automatic choice for most forms of communication. The attempts at resolution of this apparent dichotomy consist of a collection of technologies and philosophies known as Quality of Service. In an IP network, QoS defines the ability to compensate for traffic characteristics without compromising average throughput. Clearly, optimizing QoS performance for all traffic types on an IP network presents a daunting challenge. To partially address this challenge, several Internet Engineering Task Force groups have been working on standardized approaches for IP-based QoS technologies. The IETF"s approaches fall into four categories: prioritization using differentiated services, reservation using integrated services, label switching using multi-protocol label switching, bandwidth management using the subnet bandwidth manager. Differentiated services classify per-hop behaviors on the basis of a Diffserv code point attached to the type of service byte in each packet"s IP header. This DSCP approach represents a form of soft QoS that rather coarsely classifies services through packet marking. The differentiated QoS routing in GMPLS-based IP/WDM Networks are a promising candidate for the next generation optical Internet networks. By using a unified control plane, such networks make more efficient usage of network resources both at the IP layer and the WDM optical layer. In this paper, we consider prioritized routing of bandwidth-guaranteed Label Switched paths (LSPs) providing service differentiation between classes of high and normal priority traffic. The QoS delay requirements are assumed to be translated into bandwidth and O-E-O conversion requirements. We present a graphical representation of the integrated network state which is different from other conventional graphical representations in that it models the cost of usage of
Carrying Network Accessing Architecture and Strategy Based on Business Differentiation
Yanyan Han
2013-07-01
Full Text Available Due to the abilities of real-time sensing and information sharing, Wireless Sensor Network (WSN has been applied in more and more fields. Basing on the emergence of Internet of Things (IoT, the issue about heterogeneous network integration is becoming more important. We first analyze the new businesses that arise recently for cell phone users as well as the potential effect on carrying network. After that we mainly discuss the influence on traditional carrying network for WSN accessing and taking concurrent businesses as the study case, common access architecture from WSN to carrying network is constructed, which makes use of business differentiation. Furthermore, we propose the idea of tortuous access from WSN to the gateway in the carrying network to avoid congested paths with simulation and verification. Finally, we conclude the possible impacts for the integration of these two networks and present possible solutions.
A shape representation for computer vision based on differential topology.
Blicher, A P
1995-01-01
We describe a shape representation for use in computer vision, after a brief review of shape representation and object recognition in general. Our shape representation is based on graph structures derived from level sets whose characteristics are understood from differential topology, particularly singularity theory. This leads to a representation which is both stable and whose changes under deformation are simple. The latter allows smoothing in the representation domain ('symbolic smoothing'), which in turn can be used for coarse-to-fine strategies, or as a discrete analog of scale space. Essentially the same representation applies to an object embedded in 3-dimensional space as to one in the plane, and likewise for a 3D object and its silhouette. We suggest how this can be used for recognition.
Differential Protection for Distributed Micro-Grid Based on Agent
ZHOU Bin
2013-05-01
Full Text Available The Micro-grid, even though not a replacement of the conventional centralized power transmission grid, plays a very important role in the success of rapid development of renewable energy resources technologies. Due to the facts of decentralization, independence and dynamic of sources within a Micro-grid, a high level automation of protection is a must. Multi-Agent system as a approach to handle distributed system issues has been developed. This paper presents an MAS based differential protection method for distributed micro-grid. The nodes within a micro-grid are divided into primary and backup protection zones. The agents follow predefined rules to take actions to protect the system and isolate the fault when it happens. Furthermore, an algorithm is proposed to achieve high availability in case of Agent itself malfunction. The method is using Matlab for simulation and shows it satisfies relay protection in terms of the selectivity, sensitivity, rapidity and reliability requirements.
The Evolution of Facultative Conformity Based on Similarity
Efferson, Charles; Lalive, Rafael; Cacault, Maria Paula; Kistler, Deborah
2016-01-01
Conformist social learning can have a pronounced impact on the cultural evolution of human societies, and it can shape both the genetic and cultural evolution of human social behavior more broadly. Conformist social learning is beneficial when the social learner and the demonstrators from whom she learns are similar in the sense that the same behavior is optimal for both. Otherwise, the social learner’s optimum is likely to be rare among demonstrators, and conformity is costly. The trade-off between these two situations has figured prominently in the longstanding debate about the evolution of conformity, but the importance of the trade-off can depend critically on the flexibility of one’s social learning strategy. We developed a gene-culture coevolutionary model that allows cognition to encode and process information about the similarity between naive learners and experienced demonstrators. Facultative social learning strategies that condition on perceived similarity evolve under certain circumstances. When this happens, facultative adjustments are often asymmetric. Asymmetric adjustments mean that the tendency to follow the majority when learners perceive demonstrators as similar is stronger than the tendency to follow the minority when learners perceive demonstrators as different. In an associated incentivized experiment, we found that social learners adjusted how they used social information based on perceived similarity, but adjustments were symmetric. The symmetry of adjustments completely eliminated the commonly assumed trade-off between cases in which learners and demonstrators share an optimum versus cases in which they do not. In a second experiment that maximized the potential for social learners to follow their preferred strategies, a few social learners exhibited an inclination to follow the majority. Most, however, did not respond systematically to social information. Additionally, in the complete absence of information about their similarity to
Brüne, Martin
2012-04-17
The diathesis-stress model of psychiatric conditions has recently been challenged by the view that it might be more accurate to speak of 'differential susceptibility' or 'plasticity' genes, rather than one-sidedly focusing on individual vulnerability. That is, the same allelic variation that predisposes to a psychiatric disorder if associated with (developmentally early) environmental adversity may lead to a better-than-average functional outcome in the same domain under thriving (or favourable) environmental conditions. Studies of polymorphic variations of the serotonin transporter gene, the monoamino-oxidase-inhibitor A coding gene or the dopamine D4 receptor gene indicate that the early environment plays a crucial role in the development of favourable versus unfavourable outcomes. Current evidence is limited, however, to establishing a link between genetic variation and behavioural phenotypes. In contrast, little is known about how plasticity may be expressed at the neuroanatomical level as a 'hard-wired' correlate of observable behaviour. The present review article seeks to further strengthen the argument in favour of the differential susceptibility theory by incorporating findings from behavioural and neuroanatomical studies in relation to genetic variation of the oxytocin receptor gene. It is suggested that polymorphic variation at the oxytocin receptor gene (rs2254298) is associated with sociability, amygdala volume and differential risk for psychiatric conditions including autism, depression and anxiety disorder, depending on the quality of early environmental experiences. Seeing genetic variation at the core of developmental plasticity can explain, in contrast to the diathesis-stress perspective, why evolution by natural selection has maintained such 'risk' alleles in the gene pool of a population.
Brüne Martin
2012-04-01
Full Text Available Abstract The diathesis-stress model of psychiatric conditions has recently been challenged by the view that it might be more accurate to speak of 'differential susceptibility' or 'plasticity' genes, rather than one-sidedly focusing on individual vulnerability. That is, the same allelic variation that predisposes to a psychiatric disorder if associated with (developmentally early environmental adversity may lead to a better-than-average functional outcome in the same domain under thriving (or favourable environmental conditions. Studies of polymorphic variations of the serotonin transporter gene, the monoamino-oxidase-inhibitor A coding gene or the dopamine D4 receptor gene indicate that the early environment plays a crucial role in the development of favourable versus unfavourable outcomes. Current evidence is limited, however, to establishing a link between genetic variation and behavioural phenotypes. In contrast, little is known about how plasticity may be expressed at the neuroanatomical level as a 'hard-wired' correlate of observable behaviour. The present review article seeks to further strengthen the argument in favour of the differential susceptibility theory by incorporating findings from behavioural and neuroanatomical studies in relation to genetic variation of the oxytocin receptor gene. It is suggested that polymorphic variation at the oxytocin receptor gene (rs2254298 is associated with sociability, amygdala volume and differential risk for psychiatric conditions including autism, depression and anxiety disorder, depending on the quality of early environmental experiences. Seeing genetic variation at the core of developmental plasticity can explain, in contrast to the diathesis-stress perspective, why evolution by natural selection has maintained such 'risk' alleles in the gene pool of a population. Please see related manuscript: http://www.biomedcentral.com/1741-7015/10/37
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.
A Method for Image Decontamination Based on Partial Differential Equation
Hou Junping
2015-01-01
Full Text Available This paper will introduce the method to apply partial differential equations for the decontamination processing of images. It will establish continuous partial differential mathematical models for image information and use specific solving methods to conduct decontamination processing to images during the process of solving partial differential equations, such as image noise reduction, image denoising and image segmentation. This paper will study the uniqueness of solution for the partial differential equations and the monotonicity that functional constrain has on multipliers by making analysis of the ROF model in the partial differential mathematical model.
Partial differential equations-based segmentation for radiotherapy treatment planning.
Gibou, Frederic; Levy, Doron; Cardenas, Carlos; Liu, Pingyu; Boyer, Arthur
2005-04-01
The purpose of this study is to develop automatic algorithms for the segmentation phase of radiotherapy treatment planning. We develop new image processing techniques that are based on solving a partial diferential equation for the evolution of the curve that identifies the segmented organ. The velocity function is based on the piecewise Mumford-Shah functional. Our method incorporates information about the target organ into classical segmentation algorithms. This information, which is given in terms of a three- dimensional wireframe representation of the organ, serves as an initial guess for the segmentation algorithm. We check the performance of the new algorithm on eight data sets of three diferent organs: rectum, bladder, and kidney. The results of the automatic segmentation were compared with a manual seg- mentation of each data set by radiation oncology faculty and residents. The quality of the automatic segmentation was measured with the k-statistics", and with a count of over- and undersegmented frames, and was shown in most cases to be very close to the manual segmentation of the same data. A typical segmentation of an organ with sixty slices takes less than ten seconds on a Pentium IV laptop.
Differential geometry based solvation model II: Lagrangian formulation.
Chen, Zhan; Baker, Nathan A; Wei, G W
2011-12-01
Solvation is an elementary process in nature and is of paramount importance to more sophisticated chemical, biological and biomolecular processes. The understanding of solvation is an essential prerequisite for the quantitative description and analysis of biomolecular systems. This work presents a Lagrangian formulation of our differential geometry based solvation models. The Lagrangian representation of biomolecular surfaces has a few utilities/advantages. First, it provides an essential basis for biomolecular visualization, surface electrostatic potential map and visual perception of biomolecules. Additionally, it is consistent with the conventional setting of implicit solvent theories and thus, many existing theoretical algorithms and computational software packages can be directly employed. Finally, the Lagrangian representation does not need to resort to artificially enlarged van der Waals radii as often required by the Eulerian representation in solvation analysis. The main goal of the present work is to analyze the connection, similarity and difference between the Eulerian and Lagrangian formalisms of the solvation model. Such analysis is important to the understanding of the differential geometry based solvation model. The present model extends the scaled particle theory of nonpolar solvation model with a solvent-solute interaction potential. The nonpolar solvation model is completed with a Poisson-Boltzmann (PB) theory based polar solvation model. The differential geometry theory of surfaces is employed to provide a natural description of solvent-solute interfaces. The optimization of the total free energy functional, which encompasses the polar and nonpolar contributions, leads to coupled potential driven geometric flow and PB equations. Due to the development of singularities and nonsmooth manifolds in the Lagrangian representation, the resulting potential-driven geometric flow equation is embedded into the Eulerian representation for the purpose of
Bearing diagnostics: A method based on differential geometry
Tian, Ye; Wang, Zili; Lu, Chen; Wang, Zhipeng
2016-12-01
The structures around bearings are complex, and the working environment is variable. These conditions cause the collected vibration signals to become nonlinear, non-stationary, and chaotic characteristics that make noise reduction, feature extraction, fault diagnosis, and health assessment significantly challenging. Thus, a set of differential geometry-based methods with superiorities in nonlinear analysis is presented in this study. For noise reduction, the Local Projection method is modified by both selecting the neighborhood radius based on empirical mode decomposition and determining noise subspace constrained by neighborhood distribution information. For feature extraction, Hessian locally linear embedding is introduced to acquire manifold features from the manifold topological structures, and singular values of eigenmatrices as well as several specific frequency amplitudes in spectrograms are extracted subsequently to reduce the complexity of the manifold features. For fault diagnosis, information geometry-based support vector machine is applied to classify the fault states. For health assessment, the manifold distance is employed to represent the health information; the Gaussian mixture model is utilized to calculate the confidence values, which directly reflect the health status. Case studies on Lorenz signals and vibration datasets of bearings demonstrate the effectiveness of the proposed methods.
Ricardo D’Oliveira Albanus
2014-01-01
Full Text Available Chemoreception is among the most important sensory modalities in animals. Organisms use the ability to perceive chemical compounds in all major ecological activities. Recent studies have allowed the characterization of chemoreceptor gene families. These genes present strikingly high variability in copy numbers and pseudogenization degrees among different species, but the mechanisms underlying their evolution are not fully understood. We have analyzed the functional networks of these genes, their orthologs distribution, and performed phylogenetic analyses in order to investigate their evolutionary dynamics. We have modeled the chemosensory networks and compared the evolutionary constraints of their genes in Mus musculus, Homo sapiens, and Rattus norvegicus. We have observed significant differences regarding the constraints on the orthologous groups and network topologies of chemoreceptors and signal transduction machinery. Our findings suggest that chemosensory receptor genes are less constrained than their signal transducing machinery, resulting in greater receptor diversity and conservation of information processing pathways. More importantly, we have observed significant differences among the receptors themselves, suggesting that olfactory and bitter taste receptors are more conserved than vomeronasal receptors.
无
2007-01-01
Control parameters of original difierential evolution(DE)are kept fixed throughout the entire evolutionary process.However,it is not an easy task to properly set control parameters in DE for difierent optimization problems.According to the relative position of two difierent individual vectors selected to generate a difference vector in the searching place,a self-adapting strategy for the scale factor F of the difference vector is proposed.In terms of the convergence status of the target vector in the current population,a self-adapting crossover probability constant CR strategy is proposed.Therefore,good target vectors have a lower CR while worse target vectors have a large CR.At the same time,the mutation operator is modified to improve the convergence speed.The performance of these proposed approaches are studied with the use of some benchmark problems and applied to the trajectory planning of a three-joint redundant manipulator.Finally,the experiment results show that the proposed approaches can greatly improve robustness and convergence speed.
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
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.
Structural evolution of differential amino acid effector regulation in plant chorismate mutases.
Westfall, Corey S; Xu, Ang; Jez, Joseph M
2014-10-10
Chorismate mutase converts chorismate into prephenate for aromatic amino acid biosynthesis. To understand the molecular basis of allosteric regulation in the plant chorismate mutases, we analyzed the three Arabidopsis thaliana chorismate mutase isoforms (AtCM1-3) and determined the x-ray crystal structures of AtCM1 in complex with phenylalanine and tyrosine. Functional analyses show a wider range of effector control in the Arabidopsis chorismate mutases than previously reported. AtCM1 is activated by tryptophan with phenylalanine and tyrosine acting as negative effectors; however, tryptophan, cysteine, and histidine activate AtCM3. AtCM2 is a nonallosteric form. The crystal structure of AtCM1 in complex with tyrosine and phenylalanine identifies differences in the effector sites of the allosterically regulated yeast enzyme and the other two Arabidopsis isoforms. Site-directed mutagenesis of residues in the effector site reveals key features leading to differential effector regulation in these enzymes. In AtCM1, mutations of Gly-213 abolish allosteric regulation, as observed in AtCM2. A second effector site position, Gly-149 in AtCM1 and Asp-132 in AtCM3, controls amino acid effector specificity in AtCM1 and AtCM3. Comparisons of chorismate mutases from multiple plants suggest that subtle differences in the effector site are conserved in different lineages and may lead to specialized regulation of this branch point enzyme.
Macagno, Anna L M; Beckers, Oliver M; Moczek, Armin P
2015-11-01
Fecundity is a fundamental determinant of fitness, yet the proximate developmental and physiological mechanisms that enable its often rapid evolution in natural populations are poorly understood. Here, we investigated two populations of the dung beetle Onthophagus taurus that were established in exotic ranges in the early 1970s. These populations are subject to drastically different levels of resource competition in the field, and have diverged dramatically in female fecundity. Specifically, Western Australian O. taurus experience high levels of resource competition, and exhibit greatly elevated reproductive output compared to beetles from the Eastern US, where resource competition is minimal and female fecundity is low. We compared patterns of ovarian maturation, relative investment into and timing of egg production, and potential trade-offs between ovarian investment and the duration of larval development and adult body size between populations representative of both exotic ranges. We found that the rapid divergence in fecundity between exotic populations is associated with striking differences in several aspects of ovarian development: (1) Western Australian females exhibit accelerated ovarian development, (2) produce more eggs, (3) bigger eggs, and (4) start laying eggs earlier compared to their Eastern US counterparts. At the same time, divergence in ovarian maturation patterns occurred alongside changes in (5) larval developmental time, and (6) adult body size, and (7) mass. Western Australian females take longer to complete larval development and, surprisingly, emerge into smaller yet heavier adults than size-matched Eastern US females. We discuss our results in the context of the evolutionary developmental biology of fecundity in exotic populations. © 2015 Wiley Periodicals, Inc.
Optimal Differential Routing based on Finite State Machine Theory
M. S. Krishnamoorthy; Loy, James R.; McDonald, John F.
1999-01-01
Noise margins in high speed digital systems continue to erode. Full differential signal routing provides a mechanism for deferring these effects. This paper proposes a three stage routing process for solving the adjacent placement routing problem of differential signal pairs, and proves that it is optimal. The process views differential pairs as logical nets; routes the logical nets; then bifurcates the result to achieve a physical realization. Finite state machine theory provides the critica...
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.
Arm7 Based Evolution in Vehicle Mobility and Automation
Mr. N. S. Vaidya
2013-09-01
Full Text Available The “Arm7 Based Evolution In Vehicle Mobility And Automation”, various sensor is used to detect various parameters of the vehicle system like Temperature sensor is used to detect engine temperature, Light sensor is used to switch on head light at night automatically, proximity sensor are used for driver as well as driver side seat belt wearing information, figure print sensor are used to open the vehicle as well as start the car. Micro serial data card reader is used here to work as a black box of the vehicle, whenever accident happen it can give all the last information which is stored in to memory card to the investigator, which is easy to detect the cause of accident. The smartness of this vehicle is to tell all the information or sensor output data audibly to the vehicle owner or driver and driver side seat passenger. All the things can be controlled by one system which is ARM7 and information is also displayed on LCD monitor
Quantum repeater based on cavity QED evolutions and coherent light
Gonţa, Denis; van Loock, Peter
2016-05-01
In the framework of cavity QED, we propose a quantum repeater scheme that uses coherent light and chains of atoms coupled to optical cavities. In contrast to conventional repeater schemes, in our scheme there is no need for an explicit use of two-qubit quantum logical gates by exploiting solely the cavity QED evolution. In our previous work (Gonta and van Loock in Phys Rev A 88:052308, 2013), we already proposed a quantum repeater in which the entanglement between two neighboring repeater nodes was distributed using controlled displacements of input coherent light, while the produced low-fidelity entangled pairs were purified using ancillary (four-partite) entangled states. In the present work, the entanglement distribution is realized using a sequence of controlled phase shifts and displacements of input coherent light. Compared to previous coherent-state-based distribution schemes for two-qubit entanglement, our scheme here relies only upon a simple discrimination of two coherent states with opposite signs, which can be performed in a quantum mechanically optimal fashion via a beam splitter and two on-off detectors. For the entanglement purification, we employ a method that avoids the use of extra entangled ancilla states. Our repeater scheme exhibits reasonable fidelities and repeater rates providing an attractive platform for long-distance quantum communication.
Evolution of complexity in a resource-based model
Fernández, Lenin; Campos, Paulo R. A.
2017-02-01
Through a resource-based modelling the evolution of organismal complexity is studied. In the model, the cells are characterized by their metabolic rates which, together with the availability of resource, determine the rate at which they divide. The population is structured in groups. Groups are also autonomous entities regarding reproduction and propagation, and so they correspond to a higher biological organization level. The model assumes reproductive altruism as there exists a fitness transfer from the cell level to the group level. Reproductive altruism comes about by inflicting a higher energetic cost to cells belonging to larger groups. On the other hand, larger groups are less prone to extinction. The strength of this benefit arising from group augmentation can be tuned by the synergistic parameter γ. Through extensive computer simulations we make a thorough exploration of the parameter space to find out the domain in which the formation of larger groups is allowed. We show that formation of small groups can be obtained for a low level of synergy. Larger group sizes can only be attained as synergistic interactions surpass a given level of strength. Although the total resource influx rate plays a key role in determining the number of groups coexisting at the equilibrium, its function on driving group size is minor. On the other hand, how the resource is seized by the groups matters.
Phylogeny and evolution of Cervidae based on complete mitochondrial genomes.
Zhang, W-Q; Zhang, M-H
2012-03-14
Mitochondrial DNA sequences can be used to estimate phylogenetic relationships among animal taxa and for molecular phylogenetic evolution analysis. With the development of sequencing technology, more and more mitochondrial sequences have been made available in public databases, including whole mitochondrial DNA sequences. These data have been used for phylogenetic analysis of animal species, and for studies of evolutionary processes. We made phylogenetic analyses of 19 species of Cervidae, with Bos taurus as the outgroup. We used neighbor joining, maximum likelihood, maximum parsimony, and Bayesian inference methods on whole mitochondrial genome sequences. The consensus phylogenetic trees supported monophyly of the family Cervidae; it was divided into two subfamilies, Plesiometacarpalia and Telemetacarpalia, and four tribes, Cervinae, Muntiacinae, Hydropotinae, and Odocoileinae. The divergence times in these families were estimated by phylogenetic analysis using the Bayesian method with a relaxed molecular clock method; the results were consistent with those of previous studies. We concluded that the evolutionary structure of the family Cervidae can be reconstructed by phylogenetic analysis based on whole mitochondrial genomes; this method could be used broadly in phylogenetic evolutionary analysis of animal taxa.
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.
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
Rolling Force Prediction in Heavy Plate Rolling Based on Uniform Differential Neural Network
Fei Zhang
2016-01-01
Full Text Available Accurate prediction of the rolling force is critical to assuring the quality of the final product in steel manufacturing. Exit thickness of plate for each pass is calculated from roll gap, mill spring, and predicted roll force. Ideal pass scheduling is dependent on a precise prediction of the roll force in each pass. This paper will introduce a concept that allows obtaining the material model parameters directly from the rolling process on an industrial scale by the uniform differential neural network. On the basis of the characteristics that the uniform distribution can fully characterize the solution space and enhance the diversity of the population, uniformity research on differential evolution operator is made to get improved crossover with uniform distribution. When its original function is transferred with a transfer function, the uniform differential evolution algorithms can quickly solve complex optimization problems. Neural network structure and weights threshold are optimized by uniform differential evolution algorithm, and a uniform differential neural network is formed to improve rolling force prediction accuracy in process control system.
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.
Schreiber-Agus, N; Horner, J.; Torres, R.; Chiu, F C; Depinho, R.A.
1993-01-01
To gain insight into the role of Myc family oncoproteins and their associated protein Max in vertebrate growth and development, we sought to identify homologs in the zebra fish (Brachydanio rerio). A combination of a polymerase chain reaction-based cloning strategy and low-stringency hybridization screening allowed for the isolation of zebra fish c-, N-, and L-myc and max genes; subsequent structural characterization showed a high degree of conservation in regions that encode motifs of known ...
Thoma, Eva C; Maurus, Katja; Wagner, Toni U; Schartl, Manfred
2012-04-01
The generation of defined somatic cell types from pluripotent stem cells represents a promising system for many applications for regenerative therapy or developmental studies. Certain key developmental genes have been shown to be able to influence the fate determination of differentiating stem cells suggesting an alternative differentiation strategy to conventional medium-based methods. Here, we present a system allowing controlled, directed differentiation of embryonic stem cells (ESCs) solely by ectopic expression of single genes. We demonstrate that the myogenic master regulator myoD1 is sufficient to induce formation of skeletal muscle. In contrast to previous studies, our data suggest that myoD1-induced differentiation is independent of additional differentiation-inducing or lineage-promoting signals and occurs even under pluripotency-promoting conditions. Moreover, we demonstrate that single gene-induced differentiation enables the controlled formation of two distinct cell types in parallel. By mixing ES cell lines expressing myoD1 or the neural transcription factor ngn2, respectively, we generated a mixed culture of myocytes and neurons. Our findings provide new insights in the role of key developmental genes during cell fate decisions. Furthermore, this study represents an interesting strategy to obtain mixed cultures of different cells from stem cells, suggesting a valuable tool for cellular development and cell-cell interaction studies.
GPU-based parallel clustered differential pulse code modulation
Wu, Jiaji; Li, Wenze; Kong, Wanqiu
2015-10-01
Hyperspectral remote sensing technology is widely used in marine remote sensing, geological exploration, atmospheric and environmental remote sensing. Owing to the rapid development of hyperspectral remote sensing technology, resolution of hyperspectral image has got a huge boost. Thus data size of hyperspectral image is becoming larger. In order to reduce their saving and transmission cost, lossless compression for hyperspectral image has become an important research topic. In recent years, large numbers of algorithms have been proposed to reduce the redundancy between different spectra. Among of them, the most classical and expansible algorithm is the Clustered Differential Pulse Code Modulation (CDPCM) algorithm. This algorithm contains three parts: first clusters all spectral lines, then trains linear predictors for each band. Secondly, use these predictors to predict pixels, and get the residual image by subtraction between original image and predicted image. Finally, encode the residual image. However, the process of calculating predictors is timecosting. In order to improve the processing speed, we propose a parallel C-DPCM based on CUDA (Compute Unified Device Architecture) with GPU. Recently, general-purpose computing based on GPUs has been greatly developed. The capacity of GPU improves rapidly by increasing the number of processing units and storage control units. CUDA is a parallel computing platform and programming model created by NVIDIA. It gives developers direct access to the virtual instruction set and memory of the parallel computational elements in GPUs. Our core idea is to achieve the calculation of predictors in parallel. By respectively adopting global memory, shared memory and register memory, we finally get a decent speedup.
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.
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.
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.
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.
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.
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.
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.
Evolution based on domain combinations: the case of glutaredoxins
Herrero Enrique
2009-03-01
Full Text Available Abstract Background Protein domains represent the basic units in the evolution of proteins. Domain duplication and shuffling by recombination and fusion, followed by divergence are the most common mechanisms in this process. Such domain fusion and recombination events are predicted to occur only once for a given multidomain architecture. However, other scenarios may be relevant in the evolution of specific proteins, such as convergent evolution of multidomain architectures. With this in mind, we study glutaredoxin (GRX domains, because these domains of approximately one hundred amino acids are widespread in archaea, bacteria and eukaryotes and participate in fusion proteins. GRXs are responsible for the reduction of protein disulfides or glutathione-protein mixed disulfides and are involved in cellular redox regulation, although their specific roles and targets are often unclear. Results In this work we analyze the distribution and evolution of GRX proteins in archaea, bacteria and eukaryotes. We study over one thousand GRX proteins, each containing at least one GRX domain, from hundreds of different organisms and trace the origin and evolution of the GRX domain within the tree of life. Conclusion Our results suggest that single domain GRX proteins of the CGFS and CPYC classes have, each, evolved through duplication and divergence from one initial gene that was present in the last common ancestor of all organisms. Remarkably, we identify a case of convergent evolution in domain architecture that involves the GRX domain. Two independent recombination events of a TRX domain to a GRX domain are likely to have occurred, which is an exception to the dominant mechanism of domain architecture evolution.
Modelling of nonlinear shoaling based on stochastic evolution equations
Kofoed-Hansen, Henrik; Rasmussen, Jørgen Hvenekær
1998-01-01
A one-dimensional stochastic model is derived to simulate the transformation of wave spectra in shallow water including generation of bound sub- and super-harmonics, near-resonant triad wave interaction and wave breaking. Boussinesq type equations with improved linear dispersion characteristics...... are recast into evolution equations for the complex amplitudes, and serve as the underlying deterministic model. Next, a set of evolution equations for the cumulants is derived. By formally introducing the well-known Gaussian closure hypothesis, nonlinear evolution equations for the power spectrum...... and bispectrum are derived. A simple description of depth-induced wave breaking is incorporated in the model equations, assuming that the total rate of dissipation may be distributed in proportion to the spectral energy density on each discrete frequency. The proposed phase-averaged model is compared...
Yuji Liu
2014-01-01
Full Text Available We discuss the existence and uniqueness of solutions for initial value problems of nonlinear singular multiterm impulsive Caputo type fractional differential equations on the half line. Our study includes the cases for a single base point fractional differential equation as well as multiple base points fractional differential equation. The asymptotic behavior of solutions for the problems is also investigated. We demonstrate the utility of our work by applying the main results to fractional-order logistic models.
Initial Orbit Determination Based on Propagation of Admissible Regions with Differential Algebra
2017-01-19
order n with limited computational effort. In addition to basic algebraic operations, operations for differentiation and integration can be easily...AFRL-AFOSR-UK-TR-2017-0022 Initial Orbit Determination based on propagation of admissible regions with Differential Algebra Pierluigi Di Lizia...Determination based on propagation of admissible regions with Differential Algebra 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-15-1-0244 5c
Evolution of Black-Box Models Based on Volterra Series
Daniel D. Silveira
2015-01-01
Full Text Available This paper presents a historical review of the many behavioral models actually used to model radio frequency power amplifiers and a new classification of these behavioral models. It also discusses the evolution of these models, from a single polynomial to multirate Volterra models, presenting equations and estimation methods. New trends in RF power amplifier behavioral modeling are suggested.
Gomes, J. M.; Papaderos, P.
2017-07-01
The goal of population spectral synthesis (pss; also referred to as inverse, semi-empirical evolutionary- or fossil record approach) is to decipher from the spectrum of a galaxy the mass, age and metallicity of its constituent stellar populations. This technique, which is the reverse of but complementary to evolutionary synthesis, has been established as fundamental tool in extragalactic research. It has been extensively applied to large spectroscopic data sets, notably the SDSS, leading to important insights into the galaxy assembly history. However, despite significant improvements over the past decade, all current pss codes suffer from two major deficiencies that inhibit us from gaining sharp insights into the star-formation history (SFH) of galaxies and potentially introduce substantial biases in studies of their physical properties (e.g., stellar mass, mass-weighted stellar age and specific star formation rate). These are i) the neglect of nebular emission in spectral fits, consequently; ii) the lack of a mechanism that ensures consistency between the best-fitting SFH and the observed nebular emission characteristics of a star-forming (SF) galaxy (e.g., hydrogen Balmer-line luminosities and equivalent widths-EWs, shape of the continuum in the region around the Balmer and Paschen jump). In this article, we present fado (Fitting Analysis using Differential evolution Optimization) - a conceptually novel, publicly available pss tool with the distinctive capability of permitting identification of the SFH that reproduces the observed nebular characteristics of a SF galaxy. This so-far unique self-consistency concept allows us to significantly alleviate degeneracies in current spectral synthesis, thereby opening a new avenue to the exploration of the assembly history of galaxies. The innovative character of fado is further augmented by its mathematical foundation: fado is the first pss code employing genetic differential evolution optimization. This, in conjunction
Wang, Hongsen; Rus, Eric; Sakuraba, Takahito; Kikuchi, Jun; Kiya, Yasuyuki; Abruña, Héctor D
2014-07-01
A three-electrode differential electrochemical mass spectrometry (DEMS) cell has been developed to study the oxidative decomposition of electrolytes at high voltage cathode materials of Li-ion batteries. In this DEMS cell, the working electrode used was the same as the cathode electrode in real Li-ion batteries, i.e., a lithium metal oxide deposited on a porous aluminum foil current collector. A charged LiCoO2 or LiMn2O4 was used as the reference electrode, because of their insensitivity to air, when compared to lithium. A lithium sheet was used as the counter electrode. This DEMS cell closely approaches real Li-ion battery conditions, and thus the results obtained can be readily correlated with reactions occurring in real Li-ion batteries. Using DEMS, the oxidative stability of three electrolytes (1 M LiPF6 in EC/DEC, EC/DMC, and PC) at three cathode materials including LiCoO2, LiMn2O4, and LiNi(0.5)Mn(1.5)O4 were studied. We found that 1 M LiPF6 + EC/DMC electrolyte is quite stable up to 5.0 V, when LiNi(0.5)Mn(1.5)O4 is used as the cathode material. The EC/DMC solvent mixture was found to be the most stable for the three cathode materials, while EC/DEC was the least stable. The oxidative decomposition of the EC/DEC mixture solvent could be readily observed under operating conditions in our cell even at potentials as low as 4.4 V in 1 M LiPF6 + EC/DEC electrolyte on a LiCoO2 cathode, as indicated by CO2 and O2 evolution. The features of this DEMS cell to unveil solvent and electrolyte decomposition pathways are also described.
Quigley, Ian K; Turner, Jessica M; Nuckels, Richard J; Manuel, Joan L; Budi, Erine H; MacDonald, Erin L; Parichy, David M
2004-12-01
Latent precursors or stem cells of neural crest origin are present in a variety of post-embryonic tissues. Although these cells are of biomedical interest for roles in human health and disease, their potential evolutionary significance has been underappreciated. As a first step towards elucidating the contributions of such cells to the evolution of vertebrate form, we investigated the relative roles of neural crest cells and post-embryonic latent precursors during the evolutionary diversification of adult pigment patterns in Danio fishes. These pigment patterns result from the numbers and arrangements of embryonic melanophores that are derived from embryonic neural crest cells, as well as from post-embryonic metamorphic melanophores that are derived from latent precursors of presumptive neural crest origin. In the zebrafish D. rerio, a pattern of melanophore stripes arises during the larval-to-adult transformation by the recruitment of metamorphic melanophores from latent precursors. Using a comparative approach in the context of new phylogenetic data, we show that adult pigment patterns in five additional species also arise from metamorphic melanophores, identifying this as an ancestral mode of adult pigment pattern development. By contrast, superficially similar adult stripes of D. nigrofasciatus (a sister species to D. rerio) arise by the reorganization of melanophores that differentiated at embryonic stages, with a diminished contribution from metamorphic melanophores. Genetic mosaic and molecular marker analyses reveal evolutionary changes that are extrinsic to D. nigrofasciatus melanophore lineages, including a dramatic reduction of metamorphic melanophore precursors. Finally, interspecific complementation tests identify a candidate genetic pathway for contributing to the evolutionary reduction in metamorphic melanophores and the increased contribution of early larval melanophores to D. nigrofasciatus adult pigment pattern development. These results
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
Spatial differentiation and model evolution of housing prices in Yangzhou%扬州市住宅价格的空间分异与模式演变
王洋; 方创琳; 盛长元
2013-01-01
以2001-2012年扬州中心城区各居住小区的住宅平均单价为基本数据,通过建立住宅价格总体分异测度指数(GDI)计算其总体分异趋势及各住宅类型内部的分异趋势；采用核密度函数等方法探索住宅价格的分布形态和分异格局的演变规律；利用趋势面分析不同住宅类型价格的空间分异趋势；基于上述结果总结空间分异的演变模式,并分别探索空间分异与格局演变的驱动力.结果表明:①2001年以来扬州市住宅价格差距显著增大,分异趋势在波动中增强,与城市住宅均价的年增长率耦合；住宅价格呈现西高东低的空间分异格局,同档次价格小区由空间集聚转为相对分散,高、低价格住宅区分别沿固定扇面由中心向外围扩散.②不同住宅类型内的价格分异走势差别显著,各类型住宅间的价格趋势面差距明显,但其空间形态类似.③空间分异模式由2001年西高东低的扇形同档次价格集聚式分异转变为2012年扇形与圈层相结合的多档次价格混合式分异.④2001年以来住宅价格总体分异的核心驱动力是城市居住空间的迅速扩展、居民收入差距的增大、房地产市场的繁荣和住宅类型的多元化,其住宅价格空间格局演变的驱动力为城市发展方向的确立与变化、特定住宅类型建设的区位指向和古城保护、旧城改造与新区建设.%Urban housing price differentiation is an important issue in urban geography.However,relatively little analysis is available on continual time for all types,and all space.In China's high housing price times,housing price has become the core issue which was paid close attention by the government and inhabitants.The focus of this research is to examine global differentiation,spatial differentiation,model evolution and dynamics of housing prices in Yangzhou.Housing prices data for housing estates in the period 2001-2012 were used.Global differentiation
Structure analysis of growing network based on partial differential equations
Junbo JIA; Jin, Zhen
2016-01-01
The topological structure is one of the most important contents in the complex network research. Therein the node degree and the degree distribution are the most basic characteristic quantities to describe topological structure. In order to calculate the degree distribution, first of all, the node degree is considered as a continuous variable. Then, according to the Markov Property of growing network, the cumulative distribution function's evolution equation with time can be obtained. Finally...
Neurosphere Based Differentiation of Human iPSC Improves Astrocyte Differentiation
Zhou, Shuling; Szczesna, Karolina; Ochalek, Anna;
2016-01-01
of brain tissue. In this study, we determined that culturing iPSC-derived NPCs as three-dimensional (3D) floating neurospheres resulted in increased expression of the neural progenitor cell (NPC) markers, PAX6 and NESTIN. Expansion of NPCs in 3D culture methods also resulted in a more homogenous PAX6...... expression when compared to 2D culture methods. Furthermore, the 3D propagation method for NPCs resulted in a significant higher expression of the astrocyte markers GFAP and aquaporin 4 (AQP4) in the differentiated cells. Thus, our 3D propagation method could constitute a useful tool to promote NPC......Neural progenitor cells (NPCs) derived from human induced pluripotent stem cells (iPSCs) are traditionally maintained and proliferated utilizing two-dimensional (2D) adherent monolayer culture systems. However, NPCs cultured using this system hardly reflect the intrinsic spatial development...
Density based pruning for identification of differentially expressed genes from microarray data
Xu Jia
2010-11-01
Full Text Available Abstract Motivation Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes. Results We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB Pruning is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change. Conclusions Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://mleg.cse.sc.edu/degprune
Humphrey, Robert James, Jr.
Research studies over the past 30 years have found that individuals have a limited understanding of the theory of evolution and the mechanisms involved in species change. One possible avenue of improvement has been the use of alternative instructional methods, such as inquiry-based activities and teaching about nature of science. Using recommendations from research, this study integrated nature of science, evolution, and inquiry-based instruction to discern its impact on student understanding of evolution. An instructional unit was developed with a community college instructor and carried out in two introductory biology classes with a total of 38 participants. One class was taught using inquiry-based methods, with an integrated approach to nature of science and evolution, while the other was not. Data collection included student and instructor interviews, surveys, pre and post assessments, classroom observations, and student work products. The number of students holding accurate conceptions of the nature of science in the inquiry class was higher for all the reported categories on the posttest. Despite less direct exposure to evolution concepts in lecture, the inquiry class had higher means on two separate posttests for evolution. The traditional class performed better on the pretests yet the inquiry class had higher posttest scores on both measures. Students in the inquiry class held a positive view of the inquiry-based methods and they cited them as a reason for their understanding of evolution. Individuals indicated that the integration of nature of science and evolution allowed them to grasp the concepts of evolution better than if evolution was taught alone. A creationist student became more accepting of evolution and also improved her understanding of evolution. Another student interviewed four years after the intervention remembered only the inquiry-based unit and was able to still use examples from class to explain natural selection. The instructor had a
Wu Feng; Li Fei
2012-01-01
Syndrome stood for disease name and symptom in the ancient medical literature prior to the Tang Dynasty.With the development of pathogenesis theory,the meaning pathogenesis of syndrome came into being and the term differentiation of syndromes came into existence in the Song,Jin and Yuan Dynasties,hence differentiation of syndromes gradually became a diagnostics term of differentiating pathogenesis.
Integrable Equations and Their Evolutions Based on Intrinsic Geometry of Riemann Spaces
Paul Bracken
2009-01-01
Full Text Available The intrinsic geometry of surfaces and Riemannian spaces will be investigated. It is shown that many nonlinear partial differential equations with physical applications and soliton solutions can be determined from the components of the relevant metric for the space. The manifolds of interest are surfaces and higher-dimensional Riemannian spaces. Methods for specifying integrable evolutions of surfaces by means of these equations will also be presented.
Differentiating Pleural Effusions: Criteria Based on Pleural Fluid Cholesterol
Srinath Dhandapani
2016-08-01
Full Text Available Objective: To assess the efficacy of pleural fluid cholesterol in differentiating transudates and exudates as compared with Light’s criteria. Methods: Patients with pleural effusion during a 6-month period were enrolled in the study and underwent thoracentesis. Pleural fluid was analyzed for the levels of protein, lactate dehydrogenase (LDH, and cholesterol. Etiological diagnosis, which was established after considering clinical and biochemical factors, was the gold standard for comparison. Cut-off values for pleural fluid cholesterol were taken as 60 mg/dL and 45 mg/dL. Results: A total of 53 patients were included for final analysis. Of them, 19 were with transudates and 34 with exudates in their pleural fluids. The sensitivity, specificity, positive predictive value, and negative predictive value of the pleural fluid cholesterol (cut-off >45 mg/dL were 97.06%, 94.74%, 97.06%, and 94.74%, respectively, for identifying exudates. These values were differentiating better than those obtained by Light’s criteria for pleural fluid cholesterol (cut-off >60 mg/dL (p45 mg/dL gave a higher specificity (100% and positive predictive value (100% but a lower sensitivity (82.93% and negative predictive value (63.16%. Conclusion: Pleural fluid cholesterol is better than Light’s criteria for the differentiation of transudates and exudates and is less cumbersome as it does not require a simultaneous blood sampling. Cut-off value of pleural fluid cholesterol for differentiating transudates and exudates should be 45 mg/dL. Further studies are warranted to assess the efficacy of the combination of pleural fluid protein and cholesterol as criteria for classifying effusions.
Differentiating Pleural Effusions: Criteria Based on Pleural Fluid Cholesterol
Srinath Dhandapani; Sivakumar Reddy; Rajalakshmi Rajagopalan
2016-01-01
Objective: To assess the efficacy of pleural fluid cholesterol in differentiating transudates and exudates as compared with Light’s criteria. Methods: Patients with pleural effusion during a 6-month period were enrolled in the study and underwent thoracentesis. Pleural fluid was analyzed for the levels of protein, lactate dehydrogenase (LDH), and cholesterol. Etiological diagnosis, which was established after considering clinical and biochemical factors, was the gold standard for com...
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.
Woncheoul Park
Full Text Available Previous studies of horse RNA-seq were performed by mapping sequence reads to the reference genome during transcriptome analysis. However in this study, we focused on two main ideas. First, differentially expressed genes (DEGs were identified by de novo-based analysis (DBA in RNA-seq data from six Thoroughbreds before and after exercise, here-after referred to as "de novo unique differentially expressed genes" (DUDEG. Second, by integrating both conventional DEGs and genes identified as being selected for during domestication of Thoroughbred and Jeju pony from whole genome re-sequencing (WGS data, we give a new concept to the definition of DEG. We identified 1,034 and 567 DUDEGs in skeletal muscle and blood, respectively. DUDEGs in skeletal muscle were significantly related to exercise-induced stress biological process gene ontology (BP-GO terms: 'immune system process'; 'response to stimulus'; and, 'death' and a KEGG pathways: 'JAK-STAT signaling pathway'; 'MAPK signaling pathway'; 'regulation of actin cytoskeleton'; and, 'p53 signaling pathway'. In addition, we found TIMELESS, EIF4A3 and ZNF592 in blood and CHMP4C and FOXO3 in skeletal muscle, to be in common between DUDEGs and selected genes identified by evolutionary statistics such as FST and Cross Population Extended Haplotype Homozygosity (XP-EHH. Moreover, in Thoroughbreds, three out of five genes (CHMP4C, EIF4A3 and FOXO3 related to exercise response showed relatively low nucleotide diversity compared to the Jeju pony. DUDEGs are not only conceptually new DEGs that cannot be attained from reference-based analysis (RBA but also supports previous RBA results related to exercise in Thoroughbred. In summary, three exercise related genes which were selected for during domestication in the evolutionary history of Thoroughbred were identified as conceptually new DEGs in this study.
Indirect Inference for Stochastic Differential Equations Based on Moment Expansions
Ballesio, Marco
2016-01-06
We provide an indirect inference method to estimate the parameters of timehomogeneous scalar diffusion and jump diffusion processes. We obtain a system of ODEs that approximate the time evolution of the first two moments of the process by the approximation of the stochastic model applying a second order Taylor expansion of the SDE s infinitesimal generator in the Dynkin s formula. This method allows a simple and efficient procedure to infer the parameters of such stochastic processes given the data by the maximization of the likelihood of an approximating Gaussian process described by the two moments equations. Finally, we perform numerical experiments for two datasets arising from organic and inorganic fouling deposition phenomena.
Which factor dominates the industry evolution? A synergy analysis based on China's ICT industry
Li, Yaya; Zhao, Yulin; Wang, Fang
2014-01-01
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on industry convergence and technology progress. Firstly, we use self-organization method and Haken Model to establish synergetic evolution equations, select technology progress and industry convergence as the key variables of industry evolution system; then use patent licensing data of china's listed ICT companies to measure industry convergence rate and apply DEA Malmquist index method to calculate technology progress level; furthermore apply simultaneous equation estimation method to investigate the synergetic industry evolution process. From 2002 to 2012, China's ICT industry develops rapidly; it has the most obvious convergence and powerful technology progress compared with other industries. ...
Fractal-Based Methods and Inverse Problems for Differential Equations: Current State of the Art
Kunze, Herb E.; Davide La Torre; Franklin Mendivil; Manuel Ruiz Galán; Rachad Zaki
2014-01-01
We illustrate, in this short survey, the current state of the art of fractal-based techniques and their application to the solution of inverse problems for ordinary and partial differential equations. We review several methods based on the Collage Theorem and its extensions. We also discuss two innovative applications: the first one is related to a vibrating string model while the second one considers a collage-based approach for solving inverse problems for partial differential equations on ...
Zhong, Yaoquan; Guo, Wei; Jin, Yaohui; Sun, Weiqiang; Hu, Weisheng
2010-12-01
A cost-effective and service-differentiated provisioning strategy is very desirable to service providers so that they can offer users satisfactory services, while optimizing network resource allocation. Providing differentiated protection services to connections for surviving link failure has been extensively studied in recent years. However, the differentiated protection services for workflow-based applications, which consist of many interdependent tasks, have scarcely been studied. This paper investigates the problem of providing differentiated services for workflow-based applications in optical grid. In this paper, we develop three differentiated protection services provisioning strategies which can provide security level guarantee and network-resource optimization for workflow-based applications. The simulation demonstrates that these heuristic algorithms provide protection cost-effectively while satisfying the applications' failure probability requirements.
LeGray, Matthew W.; Dufrene, Brad A.; Sterling-Turner, Heather; Olmi, D. Joe; Bellone, Katherine
2010-01-01
This study provides a direct comparison of differential reinforcement of other behavior (DRO) and differential reinforcement of alternative behavior (DRA). Participants included three children in center-based classrooms referred for functional assessments due to disruptive classroom behavior. Functional assessments included interviews and brief…
Ismail, Azman; Ahmad, Rokiah Rozita; Din, Ummul Khair Salma; Hamid, Mohd Rosli A.
2014-09-01
This study is based on third order multistep method using interpolation formula. The coefficients of new formula are produced using modification on interpolation. This method is tested on ordinary differential equations. Comparisons are between the modified method and the classical Adams Bashforth. Mathematica software is used to determine the new coefficients. The methods was found to be efficient when tested on ordinary differential equation.
Odom, Karan J; Omland, Kevin E; Price, J Jordan
2015-03-01
Female bird song and combined vocal duets of mated pairs are both frequently associated with tropical, monogamous, sedentary natural histories. Little is known, however, about what selects for duetting behavior versus female song. Female song likely preceded duet evolution and could drive apparent relationships between duets and these natural histories. We compared the evolution of female song and male-female duets in the New World blackbirds (Icteridae) by investigating patterns of gains and losses of both traits and their relationships with breeding latitude, mating system, nesting pattern, and migratory behavior. We found that duets evolved only in lineages in which female song was likely ancestral. Both female song and duets were correlated with tropical breeding, social monogamy, territorial nesting, and sedentary behavior when all taxa were included; however, correlations between duets and these natural history traits disappeared when comparisons were limited to taxa with female song. Also, likelihood values supported stronger relationships between the natural history traits and female song than between these traits and duets. Our results suggest that the natural histories thought to favor the evolution of duetting may in fact be associated with female song and that additional selection pressures are responsible for the evolution of duets. © 2015 The Author(s).
F. Z. Geng
2012-01-01
Full Text Available We introduce a new method for solving Riccati differential equations, which is based on reproducing kernel method and quasilinearization technique. The quasilinearization technique is used to reduce the Riccati differential equation to a sequence of linear problems. The resulting sets of differential equations are treated by using reproducing kernel method. The solutions of Riccati differential equations obtained using many existing methods give good approximations only in the neighborhood of the initial position. However, the solutions obtained using the present method give good approximations in a larger interval, rather than a local vicinity of the initial position. Numerical results compared with other methods show that the method is simple and effective.
2011-01-01
Background In the model system Drosophila melanogaster, doublesex (dsx) is the double-switch gene at the bottom of the somatic sex determination cascade that determines the differentiation of sexually dimorphic traits. Homologues of dsx are functionally conserved in various dipteran species, including the malaria vector Anopheles gambiae. They show a striking conservation of sex-specific regulation, based on alternative splicing, and of the encoded sex-specific proteins, which are transcriptional regulators of downstream terminal genes that influence sexual differentiation of cells, tissues and organs. Results In this work, we report on the molecular characterization of the dsx homologue in the dengue and yellow fever vector Aedes aegypti (Aeadsx). Aeadsx produces sex-specific transcripts by alternative splicing, which encode isoforms with a high degree of identity to Anopheles gambiae and Drosophila melanogaster homologues. Interestingly, Aeadsx produces an additional novel female-specific splicing variant. Genomic comparative analyses between the Aedes and Anopheles dsx genes revealed a partial conservation of the exon organization and extensive divergence in the intron lengths. An expression analysis showed that Aeadsx transcripts were present from early stages of development and that sex-specific regulation starts at least from late larval stages. The analysis of the female-specific untranslated region (UTR) led to the identification of putative regulatory cis-elements potentially involved in the sex-specific splicing regulation. The Aedes dsx sex-specific splicing regulation seems to be more complex with the respect of other dipteran species, suggesting slightly novel evolutionary trajectories for its regulation and hence for the recruitment of upstream splicing regulators. Conclusions This study led to uncover the molecular evolution of Aedes aegypti dsx splicing regulation with the respect of the more closely related Culicidae Anopheles gambiae orthologue
Arcà Bruno
2011-02-01
Full Text Available Abstract Background In the model system Drosophila melanogaster, doublesex (dsx is the double-switch gene at the bottom of the somatic sex determination cascade that determines the differentiation of sexually dimorphic traits. Homologues of dsx are functionally conserved in various dipteran species, including the malaria vector Anopheles gambiae. They show a striking conservation of sex-specific regulation, based on alternative splicing, and of the encoded sex-specific proteins, which are transcriptional regulators of downstream terminal genes that influence sexual differentiation of cells, tissues and organs. Results In this work, we report on the molecular characterization of the dsx homologue in the dengue and yellow fever vector Aedes aegypti (Aeadsx. Aeadsx produces sex-specific transcripts by alternative splicing, which encode isoforms with a high degree of identity to Anopheles gambiae and Drosophila melanogaster homologues. Interestingly, Aeadsx produces an additional novel female-specific splicing variant. Genomic comparative analyses between the Aedes and Anopheles dsx genes revealed a partial conservation of the exon organization and extensive divergence in the intron lengths. An expression analysis showed that Aeadsx transcripts were present from early stages of development and that sex-specific regulation starts at least from late larval stages. The analysis of the female-specific untranslated region (UTR led to the identification of putative regulatory cis-elements potentially involved in the sex-specific splicing regulation. The Aedes dsx sex-specific splicing regulation seems to be more complex with the respect of other dipteran species, suggesting slightly novel evolutionary trajectories for its regulation and hence for the recruitment of upstream splicing regulators. Conclusions This study led to uncover the molecular evolution of Aedes aegypti dsx splicing regulation with the respect of the more closely related Culicidae
Abd El Munim, Hossam E; Farag, Aly A
2007-06-01
In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in [1]. Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework.
2011-01-01
Based on the analysis of fieldwork data collected by us from 102 households in the villages of Yaojia, Jizhuang and Wuzi, we analyze the phenomenon of differentiation behaviors of households who own different kinds of resources under the background of agricultural industrialization. The focus of this paper is to probe into characteristics of the physical contact space, information contract space between different rural households such as farmers, brokers and entrepreneurs. Then, we focus on the driving forces behind the household differentiation process. Several conclusions can be drawn from this analysis. Firstly, the geographical domain increases as the households evolutes from farmers to entrepreneurs, and the farmers’ physical contract space is larger than the information contract space while that of brokers and entrepreneurs equals. Secondly, there is a certain pattern existing in the evolution: based on the self-techniques, farmers evolutes to flower workers, and to brokers when the capital, social network and self-ability is sufficient. As a result of appropriate policy, opportunity of building business and the risk appetite characteristics, entrepreneurs may differentiate from the brokers.
Design and Experiment of a Differential-Based Power Split Device
Xiaohua Zeng
2014-04-01
Full Text Available Hybrid electric vehicles have excellent energy efficiency and emission performance. Power split device (PSD is a key component that directly affects the control strategy of power systems, the economic consumption of fuel, and the dynamic performance of vehicles. A differential-based PSD was proposed in this paper. A traditional differential was taken as the prototype and a new design method is proposed to retrofit the differential into a PSD. First, a comprehensive approach that includes theoretical analysis and software simulation was used to analyze the possibility as well as the necessity of retrofitting the differential into PSD. Then the differential was retrofitted. Finally, finite element analysis and bench test were conducted. Results showed that applying the retrofitted differential as PSD is practicable.
Bai, Yang; Lu, Yunfeng; Hu, Pengcheng; Wang, Gang; Xu, Jinxin; Zeng, Tao; Li, Zhengkun; Zhang, Zhonghua; Tan, Jiubin
2016-05-11
A simple differential capacitive sensor is provided in this paper to measure the absolute positions of length measuring systems. By utilizing a shield window inside the differential capacitor, the measurement range and linearity range of the sensor can reach several millimeters. What is more interesting is that this differential capacitive sensor is only sensitive to one translational degree of freedom (DOF) movement, and immune to the vibration along the other two translational DOFs. In the experiment, we used a novel circuit based on an AC capacitance bridge to directly measure the differential capacitance value. The experimental result shows that this differential capacitive sensor has a sensitivity of 2 × 10(-4) pF/μm with 0.08 μm resolution. The measurement range of this differential capacitive sensor is 6 mm, and the linearity error are less than 0.01% over the whole absolute position measurement range.
Starvation Based Differential Chemotherapy: A Novel Approach for Cancer Treatment
Sidra Naveed
2014-11-01
Full Text Available Cancer patients undergoing chemotherapy treatment are advised to increase food intake to overcome the therapy-induced side effects, and weight loss. Dietary restriction is known to slow down the aging process and hence reduce age-related diseases such as cancer. Fasting or short-term starvation is more effective than dietary restriction to prevent cancer growth since starved cells switch off signals for growth and reproduction and enter a protective mode, while cancer cells, being mutated, are not sensitized by any external growth signals and are not protected against any stress. This phenomenon is known as differential stress resistance (DSR. Nutrient signaling pathways involving growth hormone/insulin-like growth factor-1 axis and its downstream effectors, play a key role in DSR in response to starvation controlling the other cell maintenance systems, such as autophagy and apoptosis, that are related to the tumorigenesis. Yeast cells lacking these effectors are better protected against oxidative stress compared to normal cells. In the same way, starvation protects many cell lines and mice against high-dose chemotherapeutic drugs. According to a series of studies, fasting results in overall reduction in chemotherapy side effects in cancer patients. Data shows that starvation-dependent differential chemotherapy is safe, feasible and effective in cancer treatment, but the possible side effects of starvation limit its efficacy. However, further studies and clinical trials may result in its implementation in cancer treatment.
Wilson, David P
2010-01-01
Beliefs regarding the origins of the universe and life differ substantially between groups of people and are often particularly associated with religious worldviews. It is important to understand factors associated with evolution and creationism beliefs and unacceptance of scientific evidence for evolution. An internet-based survey was conducted to elicit information from people who self-identify as Christians, atheists, agnostics and other belief systems, as well as by geographical location and other demographic variables, on acceptance of evolution or creationism, certainty with which each position is believed, and reasons for rejecting the alternative. It was found that almost 60% of Christians believe in creationism and less than 10% believe in natural evolution. Worldwide, these proportions were relatively consistent across all locations except for in Europe. Among European Christians the majority of Christians believe in a form of evolution. It was found that the vast majority (87%) of Christians are 'absolutely certain' about their beliefs, compared with the minority of atheists and agnostics claiming 'absolute certainty'. Generally, reasons Christians did not accept evolution were based not on evidence but on religious doctrine. In contrast, the most common reason for not accepting the existence of a god by atheists who supported evolution was the lack of evidence. Innovative strategies may be required to communicate evolutionary science effectively to non-European Christians.
Alicea, Bradly; Gordon, Richard
2016-08-18
Embryonic development proceeds through a series of differentiation events. The mosaic version of this process (binary cell divisions) can be analyzed by comparing early development of Ciona intestinalis and Caenorhabditis elegans. To do this, we reorganize lineage trees into differentiation trees using the graph theory ordering of relative cell volume. Lineage and differentiation trees provide us with means to classify each cell using binary codes. Extracting data characterizing lineage tree position, cell volume, and nucleus position for each cell during early embryogenesis, we conduct several statistical analyses, both within and between taxa. We compare both cell volume distributions and cell volume across developmental time within and between single species and assess differences between lineage tree and differentiation tree orderings. This enhances our understanding of the differentiation events in a model of pure mosaic embryogenesis and its relationship to evolutionary conservation. We also contribute several new techniques for assessing both differences between lineage trees and differentiation trees, and differences between differentiation trees of different species. The results suggest that at the level of differentiation trees, there are broad similarities between distantly related mosaic embryos that might be essential to understanding evolutionary change and phylogeny reconstruction. Differentiation trees may therefore provide a basis for an Evo-Devo Postmodern Synthesis.
An evolution-based strategy for engineering allosteric regulation
Pincus, David; Resnekov, Orna; Reynolds, Kimberly A.
2017-04-01
Allosteric regulation provides a way to control protein activity at the time scale of milliseconds to seconds inside the cell. An ability to engineer synthetic allosteric systems would be of practical utility for the development of novel biosensors, creation of synthetic cell signaling pathways, and design of small molecule pharmaceuticals with regulatory impact. To this end, we outline a general approach—termed rational engineering of allostery at conserved hotspots (REACH)—to introduce novel regulation into a protein of interest by exploiting latent allostery that has been hard-wired by evolution into its structure. REACH entails the use of statistical coupling analysis (SCA) to identify ‘allosteric hotspots’ on protein surfaces, the development and implementation of experimental assays to test hotspots for functionality, and a toolkit of allosteric modulators to impinge on endogenous cellular circuitry. REACH can be broadly applied to rewire cellular processes to respond to novel inputs.
Using 2-Opt based evolution strategy for travelling salesman problem
Kenan Karagul
2016-03-01
Full Text Available Harmony search algorithm that matches the (µ+1 evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions.
Novel concepts for differential-equation-based electromagnetic field simulations
Teixeira, Fernando Lisboa
This thesis presents novel concepts for electromagetic field simulations via partial differential equation (PDE) solvers. A vital aspect for any successful general implementation of a PDE solver is the use of an efficient absorbing boundary condition (ABC). The perfectly matched layer (PML) is a recently introduced ABC in Cartesian coordinates which provides reflection errors orders of magnitude smaller than previously employed ABCs. In this work, a new interpretation of the PML as an analytic continuation of the coordinate space is used to extend the PML to other coordinate systems. Modified equations replace the original Maxwell's equations, mapping propagating solutions into exponentially decaying solutions. Alternative (Maxwellian) formulations are also put forth, where the PML is represented as an artificial media with complex constitutive tensors, and the form of Maxwell's equations is retained. The causality and dynamic stability of the PML is characterized through a spectral analysis. In addition, a rationale is presented to extend the PML to complex media, e.g., dispersive and/or (bi-)anisotropic. For the Maxwellian formulation, the general expressions for the PML tensors matched to any interior dispersive and/or (bi-)anisotropic linear media are obtained. A finite-difference time-domain (FDTD) algorithm in Cartesian coordinates which combines the PML ABC with piecewise-linear recursive convolution (PLRC) is proposed and implemented, allowing the simulation of electromagnetic fields in inhomogeneous and dispersive media with conductive loss. Two PML-PLRC-FDTD algorithms in cylindrical coordinates are also proposed and implemented. The first is developed through a split-field PML formulation, and the second through a Maxwellian (unsplit) PML formulation. A comparison is made between numerical properties of these two algorithms. The PML concept is then studied within the language of differential forms to unify the various PML formulations. Finally, the
Model of Vertical Product Differentiation Based on Triangular Distribution
HU Jian-bing; WANG He-ping; SHEN Yun-hong
2007-01-01
Supposing that the consumer preference complies with triangular distribution instead of uniform distribution, we establish the model of vertical product differentiation. The simulation shows that there exists stable equilibrium along with unstable equilibrium. In stable equilibrium, high quality products gain an advantage over low quality products. In unstable equilibrium, the former does not possess an apparent advantage in competition, likely to be at a disadvantage. In order to evolve from unstable equilibrium to stable equilibrium, it is necessary for firms to solve such problems as high prices and consumers' perception of scarcity on product qualities. In general, both product qualities and firm profits increase with the consuming capacity and quality perception, and the latter more rapidly.
Synergism of He-3 acquisition with lunar base evolution
Crabb, Thomas M.; Jacobs, Mark K.
1988-09-01
It is shown how acquisition of He-3 affects Lunar Base development and operation. A four phase evolutionary Lunar Base scenario is summarized with initial equipment mass and resupply requirements. Requirements for various He-3 mining operations are shown and available by-products are identified. Impacts of mining He-3 on Lunar Base development include increases in equipment masses to be delivered to the lunar surface and a reduction of Lunar Base resupply based on availability of He-3 acquisition by-products. It is concluded that the acquisition of this valuable fusion fuel element greatly enhances the commercial potential of a Lunar Base.
Karim MAMDOUH ABBAS
2014-04-01
Full Text Available The present investigation aims to determine the factors affecting evolution of Activity Based Costing (ABC system in Egyptian case. The study used the survey method to describe and analyze these factors in some Egyptian firms. The population of the study is Egyptian manufacturing firms. Accordingly, the number of received questionnaires was 392 (23 Egyptian manufacturing firms in the first half of 2013. Finally, the study stated some influencing factors for evolution this system (ABC in Egyptian manufacturing firms.
Properties of Differential Scattering Section Based on Multi-photon Nonlinear Compton Effect
无
2002-01-01
Properties of damping electrons in collision with photons based on multi-photon nonlinear Compton effect are investigated. The expressions of the differential scattering section are derived. Several useful conclusions are drawn.
A Distributed Problem Solving Environment (PSE) for Partial Differential Equation Based Problems
TERAMOTO, Takayuki; NAKAMURA, Takashi; KAWATA, Shigeo; MATIDE, Syunsuke; HAYASAKA, Koji; NONAKA, Hidetaka; SASAKI, Eiji; SANADA, Yasuhiro
2001-01-01
...) for partial differential equation (PDE) based problems. The system inputs a problem information including a discretization and computation scheme, and outputs a program flow and also a C-language source code for the problem...
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.
Qi, Zhihua; Zambelli, Joseph; Bevins, Nicholas; Chen, Guang-Hong
2010-04-01
Compared to single energy CT, which provides information only about the x-ray linear attenuation coefficients, dual energy CT is able to obtain the electron density and effective atomic number for different materials in a quantitative way. In this study, as an alternative to dual energy CT, a novel quantitative imaging method based on phase contrast CT is described. Rather than requiring two scans with different x-ray photon energies, diffraction grating-based phase contrast CT is capable of reconstructing images of both the linear attenuation and refractive index decrement from a single scan. From the two images, quantitative information of both the electron density and effective atomic number can be extracted. Experimental results demonstrate that: (1) electron density can be accurately determined from refractive index decrement through a linear relationship; and (2) effective atomic number can be explicitly derived from the ratio of linear attenuation to refractive index decrement, using a simple function, i.e., a power function plus a constant. The presented method will shed insight into the field of material separation and find its use in medical and non-medical applications.
Evolution of Web-Based Applications Using Domain-Specific Markup Languages
Guntram Graef
2000-11-01
Full Text Available The lifecycle of Web-based applications is characterized by frequent changes to content, user interface, and functionality. Updating content, improving the services provided to users, drives further development of a Web-based application. The major goal for the success of a Web-based application becomes therefore its evolution. Though, development and maintenance of Web-based applications suffers from the underlying document-based implementation model. A disciplined evolution of Web based applications requires the application of software engineering practice for systematic further development and reuse of software artifacts. In this contribution we suggest to adopt the component paradigm to development and evolution of Web-based applications. The approach is based on a dedicated component technology and component-software architecture. It allows abstracting from many technical aspects related to the Web as an application platform by introducing domain specific markup languages. These languages allow the description of services, which represent domain components in our Web-component-software approach. Domain experts with limited knowledge of technical details can therefore describe application functionality and the evolution of orthogonal aspects of the application can be de-coupled. The whole approach is based on XML to achieve the necessary standardization and economic efficiency for the use in real world projects.
Yin, Guisheng; Chi, Kuo, E-mail: chik89769@hrbeu.edu.cn; Dong, Yuxin; Dong, Hongbin
2017-04-25
In this paper, an approach of community evolution based on gravitational relationship refactoring between the nodes in a dynamic network is proposed, and it can be used to simulate the process of community evolution. A static community detection algorithm and a dynamic community evolution algorithm are included in the approach. At first, communities are initialized by constructing the core nodes chains, the nodes can be iteratively searched and divided into corresponding communities via the static community detection algorithm. For a dynamic network, an evolutionary process is divided into three phases, and behaviors of community evolution can be judged according to the changing situation of the core nodes chain in each community. Experiments show that the proposed approach can achieve accuracy and availability in the synthetic and real world networks. - Highlights: • The proposed approach considers both the static community detection and dynamic community evolution. • The approach of community evolution can identify the whole 6 common evolution events. • The proposed approach can judge the evolutionary events according to the variations of the core nodes chains.
Drone Based Experimental Investigation of Wind Turbine Wake Evolution
Subramanian, Balaji, , Dr.; Chokani, Ndaona, , Dr.; Abhari, Reza, Prof. _., Dr.
2016-11-01
The characteristics of the wake downstream of a wind turbine has an important bearing on the optimized micrositing of wind turbines in a given land area, as well as on the loads seen by downstream turbines. We use a novel measurement system to measure the flow field upstream and in the wake of a full-scale wind turbine. The system consists of a fast response aerodynamic probe, mounted on an autonomous drone that is equipped with a suite of sensors. These measurements detail, for the first time at full-scale Reynolds number conditions, the evolution and breakdown of tip vortices that are characteristic of the near wake, as well as the turbulent mixing and entrainment of more energised flow, which are distinctive in the far wake. A short-time Fourier transform (STFT) analysis method is used to derive time-localized TKE along the drone's trajectory. Detailed upstream and wake measurements are needed to understand the flow behavior, as it helps in developing and validating simplified wake models that can approximate the wake qualities. Comparisons of these measurements to recently developed wake prediction models highlights how these measurements can support further model development.
Simulation Study of Swarm Intelligence Based on Life Evolution Behavior
Yanmin Liu
2015-01-01
Full Text Available Swarm intelligence (SI is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DNA + environment + interaction of environment + gene,” to propose the mutation and crossover operation of DNA fragments by the environmental change to improve the performance efficiency of intelligence algorithms. Additionally, PSO is a random swarm intelligence algorithm with the genetic and sociological property, so we embed the improved mutation and crossover operation to particle swarm optimization (PSO and designed DNA-PSO algorithm to optimize single and multiobjective optimization problems. Simulation experiments in single and multiobjective optimization problems show that the proposed strategies can effectively improve the performance of swarm intelligence.
A paradigm-based evolution of chemical engineering
Alexandru Woinaroschy
2016-01-01
A short presentation of chemical engineering evolution, as guided by its paradigms, is exposed. The first paradigm–unit operations–has emerged as a necessity of systematization due to the explosion of chemical industrial applica-tions at the end of 19th century. The birth in the late 1950s of the second paradigm–transport phenomena–was the consequence of the need for a deep, scientific knowledge of the phenomena that explain what happens inside of unit operations. In the second part of 20th century, the importance of chemical product properties and qualities has become essential y in the market fights. Accordingly, it was required with additional and even new fundamen-tal approaches, and product engineering was recognized as the third paradigm. Nowadays chemical industry, as a huge materials and energy consumer, and with a strong ecological impact, couldn't remain outside of sustainability requirements. The basics of the fourth paradigm–sustainable chemical engineering–are now formulated.
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.
Non-Local Fractional Differential-Based Approach for Image Enhancement
Da-Li Chen
2013-09-01
Full Text Available This study proposed an image enhancing method which is based on the non-local fractional order differential operator. In this method, a matrix form representation of discrete fractional order differentiation is introduced to enhance the digital image, which is effective to reduce the computation error caused by the traditional local approximate method of the fractional order differentiation. The proposed enhancing method is able to make effective use of the whole image information and improve the enhancing performance of the image enhancing algorithm based on the local mask. The color image enhancing strategy based on the non-local fractional differential also is given. A lot of experiments demonstrate that the proposed method is capable of enhancing gray and color image effectively.
Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs
Ye Zhi-Qiang
2011-08-01
Full Text Available Abstract Background Differential coexpression analysis (DCEA is increasingly used for investigating the global transcriptional mechanisms underlying phenotypic changes. Current DCEA methods mostly adopt a gene connectivity-based strategy to estimate differential coexpression, which is characterized by comparing the numbers of gene neighbors in different coexpression networks. Although it simplifies the calculation, this strategy mixes up the identities of different coexpression neighbors of a gene, and fails to differentiate significant differential coexpression changes from those trivial ones. Especially, the correlation-reversal is easily missed although it probably indicates remarkable biological significance. Results We developed two link-based quantitative methods, DCp and DCe, to identify differentially coexpressed genes and gene pairs (links. Bearing the uniqueness of exploiting the quantitative coexpression change of each gene pair in the coexpression networks, both methods proved to be superior to currently popular methods in simulation studies. Re-mining of a publicly available type 2 diabetes (T2D expression dataset from the perspective of differential coexpression analysis led to additional discoveries than those from differential expression analysis. Conclusions This work pointed out the critical weakness of current popular DCEA methods, and proposed two link-based DCEA algorithms that will make contribution to the development of DCEA and help extend it to a broader spectrum.
Managing Evolution and Change in Web-Based Teaching and Learning Environments.
Pahl, Claus
2003-01-01
Discusses the design and maintenance of computer-based teaching and learning environments and illustrates consequences of evolution and change in Web-based courses. Focuses on changes in content; format of the course; infrastructure, including hardware, systems, and software; and pedagogy, or instructional design, including knowledge modeling,…
Evolution of the base of the brain in highly encephalized human species.
Bastir, Markus; Rosas, Antonio; Gunz, Philipp; Peña-Melian, Angel; Manzi, Giorgio; Harvati, Katerina; Kruszynski, Robert; Stringer, Chris; Hublin, Jean-Jacques
2011-12-13
The increase of brain size relative to body size-encephalization-is intimately linked with human evolution. However, two genetically different evolutionary lineages, Neanderthals and modern humans, have produced similarly large-brained human species. Thus, understanding human brain evolution should include research into specific cerebral reorganization, possibly reflected by brain shape changes. Here we exploit developmental integration between the brain and its underlying skeletal base to test hypotheses about brain evolution in Homo. Three-dimensional geometric morphometric analyses of endobasicranial shape reveal previously undocumented details of evolutionary changes in Homo sapiens. Larger olfactory bulbs, relatively wider orbitofrontal cortex, relatively increased and forward projecting temporal lobe poles appear unique to modern humans. Such brain reorganization, beside physical consequences for overall skull shape, might have contributed to the evolution of H. sapiens' learning and social capacities, in which higher olfactory functions and its cognitive, neurological behavioral implications could have been hitherto underestimated factors.
A Pattern Language for the Evolution of Component-based Software Architectures
Ahmad, Aakash; Jamshidi, Pooyan; Pahl, Claus
2013-01-01
that enable reuse-driven and consistent evolution in component-based software architectures. Pattern interconnections represent possible relationships among patterns (such as variants or related patterns) in the language. In general, we introduce architecture change mining (pattern language development...... as a measure of selecting the most appropriate pattern(s) from the language collection. The pattern language itself continuously evolves with an incremental acquisition of new patterns from change logs over time....... evolution problems. We propose that architectural evolution process requires an explicit evolution-centric knowledge – that can be discovered, shared, and reused – to anticipate and guide change management. Therefore, we present a pattern language as a collection of interconnected change patterns...
Modelling for Forest Fire Evolution Based on the Energy Accumulation and Release
Fan Yang
2015-09-01
Full Text Available Forest fire evolution plays an important role in the decision-making of controlling the forest fire. This paper aims to simulate the dynamics of the forest fire spread using a cellular automaton approach. Having analyzed the characteristics and evolution of forest fires, a simulation model for the forest fire evolution based on the energy accumulation and release is proposed. And, taking Australia's catastrophic forest fire in 2009 as an example, the fire’s evolution closely to the reality is simulated. The results of the experiments are shown that if forest energy is released in a small scale before or during the fire, the fire would be better controlled even if it does not occur. Improving the efficiency of the fire extinguishing procedures and reducing the speed of the fire spread are also effective for controlling the forest fire.
Wu, Jin Jei; Hou, Da Jun; Liu, Kexin; Shen, Linfang; Tsai, Chi An; Wu, Chien Jang; Tsai, Dichi; Yang, Tzong-Jer
2014-11-03
We apply the concept of spoof surface plasmon polaritons (SPPs) to the design of differential microstrip lines by introducing periodic subwavelength corrugations on their edges. The dispersion relation and field distribution of those lines are analyzed numerically. And then through designing practical coupling circuits, we found that compared with conventional differential microstrip lines, the electromagnetic field can be strongly confined inside the grooves of the corrugated microstrip lines, so the crosstalk between the differential pair and the adjacent microstrip lines is greatly reduced, and the conversion from the differential signal to the common mode signal can also be effectively suppressed. The propagation length of those lines is also very long in a wide band. Moreover, the experimental results in time domain demonstrate those lines perform very well in high-speed circuit. Therefore, those novel kinds of spoof SPPs based differential microstrip lines can be widely utilized in high-density microwave circuits and guarantee signal integrity in high-speed systems.
G R, R. K.; C, S.
2015-12-01
The fundamental challenge in understanding the origin and evolution of the continental crust is to recognize how primary mantle source, and oceanic crust, which are essentially mafic to ultramafic in composition, could differentiate into a more or less felsic compositions. It is possible to understand growth and differentiation of the continental crust by constraining the interplay of magmatism, deformation, and high-grade metamorphism in the lower crust. Here, we apply this knowledge on the lower crustal granitoids of southern India and speculate on the variations in geochemistry as a consequence of differentiation and secular evolution of the continental crust.The major groups of granitoids of southern India are classified as metatonalites, comparable to typical Archaean TTGs with pronounced calc-alkaline affinity, and metagranites which are magmatic fractionation produced by reworking of early crust. Metatonalites are sodic-trondhjemites with slightly magnesian, moderate LREE (average LaN = 103) and low HREE (average YbN = 2) characerestics, where as metagranites are calc-alkaline ferroan types with enriched LREE (average LaN = 427) and HREE (average YbN = 23). Petrogenetic characteristics of granitoids illustrate continuous evolution of a primary crust into diverse magmatic units by multiple stages of intracrustal differentiation processes attributed to following tectonic scenarios: (1) formation of tonalitic magma by low- to moderate-degree partial melting of hydrated basaltic crust at pressures high enough to stabilize garnet-amphibole residue and (2) genesis of granite in a continental arc-accretion setting by an episode of crustal remelting of the tonalitic crust, within plagioclase stability field. The first-stage formed in a flat-subduction setting of an volcanic-arc, leading to the formation of tonalites. The heat budget required is ascribed to the upwelling of the mantle and/or basaltic underplating. Progressive decline in mantle potential temperature
The Evolution of ICT Markets: An Agent-Based Model on Complex Networks
Zhao, Liangjie; Wu, Bangtao; Chen, Zhong; Li, Li
Information and communication technology (ICT) products exhibit positive network effects.The dynamic process of ICT markets evolution has two intrinsic characteristics: (1) customers are influenced by each others’ purchasing decision; (2) customers are intelligent agents with bounded rationality.Guided by complex systems theory, we construct an agent-based model and simulate on complex networks to examine how the evolution can arise from the interaction of customers, which occur when they make expectations about the future installed base of a product by the fraction of neighbors who are using the same product in his personal network.We demonstrate that network effects play an important role in the evolution of markets share, which make even an inferior product can dominate the whole market.We also find that the intensity of customers’ communication can influence whether the best initial strategy for firms is to improve product quality or expand their installed base.
Microstructural Evolution During Friction Stir Welding of Mild Steel and Ni-Based Alloy 625
Fernandez, Johnnatan Rodriguez; Ramirez, Antonio J.
2017-03-01
Microstructure evolution during friction stir welding (FSW) of mild steel and Ni-based alloy 625 was studied. Regarding the Ni-based alloy, the welding process led to grain refinement caused by discontinuous and continuous dynamic recrystallization, where bulging of the pre-existing grains and subgrain rotation were the primary mechanisms of recrystallization. In the steel, discontinuous dynamic recrystallization was identified as the recovery process experienced by the austenite. Simple shear textures were observed in the regions affected by the deformation of both materials. Although the allotropic transformation obscured the deformation history, the thermo-mechanically affected zone was identified in the steel by simple shear texture components. A new methodology for the study of texture evolution based on rotations of the slip systems using pole figures is presented as an approximation to describe the texture evolution in FSW.
Microstructural Evolution During Friction Stir Welding of Mild Steel and Ni-Based Alloy 625
Fernandez, Johnnatan Rodriguez; Ramirez, Antonio J.
2017-01-01
Microstructure evolution during friction stir welding (FSW) of mild steel and Ni-based alloy 625 was studied. Regarding the Ni-based alloy, the welding process led to grain refinement caused by discontinuous and continuous dynamic recrystallization, where bulging of the pre-existing grains and subgrain rotation were the primary mechanisms of recrystallization. In the steel, discontinuous dynamic recrystallization was identified as the recovery process experienced by the austenite. Simple shear textures were observed in the regions affected by the deformation of both materials. Although the allotropic transformation obscured the deformation history, the thermo-mechanically affected zone was identified in the steel by simple shear texture components. A new methodology for the study of texture evolution based on rotations of the slip systems using pole figures is presented as an approximation to describe the texture evolution in FSW.
Sex-Based Selectivity of PPARγ Regulation in Th1, Th2, and Th17 Differentiation
Hong-Jai Park
2016-08-01
Full Text Available Peroxisome proliferator-activated receptor gamma (PPARγ has recently been recognized to regulate adaptive immunity through Th17 differentiation, Treg functions, and TFH responses. However, its role in adaptive immunity and autoimmune disease is still not clear, possibly due to sexual differences. Here, we investigated in vitro treatment study with the PPARγ agonist pioglitazone to compare Th1, Th2, and Th17 differentiation in male and female mouse splenic T cells. Pioglitazone treatment significantly inhibited various effector T cell differentiations including Th1, Th2, and Th17 cells from female naïve T cells, but it selectively reduced IL-17 production in male Th17 differentiation. Interestingly, pioglitazone and estradiol (E2 co-treatment of T cells in males inhibited differentiation of Th1, Th2, and Th17 cells, suggesting a mechanism for the greater sensitivity of PPARγ to ligand treatment in the regulation of effector T cell differentiation in females. Collectively, these results demonstrate that PPARγ selectively inhibits Th17 differentiation only in male T cells and modulates Th1, Th2, and Th17 differentiation in female T cells based on different level of estrogen exposure. Accordingly, PPARγ could be an important immune regulator of sexual differences in adaptive immunity.
Differential blood-based biomarkers of psychopathological dimensions of schizophrenia.
Garcia-Alvarez, Leticia; Garcia-Portilla, Maria Paz; Gonzalez-Blanco, Leticia; Saiz Martinez, Pilar Alejandra; de la Fuente-Tomas, Lorena; Menendez-Miranda, Isabel; Iglesias, Celso; Bobes, Julio
Symptomatology of schizophrenia is heterogeneous, there is not any pathognomonic symptom. Moreover, the diagnosis is difficult, since it is based on subjective information, instead of markers. The purpose of this study is to provide a review of the current status of blood-based biomarkers of psychopathological dimensions of schizophrenia. Inflammatory, hormonal or metabolic dysfunctions have been identified in patients with schizophrenia and it has attempted to establish biomarkers responsible for these dysfunctions. The identification of these biomarkers could contribute to the diagnosis and treatment of schizophrenia. Copyright © 2016 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.
Protein-based signatures of functional evolution in Plasmodium falciparum.
Gardner, Kate B; Sinha, Ipsita; Bustamante, Leyla Y; Day, Nicholas Pj; White, Nicholas J; Woodrow, Charles J
2011-09-14
neutral evolution on a scale that appears unrivalled in biology. This distinct evolutionary landscape has potential to confound analytical methods developed for other genera. Against this tide of genetic drift, polymorphisms mediating functional change stand out to such an extent that evolutionary context provides a useful signal for identifying the molecular basis of drug resistance in malaria parasites, a finding that is of relevance to both genome-wide and candidate gene studies in this genus.
Protein-based signatures of functional evolution in Plasmodium falciparum
Day Nicholas PJ
2011-09-01
prospectively definable domains subject to neutral or nearly neutral evolution on a scale that appears unrivalled in biology. This distinct evolutionary landscape has potential to confound analytical methods developed for other genera. Against this tide of genetic drift, polymorphisms mediating functional change stand out to such an extent that evolutionary context provides a useful signal for identifying the molecular basis of drug resistance in malaria parasites, a finding that is of relevance to both genome-wide and candidate gene studies in this genus.
Activity-based differentiation of pathologists' workload in surgical pathology.
Meijer, G A; Oudejans, J J; Koevoets, J J M; Meijer, C J L M
2009-06-01
Adequate budget control in pathology practice requires accurate allocation of resources. Any changes in types and numbers of specimens handled or protocols used will directly affect the pathologists' workload and consequently the allocation of resources. The aim of the present study was to develop a model for measuring the pathologists' workload that can take into account the changes mentioned above. The diagnostic process was analyzed and broken up into separate activities. The time needed to perform these activities was measured. Based on linear regression analysis, for each activity, the time needed was calculated as a function of the number of slides or blocks involved. The total pathologists' time required for a range of specimens was calculated based on standard protocols and validated by comparing to actually measured workload. Cutting up, microscopic procedures and dictating turned out to be highly correlated to number of blocks and/or slides per specimen. Calculated workload per type of specimen was significantly correlated to the actually measured workload. Modeling pathologists' workload based on formulas that calculate workload per type of specimen as a function of the number of blocks and slides provides a basis for a comprehensive, yet flexible, activity-based costing system for pathology.
Differential expression analysis of RNA-seq data at single-base resolution.
Frazee, Alyssa C; Sabunciyan, Sarven; Hansen, Kasper D; Irizarry, Rafael A; Leek, Jeffrey T
2014-07-01
RNA-sequencing (RNA-seq) is a flexible technology for measuring genome-wide expression that is rapidly replacing microarrays as costs become comparable. Current differential expression analysis methods for RNA-seq data fall into two broad classes: (1) methods that quantify expression within the boundaries of genes previously published in databases and (2) methods that attempt to reconstruct full length RNA transcripts. The first class cannot discover differential expression outside of previously known genes. While the second approach does possess discovery capabilities, statistical analysis of differential expression is complicated by the ambiguity and variability incurred while assembling transcripts and estimating their abundances. Here, we propose a novel method that first identifies differentially expressed regions (DERs) of interest by assessing differential expression at each base of the genome. The method then segments the genome into regions comprised of bases showing similar differential expression signal, and then assigns a measure of statistical significance to each region. Optionally, DERs can be annotated using a reference database of genomic features. We compare our approach with leading competitors from both current classes of differential expression methods and highlight the strengths and weaknesses of each. A software implementation of our method is available on github (https://github.com/alyssafrazee/derfinder).
Evolution of Cell-Type-Specific RNA Aptamers Via Live Cell-Based SELEX.
Zhou, Jiehua; Rossi, John J
2016-01-01
Live cell-based SELEX (Systematic Evolution of Ligand EXponential enrichment) is a promising approach for identifying aptamers that can selectively bind to a cell-surface antigen or a particular target cell population. In particular, it offers a facile selection strategy for some special cell-surface proteins that are original glycosylated or heavily post-translationally modified, and are unavailable in their native/active conformation after in vitro expression and purification. In this chapter, we describe evolution of cell-type-specific RNA aptamers targeting the human CCR5 by combining the live cell-based SELEX strategy with high-throughput sequencing (HTS) and bioinformatics analysis.
Fault Detection Based on Tracking Differentiator Applied on the Suspension System of Maglev Train
Hehong Zhang
2015-01-01
Full Text Available A fault detection method based on the optimized tracking differentiator is introduced. It is applied on the acceleration sensor of the suspension system of maglev train. It detects the fault of the acceleration sensor by comparing the acceleration integral signal with the speed signal obtained by the optimized tracking differentiator. This paper optimizes the control variable when the states locate within or beyond the two-step reachable region to improve the performance of the approximate linear discrete tracking differentiator. Fault-tolerant control has been conducted by feedback based on the speed signal acquired from the optimized tracking differentiator when the acceleration sensor fails. The simulation and experiment results show the practical usefulness of the presented method.
An Intrinsic Characterization of Bonnet Surfaces Based on a Closed Differential Ideal
Paul Bracken
2014-01-01
Full Text Available The structure equations for a two-dimensional manifold are introduced and two results based on the Codazzi equations pertinent to the study of isometric surfaces are obtained from them. Important theorems pertaining to isometric surfaces are stated and a theorem due to Bonnet is obtained. A transformation for the connection forms is developed. It is proved that the angle of deformation must be harmonic, and that the differentials of many of the important variables generate a closed differential ideal. This implies that a coordinate system exists in which many of the variables satisfy particular ordinary differential equations, and these results can be used to characterize Bonnet surfaces.
Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events
Bo Li
2014-01-01
Full Text Available Agent based simulation method has become a prominent approach in computational modeling and analysis of public emergency management in social science research. The group emotions evolution, information diffusion, and collective behavior selection make extreme incidents studies a complex system problem, which requires new methods for incidents management and strategy evaluation. This paper studies the group emotion evolution and intervention strategy effectiveness using agent based simulation method. By employing a computational experimentation methodology, we construct the group emotion evolution as a complex system and test the effects of three strategies. In addition, the events-chain model is proposed to model the accumulation influence of the temporal successive events. Each strategy is examined through three simulation experiments, including two make-up scenarios and a real case study. We show how various strategies could impact the group emotion evolution in terms of the complex emergence and emotion accumulation influence in extreme events. This paper also provides an effective method of how to use agent-based simulation for the study of complex collective behavior evolution problem in extreme incidents, emergency, and security study domains.
Regulation, cell differentiation and protein-based inheritance.
Malagnac, Fabienne; Silar, Philippe
2006-11-01
Recent research using fungi as models provide new insight into the ability of regulatory networks to generate cellular states that are sufficiently stable to be faithfully transmitted to daughter cells, thereby generating epigenetic inheritance. Such protein-based inheritance is driven by infectious factors endowed with properties usually displayed by prions. We emphasize the contribution of regulatory networks to the emerging properties displayed by cells.
Spether, Dominik; Scharpf, Marcus; Hennenlotter, Jörg; Schwentner, Christian; Neugebauer, Alexander; Nüßle, Daniela; Fischer, Klaus; Zappe, Hans; Stenzl, Arnulf; Fend, Falko; Seifert, Andreas; Enderle, Markus
2015-01-01
Complete surgical removal of cancer tissue with effective preservation of healthy tissue is one of the most important challenges in modern oncology. We present a method for real-time, in situ differentiation of tissue based on optical emission spectroscopy (OES) performed during electrosurgery not requiring any biomarkers, additional light sources or other excitation processes. The analysis of the optical emission spectra, enables the differentiation of healthy and tumorous tissue. By using m...
Bauwens, Celine L.; Toms, Derek; Ungrin, Mark
2016-01-01
Cardiac differentiation of human pluripotent stems cells (hPSCs) is typically carried out in suspension cell aggregates. Conventional aggregate formation of hPSCs involves dissociating cell colonies into smaller clumps, with size control of the clumps crudely controlled by pipetting the cell suspension until the desired clump size is achieved. One of the main challenges of conventional aggregate-based cardiac differentiation of hPSCs is that culture heterogeneity and spatial disorganization l...