Optimization of dynamic economic dispatch with valve-point effect using chaotic sequence based differential evolution algorithms

International Nuclear Information System (INIS)

He Dakuo; Dong Gang; Wang Fuli; Mao Zhizhong

2011-01-01

A chaotic sequence based differential evolution (DE) approach for solving the dynamic economic dispatch problem (DEDP) with valve-point effect is presented in this paper. The proposed method combines the DE algorithm with the local search technique to improve the performance of the algorithm. DE is the main optimizer, while an approximated model for local search is applied to fine tune in the solution of the DE run. To accelerate convergence of DE, a series of constraints handling rules are adopted. An initial population obtained by using chaotic sequence exerts optimal performance of the proposed algorithm. The combined algorithm is validated for two test systems consisting of 10 and 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. The proposed combined method outperforms other algorithms reported in literatures for DEDP considering valve-point effects.

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

Science.gov (United States)

Binol, Hamidullah; Bal, Abdullah; Cukur, Huseyin

2015-10-01

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

A Self Adaptive Differential Evolution Algorithm for Global Optimization

Science.gov (United States)

Kumar, Pravesh; Pant, Millie

This paper presents a new Differential Evolution algorithm based on hybridization of adaptive control parameters and trigonometric mutation. First we propose a self adaptive DE named ADE where choice of control parameter F and Cr is not fixed at some constant value but is taken iteratively. The proposed algorithm is further modified by applying trigonometric mutation in it and the corresponding algorithm is named as ATDE. The performance of ATDE is evaluated on the set of 8 benchmark functions and the results are compared with the classical DE algorithm in terms of average fitness function value, number of function evaluations, convergence time and success rate. The numerical result shows the competence of the proposed algorithm.

An Orthogonal Learning Differential Evolution Algorithm for Remote Sensing Image Registration

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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.

Genetic algorithm-based optimization of testing and maintenance under uncertain unavailability and cost estimation: A survey of strategies for harmonizing evolution and accuracy

International Nuclear Information System (INIS)

Villanueva, J.F.; Sanchez, A.I.; Carlos, S.; Martorell, S.

2008-01-01

This paper presents the results of a survey to show the applicability of an approach based on a combination of distribution-free tolerance interval and genetic algorithms for testing and maintenance optimization of safety-related systems based on unavailability and cost estimation acting as uncertain decision criteria. Several strategies have been checked using a combination of Monte Carlo (simulation)--genetic algorithm (search-evolution). Tolerance intervals for the unavailability and cost estimation are obtained to be used by the genetic algorithms. Both single- and multiple-objective genetic algorithms are used. In general, it is shown that the approach is a robust, fast and powerful tool that performs very favorably in the face of noise in the output (i.e. uncertainty) and it is able to find the optimum over a complicated, high-dimensional nonlinear space in a tiny fraction of the time required for enumeration of the decision space. This approach reduces the computational effort by means of providing appropriate balance between accuracy of simulation and evolution; however, negative effects are also shown when a not well-balanced accuracy-evolution couple is used, which can be avoided or mitigated with the use of a single-objective genetic algorithm or the use of a multiple-objective genetic algorithm with additional statistical information

Optimization of wind farm turbines layout using an evolutive algorithm

International Nuclear Information System (INIS)

Gonzalez, Javier Serrano; Santos, Jesus Riquelme; Payan, Manuel Burgos; Gonzalez Rodriguez, Angel G.; Mora, Jose Castro

2010-01-01

The optimum wind farm configuration problem is discussed in this paper and an evolutive algorithm to optimize the wind farm layout is proposed. The algorithm's optimization process is based on a global wind farm cost model using the initial investment and the present value of the yearly net cash flow during the entire wind-farm life span. The proposed algorithm calculates the yearly income due to the sale of the net generated energy taking into account the individual wind turbine loss of production due to wake decay effects and it can deal with areas or terrains with non-uniform load-bearing capacity soil and different roughness length for every wind direction or restrictions such as forbidden areas or limitations in the number of wind turbines or the investment. The results are first favorably compared with those previously published and a second collection of test cases is used to proof the performance and suitability of the proposed evolutive algorithm to find the optimum wind farm configuration. (author)

RDEL: Restart Differential Evolution algorithm with Local Search Mutation for global numerical optimization

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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.

Simulating Evolution of Drosophila melanogaster Ebony Mutants Using a Genetic Algorithm

DEFF Research Database (Denmark)

Helles, Glennie

2009-01-01

Genetic algorithms are generally quite easy to understand and work with, and they are a popular choice in many cases. One area in which genetic algorithms are widely and successfully used is artificial life where they are used to simulate evolution of artificial creatures. However, despite...... their suggestive name, simplicity and popularity in artificial life, they do not seem to have gained a footing within the field of population genetics to simulate evolution of real organisms --- possibly because genetic algorithms are based on a rather crude simplification of the evolutionary mechanisms known...

Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

Science.gov (United States)

Guo, Zhan; Yan, Xuefeng

2018-04-01

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

An Improved Binary Differential Evolution Algorithm to Infer Tumor Phylogenetic Trees.

Science.gov (United States)

Liang, Ying; Liao, Bo; Zhu, Wen

2017-01-01

Tumourigenesis is a mutation accumulation process, which is likely to start with a mutated founder cell. The evolutionary nature of tumor development makes phylogenetic models suitable for inferring tumor evolution through genetic variation data. Copy number variation (CNV) is the major genetic marker of the genome with more genes, disease loci, and functional elements involved. Fluorescence in situ hybridization (FISH) accurately measures multiple gene copy number of hundreds of single cells. We propose an improved binary differential evolution algorithm, BDEP, to infer tumor phylogenetic tree based on FISH platform. The topology analysis of tumor progression tree shows that the pathway of tumor subcell expansion varies greatly during different stages of tumor formation. And the classification experiment shows that tree-based features are better than data-based features in distinguishing tumor. The constructed phylogenetic trees have great performance in characterizing tumor development process, which outperforms other similar algorithms.

Parameter Estimation of Damped Compound Pendulum Differential Evolution Algorithm

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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.

A DIFFERENTIAL EVOLUTION ALGORITHM DEVELOPED FOR A NURSE SCHEDULING PROBLEM

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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.

Algebraic dynamics solutions and algebraic dynamics algorithm for nonlinear partial differential evolution equations of dynamical systems

Institute of Scientific and Technical Information of China (English)

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.

The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

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Dazhi Jiang

2015-01-01

Full Text Available At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.

An implementation of differential evolution algorithm for inversion of geoelectrical data

Science.gov (United States)

Balkaya, Çağlayan

2013-11-01

Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.

An Improved Differential Evolution Algorithm for Maritime Collision Avoidance Route Planning

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Yu-xin Zhao

2014-01-01

Full Text Available High accuracy navigation and surveillance systems are pivotal to ensure efficient ship route planning and marine safety. Based on existing ship navigation and maritime collision prevention rules, an improved approach for collision avoidance route planning using a differential evolution algorithm was developed. Simulation results show that the algorithm is capable of significantly enhancing the optimized route over current methods. It has the potential to be used as a tool to generate optimal vessel routing in the presence of conflicts.

Differential Evolution algorithm applied to FSW model calibration

Science.gov (United States)

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.

A New Evolutionary Algorithm Based on Bacterial Evolution and Its Application for Scheduling A Flexible Manufacturing System

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Chandramouli Anandaraman

2012-01-01

Full Text Available A new evolutionary computation algorithm, Superbug algorithm, which simulates evolution of bacteria in a culture, is proposed. The algorithm is developed for solving large scale optimization problems such as scheduling, transportation and assignment problems. In this work, the algorithm optimizes machine schedules in a Flexible Manufacturing System (FMS by minimizing makespan. The FMS comprises of four machines and two identical Automated Guided Vehicles (AGVs. AGVs are used for carrying jobs between the Load/Unload (L/U station and the machines. Experimental results indicate the efficiency of the proposed algorithm in its optimization performance in scheduling is noticeably superior to other evolutionary algorithms when compared to the best results reported in the literature for FMS Scheduling.

Evolution of Strategies for "Prisoner's Dilemma" using Genetic Algorithm

OpenAIRE

Heinz, Jan

2010-01-01

The subject of this thesis is the software application "Prisoner's Dilemma". The program creates a population of players of "Prisoner's Dilemma", has them play against each other, and - based on their results - performs an evolution of their strategies by means of a genetic algorithm (selection, mutation, and crossover). The program was written in Microsoft Visual Studio, in the C++ programming language, and its interface makes use of the .NET Framework. The thesis includes examples of strate...

DE and NLP Based QPLS Algorithm

Science.gov (United States)

Yu, Xiaodong; Huang, Dexian; Wang, Xiong; Liu, Bo

As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.

A Hybrid Backtracking Search Optimization Algorithm with Differential Evolution

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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.

Solving Bi-Objective Optimal Power Flow using Hybrid method of Biogeography-Based Optimization and Differential Evolution Algorithm: A case study of the Algerian Electrical Network

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Ouafa Herbadji

2016-03-01

Full Text Available This paper proposes a new hybrid metaheuristique algorithm based on the hybridization of Biogeography-based optimization with the Differential Evolution for solving the optimal power flow problem with emission control. The biogeography-based optimization (BBO algorithm is strongly influenced by equilibrium theory of island biogeography, mainly through two steps: Migration and Mutation. Differential Evolution (DE is one of the best Evolutionary Algorithms for global optimization. The hybridization of these two methods is used to overcome traps of local optimal solutions and problems of time consumption. The objective of this paper is to minimize the total fuel cost of generation, total emission, total real power loss and also maintain an acceptable system performance in terms of limits on generator real power, bus voltages and power flow of transmission lines. In the present work, BBO/DE has been applied to solve the optimal power flow problems on IEEE 30-bus test system and the Algerian electrical network 114 bus. The results obtained from this method show better performances compared with DE, BBO and other well known metaheuristique and evolutionary optimization methods.

Application of differential evolution algorithm on self-potential data.

Science.gov (United States)

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.

Hybrid Discrete Differential Evolution Algorithm for Lot Splitting with Capacity Constraints in Flexible Job Scheduling

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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.

Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos; Mariani, Viviana Cocco

2007-01-01

Global optimization based on evolutionary algorithms can be used as the important component for many engineering optimization problems. Evolutionary algorithms have yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization problems in the power systems area. Differential evolution (DE) is a simple and efficient evolutionary algorithm for function optimization over continuous spaces. It has reportedly outperformed search heuristics when tested over both benchmark and real world problems. This paper proposes improved DE algorithms for solving economic load dispatch problems that take into account nonlinear generator features such as ramp rate limits and prohibited operating zones in the power system operation. The DE algorithms and its variants are validated for two test systems consisting of 6 and 15 thermal units. Various DE approaches outperforms other state of the art algorithms reported in the literature in solving load dispatch problems with generator constraints

Differential Evolution Algorithm with Self-Adaptive Population Resizing Mechanism

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Xu Wang

2013-01-01

Full Text Available A differential evolution (DE algorithm with self-adaptive population resizing mechanism, SapsDE, is proposed to enhance the performance of DE by dynamically choosing one of two mutation strategies and tuning control parameters in a self-adaptive manner. More specifically, more appropriate mutation strategies along with its parameter settings can be determined adaptively according to the previous status at different stages of the evolution process. To verify the performance of SapsDE, 17 benchmark functions with a wide range of dimensions, and diverse complexities are used. Nonparametric statistical procedures were performed for multiple comparisons between the proposed algorithm and five well-known DE variants from the literature. Simulation results show that SapsDE is effective and efficient. It also exhibits much more superiorresultsthan the other five algorithms employed in the comparison in most of the cases.

Long-Term Scheduling of Large-Scale Cascade Hydropower Stations Using Improved Differential Evolution Algorithm

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Xiaohao Wen

2018-03-01

Full Text Available Long-term scheduling of large cascade hydropower stations (LSLCHS is a complex problem of high dimension, nonlinearity, coupling and complex constraint. In view of the above problem, we present an improved differential evolution (iLSHADE algorithm based on LSHADE, a state-of-the-art evolutionary algorithm. iLSHADE uses new mutation strategies “current to pbest/2-rand” to obtain wider search range and accelerate convergence with the preventing individual repeated failure evolution (PIRFE strategy. The handling of complicated constraints strategy of ε-constrained method is presented to handle outflow, water level and output constraints in the cascade reservoir operation. Numerical experiments of 10 benchmark functions have been done, showing that iLSHADE has stable convergence and high efficiency. Furthermore, we demonstrate the performance of the iLSHADE algorithm by comparing it with other improved differential evolution algorithms for LSLCHS in four large hydropower stations of the Jinsha River. With the applications of iLSHADE in reservoir operation, LSLCHS can obtain more power generation benefit than other alternatives in dry, normal, and wet years. The results of numerical experiments and case studies show that the iLSHADE has a distinct optimization effect and good stability, and it is a valid and reliable tool to solve LSLCHS problem.

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

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Rafał Dreżewski

2017-08-01

Full Text Available Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

Differential evolution algorithm based automatic generation control for interconnected power systems with

Directory of Open Access Journals (Sweden)

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.

A comparative analysis of particle swarm optimization and differential evolution algorithms for parameter estimation in nonlinear dynamic systems

International Nuclear Information System (INIS)

Banerjee, Amit; Abu-Mahfouz, Issam

2014-01-01

The use of evolutionary algorithms has been popular in recent years for solving the inverse problem of identifying system parameters given the chaotic response of a dynamical system. The inverse problem is reformulated as a minimization problem and population-based optimizers such as evolutionary algorithms have been shown to be efficient solvers of the minimization problem. However, to the best of our knowledge, there has been no published work that evaluates the efficacy of using the two most popular evolutionary techniques – particle swarm optimization and differential evolution algorithm, on a wide range of parameter estimation problems. In this paper, the two methods along with their variants (for a total of seven algorithms) are applied to fifteen different parameter estimation problems of varying degrees of complexity. Estimation results are analyzed using nonparametric statistical methods to identify if an algorithm is statistically superior to others over the class of problems analyzed. Results based on parameter estimation quality suggest that there are significant differences between the algorithms with the newer, more sophisticated algorithms performing better than their canonical versions. More importantly, significant differences were also found among variants of the particle swarm optimizer and the best performing differential evolution algorithm

A novel Random Walk algorithm with Compulsive Evolution for heat exchanger network synthesis

International Nuclear Information System (INIS)

Xiao, Yuan; Cui, Guomin

2017-01-01

Highlights: • A novel Random Walk Algorithm with Compulsive Evolution is proposed for HENS. • A simple and feasible evolution strategy is presented in RWCE algorithm. • The integer and continuous variables of HEN are optimized simultaneously in RWCE. • RWCE is demonstrated a relatively strong global search ability in HEN optimization. - Abstract: The heat exchanger network (HEN) synthesis can be characterized as highly combinatorial, nonlinear and nonconvex, contributing to unmanageable computational time and a challenge in identifying the global optimal network design. Stochastic methods are robust and show a powerful global optimizing ability. Based on the common characteristic of different stochastic methods, namely randomness, a novel Random Walk algorithm with Compulsive Evolution (RWCE) is proposed to achieve the best possible total annual cost of heat exchanger network with the relatively simple and feasible evolution strategy. A population of heat exchanger networks is first randomly initialized. Next, the heat load of heat exchanger for each individual is randomly expanded or contracted in order to optimize both the integer and continuous variables simultaneously and to obtain the lowest total annual cost. Besides, when individuals approach to local optima, there is a certain probability for them to compulsively accept the imperfect networks in order to keep the population diversity and ability of global optimization. The presented method is then applied to heat exchanger network synthesis cases from the literature to compare the best results published. RWCE consistently has a lower computed total annual cost compared to previously published results.

Application of differential evolution algorithm on self-potential data.

Directory of Open Access Journals (Sweden)

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.

Harmony Search Based Parameter Ensemble Adaptation for Differential Evolution

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Rammohan Mallipeddi

2013-01-01

Full Text Available In differential evolution (DE algorithm, depending on the characteristics of the problem at hand and the available computational resources, different strategies combined with a different set of parameters may be effective. In addition, a single, well-tuned combination of strategies and parameters may not guarantee optimal performance because different strategies combined with different parameter settings can be appropriate during different stages of the evolution. Therefore, various adaptive/self-adaptive techniques have been proposed to adapt the DE strategies and parameters during the course of evolution. In this paper, we propose a new parameter adaptation technique for DE based on ensemble approach and harmony search algorithm (HS. In the proposed method, an ensemble of parameters is randomly sampled which form the initial harmony memory. The parameter ensemble evolves during the course of the optimization process by HS algorithm. Each parameter combination in the harmony memory is evaluated by testing them on the DE population. The performance of the proposed adaptation method is evaluated using two recently proposed strategies (DE/current-to-pbest/bin and DE/current-to-gr_best/bin as basic DE frameworks. Numerical results demonstrate the effectiveness of the proposed adaptation technique compared to the state-of-the-art DE based algorithms on a set of challenging test problems (CEC 2005.

Parameter optimization of differential evolution algorithm for automatic playlist generation problem

Science.gov (United States)

Alamag, Kaye Melina Natividad B.; Addawe, Joel M.

2017-11-01

With the digitalization of music, the number of collection of music increased largely and there is a need to create lists of music that filter the collection according to user preferences, thus giving rise to the Automatic Playlist Generation Problem (APGP). Previous attempts to solve this problem include the use of search and optimization algorithms. If a music database is very large, the algorithm to be used must be able to search the lists thoroughly taking into account the quality of the playlist given a set of user constraints. In this paper we perform an evolutionary meta-heuristic optimization algorithm, Differential Evolution (DE) using different combination of parameter values and select the best performing set when used to solve four standard test functions. Performance of the proposed algorithm is then compared with normal Genetic Algorithm (GA) and a hybrid GA with Tabu Search. Numerical simulations are carried out to show better results from Differential Evolution approach with the optimized parameter values.

An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

Science.gov (United States)

Li, Bai; Gong, Li-gang; Yang, Wen-lun

2014-01-01

Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Research on the Compression Algorithm of the Infrared Thermal Image Sequence Based on Differential Evolution and Double Exponential Decay Model

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Zhang, Jin-Yu; Meng, Xiang-Bing; Xu, Wei; Zhang, Wei; Zhang, Yong

2014-01-01

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. PMID:24696649

Research on the Compression Algorithm of the Infrared Thermal Image Sequence Based on Differential Evolution and Double Exponential Decay Model

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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.

An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning

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Bai Li

2014-01-01

Full Text Available Unmanned combat aerial vehicles (UCAVs have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC algorithm improved by a balance-evolution strategy (BES is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

Network-based recommendation algorithms: A review

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Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš

2016-06-01

Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.

Application of evolution strategy algorithm for optimization of a single-layer sound absorber

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Morteza Gholamipoor

2014-12-01

Full Text Available Depending on different design parameters and limitations, optimization of sound absorbers has always been a challenge in the field of acoustic engineering. Various methods of optimization have evolved in the past decades with innovative method of evolution strategy gaining more attention in the recent years. Based on their simplicity and straightforward mathematical representations, single-layer absorbers have been widely used in both engineering and industrial applications and an optimized design for these absorbers has become vital. In the present study, the method of evolution strategy algorithm is used for optimization of a single-layer absorber at both a particular frequency and an arbitrary frequency band. Results of the optimization have been compared against different methods of genetic algorithm and penalty functions which are proved to be favorable in both effectiveness and accuracy. Finally, a single-layer absorber is optimized in a desired range of frequencies that is the main goal of an industrial and engineering optimization process.

An approach of community evolution based on gravitational relationship refactoring in dynamic networks

International Nuclear Information System (INIS)

Yin, Guisheng; Chi, Kuo; Dong, Yuxin; Dong, Hongbin

2017-01-01

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.

Analog Group Delay Equalizers Design Based on Evolutionary Algorithm

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M. Laipert

2006-04-01

Full Text Available This paper deals with a design method of the analog all-pass filter designated for equalization of the group delay frequency response of the analog filter. This method is based on usage of evolutionary algorithm, the Differential Evolution algorithm in particular. We are able to design such equalizers to be obtained equal-ripple group delay frequency response in the pass-band of the low-pass filter. The procedure works automatically without an input estimation. The method is presented on solving practical examples.

Improved Differential Evolution Algorithm for Parameter Estimation to Improve the Production of Biochemical Pathway

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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

Cloud Computing Task Scheduling Based on Cultural Genetic Algorithm

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Li Jian-Wen

2016-01-01

Full Text Available The task scheduling strategy based on cultural genetic algorithm(CGA is proposed in order to improve the efficiency of task scheduling in the cloud computing platform, which targets at minimizing the total time and cost of task scheduling. The improved genetic algorithm is used to construct the main population space and knowledge space under cultural framework which get independent parallel evolution, forming a mechanism of mutual promotion to dispatch the cloud task. Simultaneously, in order to prevent the defects of the genetic algorithm which is easy to fall into local optimum, the non-uniform mutation operator is introduced to improve the search performance of the algorithm. The experimental results show that CGA reduces the total time and lowers the cost of the scheduling, which is an effective algorithm for the cloud task scheduling.

A Hybrid Multiobjective Differential Evolution Algorithm and Its Application to the Optimization of Grinding and Classification

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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.

Optimization Shape of Variable Capacitance Micromotor Using Differential Evolution Algorithm

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A. Ketabi

2010-01-01

Full Text Available A new method for optimum shape design of variable capacitance micromotor (VCM using Differential Evolution (DE, a stochastic search algorithm, is presented. In this optimization exercise, the objective function aims to maximize torque value and minimize the torque ripple, where the geometric parameters are considered to be the variables. The optimization process is carried out using a combination of DE algorithm and FEM analysis. Fitness value is calculated by FEM analysis using COMSOL3.4, and the DE algorithm is realized by MATLAB7.4. The proposed method is applied to a VCM with 8 poles at the stator and 6 poles at the rotor. The results show that the optimized micromotor using DE algorithm had higher torque value and lower torque ripple, indicating the validity of this methodology for VCM design.

Design of Test Wrapper Scan Chain Based on Differential Evolution

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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.

Differential evolution based method for total transfer capability ...

African Journals Online (AJOL)

The application of Differential Evolution (DE) to compute the Total Transfer Capability (TTC) in deregulated market is proposed in this paper. The objective is to maximize a specific point-to-point power transaction without violating system constraints using DE. This algorithm is based on full ac optimal power flow solution to ...

Implementation of Evolution Strategies (ES Algorithm to Optimization Lovebird Feed Composition

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Agung Mustika Rizki

2017-05-01

Full Text Available Lovebird current society, especially popular among bird lovers. Some people began to try to develop the cultivation of these birds. In the cultivation process to consider the composition of feed to produce a quality bird. Determining the feed is not easy because it must consider the cost and need for vitamin Lovebird. This problem can be solved by the algorithm Evolution Strategies (ES. Based on test results obtained optimal fitness value of 0.3125 using a population size of 100 and optimal fitness value of 0.3267 in the generation of 1400. 

Quantum-circuit model of Hamiltonian search algorithms

International Nuclear Information System (INIS)

Roland, Jeremie; Cerf, Nicolas J.

2003-01-01

We analyze three different quantum search algorithms, namely, the traditional circuit-based Grover's algorithm, its continuous-time analog by Hamiltonian evolution, and the quantum search by local adiabatic evolution. We show that these algorithms are closely related in the sense that they all perform a rotation, at a constant angular velocity, from a uniform superposition of all states to the solution state. This makes it possible to implement the two Hamiltonian-evolution algorithms on a conventional quantum circuit, while keeping the quadratic speedup of Grover's original algorithm. It also clarifies the link between the adiabatic search algorithm and Grover's algorithm

DSTATCOM allocation in distribution networks considering reconfiguration using differential evolution algorithm

International Nuclear Information System (INIS)

Jazebi, S.; Hosseinian, S.H.; Vahidi, B.

2011-01-01

Highlights: → Reconfiguration and DSTATCOM allocation are implemented for RDS planning. → Differential evolution algorithm is applied to solve the nonlinear problem. → Optimal status of tie switches, DSTATCOM size and location are determined. → The goal is to minimize network losses and to improve voltage profile. → The results show the effectiveness of the proposed method to satisfy objectives. -- Abstract: The main idea in distribution network reconfiguration is usually to reduce loss by changing the status of sectionalizing switches and determining appropriate tie switches. Recently Distribution FACTS (DFACTS) devices such as DSTATCOM also have been planned for loss reduction and voltage profile improvement in steady state conditions. This paper implements a combinatorial process based on reconfiguration and DSTATCOM allocation in order to mitigate losses and improve voltage profile in power distribution networks. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. Differential evolution algorithm (DEA) has been used to solve and overcome the complicity of this combinatorial nonlinear optimization problem. To validate the accuracy of results a comparison with particle swarm optimization (PSO) has been made. Simulations have been applied on 69 and 83 busses distribution test systems. All optimization results show the effectiveness of the combinatorial approach in loss reduction and voltage profile improvement.

SIGNUM: A Matlab, TIN-based landscape evolution model

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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.

Ant-Based Phylogenetic Reconstruction (ABPR: A new distance algorithm for phylogenetic estimation based on ant colony optimization

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Karla Vittori

2008-12-01

Full Text Available We propose a new distance algorithm for phylogenetic estimation based on Ant Colony Optimization (ACO, named Ant-Based Phylogenetic Reconstruction (ABPR. ABPR joins two taxa iteratively based on evolutionary distance among sequences, while also accounting for the quality of the phylogenetic tree built according to the total length of the tree. Similar to optimization algorithms for phylogenetic estimation, the algorithm allows exploration of a larger set of nearly optimal solutions. We applied the algorithm to four empirical data sets of mitochondrial DNA ranging from 12 to 186 sequences, and from 898 to 16,608 base pairs, and covering taxonomic levels from populations to orders. We show that ABPR performs better than the commonly used Neighbor-Joining algorithm, except when sequences are too closely related (e.g., population-level sequences. The phylogenetic relationships recovered at and above species level by ABPR agree with conventional views. However, like other algorithms of phylogenetic estimation, the proposed algorithm failed to recover expected relationships when distances are too similar or when rates of evolution are very variable, leading to the problem of long-branch attraction. ABPR, as well as other ACO-based algorithms, is emerging as a fast and accurate alternative method of phylogenetic estimation for large data sets.

Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation.

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Liu, Xiaofeng; Bai, Fang; Ouyang, Sisheng; Wang, Xicheng; Li, Honglin; Jiang, Hualiang

2009-03-31

Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105-112). Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 A to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 +/- 0.18 seconds per molecule) renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms other four multiple conformer generators in the case of

Cyndi: a multi-objective evolution algorithm based method for bioactive molecular conformational generation

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Li Honglin

2009-03-01

Full Text Available Abstract Background Conformation generation is a ubiquitous problem in molecule modelling. Many applications require sampling the broad molecular conformational space or perceiving the bioactive conformers to ensure success. Numerous in silico methods have been proposed in an attempt to resolve the problem, ranging from deterministic to non-deterministic and systemic to stochastic ones. In this work, we described an efficient conformation sampling method named Cyndi, which is based on multi-objective evolution algorithm. Results The conformational perturbation is subjected to evolutionary operation on the genome encoded with dihedral torsions. Various objectives are designated to render the generated Pareto optimal conformers to be energy-favoured as well as evenly scattered across the conformational space. An optional objective concerning the degree of molecular extension is added to achieve geometrically extended or compact conformations which have been observed to impact the molecular bioactivity (J Comput -Aided Mol Des 2002, 16: 105–112. Testing the performance of Cyndi against a test set consisting of 329 small molecules reveals an average minimum RMSD of 0.864 Å to corresponding bioactive conformations, indicating Cyndi is highly competitive against other conformation generation methods. Meanwhile, the high-speed performance (0.49 ± 0.18 seconds per molecule renders Cyndi to be a practical toolkit for conformational database preparation and facilitates subsequent pharmacophore mapping or rigid docking. The copy of precompiled executable of Cyndi and the test set molecules in mol2 format are accessible in Additional file 1. Conclusion On the basis of MOEA algorithm, we present a new, highly efficient conformation generation method, Cyndi, and report the results of validation and performance studies comparing with other four methods. The results reveal that Cyndi is capable of generating geometrically diverse conformers and outperforms

The Cellular Differential Evolution Based on Chaotic Local Search

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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.

Multi-objective optimization of in-situ bioremediation of groundwater using a hybrid metaheuristic technique based on differential evolution, genetic algorithms and simulated annealing

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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.

Malicious Botnet Survivability Mechanism Evolution Forecasting by Means of a Genetic Algorithm

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Nikolaj Goranin

2012-04-01

Full Text Available Botnets are considered to be among the most dangerous modern malware types and the biggest current threats to global IT infrastructure. Botnets are rapidly evolving, and therefore forecasting their survivability strategies is important for the development of countermeasure techniques. The article propose the botnet-oriented genetic algorithm based model framework, which aimed at forecasting botnet survivability mechanisms. The model may be used as a framework for forecasting the evolution of other characteristics. The efficiency of different survivability mechanisms is evaluated by applying the proposed fitness function. The model application area also covers scientific botnet research and modelling tasks.

An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle

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Milinkovitch Michel C

2007-11-01

Full Text Available Abstract Background Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME principle (aiming at recovering the phylogeny with shortest length is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@-hard class of problems. Results In this paper, we introduce an Ant Colony Optimization (ACO algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. Conclusion We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.

Numerical simulation for Jeffery-Hamel flow and heat transfer of micropolar fluid based on differential evolution algorithm

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Ara, Asmat; Khan, Najeeb Alam; Naz, Farah; Raja, Muhammad Asif Zahoor; Rubbab, Qammar

2018-01-01

This article explores the Jeffery-Hamel flow of an incompressible non-Newtonian fluid inside non-parallel walls and observes the influence of heat transfer in the flow field. The fluid is considered to be micropolar fluid that flows in a convergent/divergent channel. The governing nonlinear partial differential equations (PDEs) are converted to nonlinear coupled ordinary differential equations (ODEs) with the help of a suitable similarity transformation. The resulting nonlinear analysis is determined analytically with the utilization of the Taylor optimization method based on differential evolution (DE) algorithm. In order to understand the flow field, the effects of pertinent parameters such as the coupling parameter, spin gradient viscosity parameter and the Reynolds number have been examined on velocity and temperature profiles. It concedes that the good results can be attained by an implementation of the proposed method. Ultimately, the accuracy of the method is confirmed by comparing the present results with the results obtained by Runge-Kutta method.

Numerical simulation for Jeffery-Hamel flow and heat transfer of micropolar fluid based on differential evolution algorithm

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Asmat Ara

2018-01-01

Full Text Available This article explores the Jeffery-Hamel flow of an incompressible non-Newtonian fluid inside non-parallel walls and observes the influence of heat transfer in the flow field. The fluid is considered to be micropolar fluid that flows in a convergent/divergent channel. The governing nonlinear partial differential equations (PDEs are converted to nonlinear coupled ordinary differential equations (ODEs with the help of a suitable similarity transformation. The resulting nonlinear analysis is determined analytically with the utilization of the Taylor optimization method based on differential evolution (DE algorithm. In order to understand the flow field, the effects of pertinent parameters such as the coupling parameter, spin gradient viscosity parameter and the Reynolds number have been examined on velocity and temperature profiles. It concedes that the good results can be attained by an implementation of the proposed method. Ultimately, the accuracy of the method is confirmed by comparing the present results with the results obtained by Runge-Kutta method.

Calibration of three-axis magnetometers with differential evolution algorithm

International Nuclear Information System (INIS)

Pang, Hongfeng; Zhang, Qi; Wang, Wei; Wang, Junya; Li, Ji; Luo, Shitu; Wan, Chengbiao; Chen, Dixiang; Pan, Mengchun; Luo, Feilu

2013-01-01

The accuracy of three-axis magnetometers is influenced by different scale and bias of each axis and nonorthogonality between axes. One limitation of traditional iteration methods is that initial parameters influence the calibration, thus leading to the local optimal or wrong results. In this paper, a new method is proposed to calibrate three-axis magnetometers. To employ this method, a nonmagnetic rotation platform, a proton magnetometer, a DM-050 three-axis magnetometer and the differential evolution (DE) algorithm are used. The performance of this calibration method is analyzed with simulation and experiment. In simulation, the calibration results of DE, unscented Kalman filter (UKF), recursive least squares (RLS) and genetic algorithm (GA) are compared. RMS error using DE is least, which is reduced from 81.233 nT to 1.567 nT. Experimental results show that comparing with UKF, RLS and GA, the DE algorithm has not only the least calibration error but also the best robustness. After calibration, RMS error is reduced from 68.914 nT to 2.919 nT. In addition, the DE algorithm is not sensitive to initial parameters, which is an important advantage compared with traditional iteration algorithms. The proposed algorithm can avoid the troublesome procedure to select suitable initial parameters, thus it can improve the calibration performance of three-axis magnetometers. - Highlights: • The calibration results and robustness of UKF, GA, RLS and DE algorithm are analyzed. • Calibration error of DE is the least in simulation and experiment. • Comparing with traditional calibration algorithms, DE is not sensitive to initial parameters. • It can improve the calibration performance of three-axis magnetometers

An adaptive left–right eigenvector evolution algorithm for vibration isolation control

International Nuclear Information System (INIS)

Wu, T Y

2009-01-01

The purpose of this research is to investigate the feasibility of utilizing an adaptive left and right eigenvector evolution (ALREE) algorithm for active vibration isolation. As depicted in the previous paper presented by Wu and Wang (2008 Smart Mater. Struct. 17 015048), the structural vibration behavior depends on both the disturbance rejection capability and mode shape distributions, which correspond to the left and right eigenvector distributions of the system, respectively. In this paper, a novel adaptive evolution algorithm is developed for finding the optimal combination of left–right eigenvectors of the vibration isolator, which is an improvement over the simultaneous left–right eigenvector assignment (SLREA) method proposed by Wu and Wang (2008 Smart Mater. Struct. 17 015048). The isolation performance index used in the proposed algorithm is defined by combining the orthogonality index of left eigenvectors and the modal energy ratio index of right eigenvectors. Through the proposed ALREE algorithm, both the left and right eigenvectors evolve such that the isolation performance index decreases, and therefore one can find the optimal combination of left–right eigenvectors of the closed-loop system for vibration isolation purposes. The optimal combination of left–right eigenvectors is then synthesized to determine the feedback gain matrix of the closed-loop system. The result of the active isolation control shows that the proposed method can be utilized to improve the vibration isolation performance compared with the previous approaches

Optimal Location and Sizing of UPQC in Distribution Networks Using Differential Evolution Algorithm

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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.

Dynamics of the evolution of learning algorithms by selection

International Nuclear Information System (INIS)

Neirotti, Juan Pablo; Caticha, Nestor

2003-01-01

We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate populations of programs that implement algorithms used by neural network classifiers to learn a rule in a supervised learning scenario. In contrast to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process. Phenotypic and genotypic entropies, which describe the distribution of fitness and of symbols, respectively, are used to monitor the dynamics. We identify significant functional structures responsible for the improvements in the learning process. In particular, some combinations of variables and operators are useful in assessing performance in rule extraction and can thus implement annealing of the learning schedule. We also find combinations that can signal surprise, measured on a single example, by the difference between predicted and correct classification. When such favorable structures appear, they are disseminated on very short time scales throughout the population. Due to such abruptness they can be thought of as dynamical transitions. But foremost, we find a strict temporal order of such discoveries. Structures that measure performance are never useful before those for measuring surprise. Invasions of the population by such structures in the reverse order were never observed. Asymptotically, the generalization ability approaches Bayesian results

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

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Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

A Novel Discrete Differential Evolution Algorithm for the Vehicle Routing Problem in B2C E-Commerce

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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.

Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence

Directory of Open Access Journals (Sweden)

Man Ding

2010-01-01

Full Text Available In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs. The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs and Swarm Intelligence (SI. In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA and Difference Evolution (DE, and one SI algorithm, namely, Particle Swarm Optimization (PSO, on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.

A Sustainable City Planning Algorithm Based on TLBO and Local Search

Science.gov (United States)

Zhang, Ke; Lin, Li; Huang, Xuanxuan; Liu, Yiming; Zhang, Yonggang

2017-09-01

Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the “teaching” and “learning” behavior of teaching class in the life. The evolution of the population is realized by simulating the “teaching” of the teacher and the student “learning” from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO_LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.

A SLAM based on auxiliary marginalised particle filter and differential evolution

Science.gov (United States)

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 Convergent Differential Evolution Algorithm with Hidden Adaptation Selection for Engineering Optimization

Directory of Open Access Journals (Sweden)

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.

A novel method to design S-box based on chaotic map and genetic algorithm

International Nuclear Information System (INIS)

Wang, Yong; Wong, Kwok-Wo; Li, Changbing; Li, Yang

2012-01-01

The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.

A novel method to design S-box based on chaotic map and genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Wang, Yong, E-mail: wangyong_cqupt@163.com [State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing 400044 (China); Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Wong, Kwok-Wo [Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong (Hong Kong); Li, Changbing [Key Laboratory of Electronic Commerce and Logistics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China); Li, Yang [Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapping Street, S1 3DJ (United Kingdom)

2012-01-30

The substitution box (S-box) is an important component in block encryption algorithms. In this Letter, the problem of constructing S-box is transformed to a Traveling Salesman Problem and a method for designing S-box based on chaos and genetic algorithm is proposed. Since the proposed method makes full use of the traits of chaotic map and evolution process, stronger S-box is obtained. The results of performance test show that the presented S-box has good cryptographic properties, which justify that the proposed algorithm is effective in generating strong S-boxes. -- Highlights: ► The problem of constructing S-box is transformed to a Traveling Salesman Problem. ► We present a new method for designing S-box based on chaos and genetic algorithm. ► The proposed algorithm is effective in generating strong S-boxes.

Assessing the performance of a differential evolution algorithm in structural damage detection by varying the objective function

OpenAIRE

Villalba-Morales, Jesús Daniel; Laier, José Elias

2014-01-01

Structural damage detection has become an important research topic in certain segments of the engineering community. These methodologies occasionally formulate an optimization problem by defining an objective function based on dynamic parameters, with metaheuristics used to find the solution. In this study, damage localization and quantification is performed by an Adaptive Differential Evolution algorithm, which solves the associated optimization problem. Furthermore, this paper looks at the ...

Historical feature pattern extraction based network attack situation sensing algorithm.

Science.gov (United States)

Zeng, Yong; Liu, Dacheng; Lei, Zhou

2014-01-01

The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE). First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Historical Feature Pattern Extraction Based Network Attack Situation Sensing Algorithm

Directory of Open Access Journals (Sweden)

Yong Zeng

2014-01-01

Full Text Available The situation sequence contains a series of complicated and multivariate random trends, which are very sudden, uncertain, and difficult to recognize and describe its principle by traditional algorithms. To solve the above questions, estimating parameters of super long situation sequence is essential, but very difficult, so this paper proposes a situation prediction method based on historical feature pattern extraction (HFPE. First, HFPE algorithm seeks similar indications from the history situation sequence recorded and weighs the link intensity between occurred indication and subsequent effect. Then it calculates the probability that a certain effect reappears according to the current indication and makes a prediction after weighting. Meanwhile, HFPE method gives an evolution algorithm to derive the prediction deviation from the views of pattern and accuracy. This algorithm can continuously promote the adaptability of HFPE through gradual fine-tuning. The method preserves the rules in sequence at its best, does not need data preprocessing, and can track and adapt to the variation of situation sequence continuously.

Differential evolution and simulated annealing algorithms for mechanical systems design

Directory of Open Access Journals (Sweden)

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.

Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm

Directory of Open Access Journals (Sweden)

Shuyu Dai

2018-04-01

Full Text Available In recent years, the construction of China’s power grid has experienced rapid development, and its scale has leaped into the first place in the world. Accurate and effective prediction of power grid investment can not only help pool funds and rationally arrange investment in power grid construction, but also reduce capital costs and economic risks, which plays a crucial role in promoting power grid investment planning and construction process. In order to forecast the power grid investment of China accurately, firstly on the basis of analyzing the influencing factors of power grid investment, the influencing factors system for China’s power grid investment forecasting is constructed in this article. The method of grey relational analysis is used for screening the main influencing factors as the prediction model input. Then, a novel power grid investment prediction model based on DE-GWO-SVM (support vector machine optimized by differential evolution and grey wolf optimization algorithm is proposed. Next, two cases are taken for empirical analysis to prove that the DE-GWO-SVM model has strong generalization capacity and has achieved a good prediction effect for power grid investment forecasting in China. Finally, the DE-GWO-SVM model is adopted to forecast power grid investment in China from 2018 to 2022.

A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm

Science.gov (United States)

Thirer, Nonel

2013-05-01

With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.

Adaptive algorithm for mobile user positioning based on environment estimation

Directory of Open Access Journals (Sweden)

Grujović Darko

2014-01-01

Full Text Available This paper analyzes the challenges to realize an infrastructure independent and a low-cost positioning method in cellular networks based on RSS (Received Signal Strength parameter, auxiliary timing parameter and environment estimation. The proposed algorithm has been evaluated using field measurements collected from GSM (Global System for Mobile Communications network, but it is technology independent and can be applied in UMTS (Universal Mobile Telecommunication Systems and LTE (Long-Term Evolution networks, also.

Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm

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Baljit Singh Khehra

2015-03-01

Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.

An Efficient ABC_DE_Based Hybrid Algorithm for Protein–Ligand Docking

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Boxin Guan

2018-04-01

Full Text Available Protein–ligand docking is a process of searching for the optimal binding conformation between the receptor and the ligand. Automated docking plays an important role in drug design, and an efficient search algorithm is needed to tackle the docking problem. To tackle the protein–ligand docking problem more efficiently, An ABC_DE_based hybrid algorithm (ADHDOCK, integrating artificial bee colony (ABC algorithm and differential evolution (DE algorithm, is proposed in the article. ADHDOCK applies an adaptive population partition (APP mechanism to reasonably allocate the computational resources of the population in each iteration process, which helps the novel method make better use of the advantages of ABC and DE. The experiment tested fifty protein–ligand docking problems to compare the performance of ADHDOCK, ABC, DE, Lamarckian genetic algorithm (LGA, running history information guided genetic algorithm (HIGA, and swarm optimization for highly flexible protein–ligand docking (SODOCK. The results clearly exhibit the capability of ADHDOCK toward finding the lowest energy and the smallest root-mean-square deviation (RMSD on most of the protein–ligand docking problems with respect to the other five algorithms.

Time-advance algorithms based on Hamilton's principle

International Nuclear Information System (INIS)

Lewis, H.R.; Kostelec, P.J.

1993-01-01

Time-advance algorithms based on Hamilton's variational principle are being developed for application to problems in plasma physics and other areas. Hamilton's principle was applied previously to derive a system of ordinary differential equations in time whose solution provides an approximation to the evolution of a plasma described by the Vlasov-Maxwell equations. However, the variational principle was not used to obtain an algorithm for solving the ordinary differential equations numerically. The present research addresses the numerical solution of systems of ordinary differential equations via Hamilton's principle. The basic idea is first to choose a class of functions for approximating the solution of the ordinary differential equations over a specific time interval. Then the parameters in the approximating function are determined by applying Hamilton's principle exactly within the class of approximating functions. For example, if an approximate solution is desired between time t and time t + Δ t, the class of approximating functions could be polynomials in time up to some degree. The issue of how to choose time-advance algorithms is very important for achieving efficient, physically meaningful computer simulations. The objective is to reliably simulate those characteristics of an evolving system that are scientifically most relevant. Preliminary numerical results are presented, including comparisons with other computational methods

Macro-micro interlocked simulation algorithm: an exemplification for aurora arc evolution

Energy Technology Data Exchange (ETDEWEB)

Sato, Tetsuya [University of Hyogo, Kobe 650-0044 (Japan); Hasegawa, Hiroki; Ohno, Nobuaki [Japan Agency for Marine-Earth Science and Technology, Yokohama 236-0001 (Japan)], E-mail: sato@hq.u-hyogo.ac.jp

2009-01-01

Using an innovative holistic simulation algorithm that can self-consistently treat a system that evolves as cooperation between macroscopic and microscopic processes, the evolution of a colorful aurora arc is beautifully reproduced as the result of cooperation between the global field-aligned feedback instability of the coupled magnetosphere-ionosphere system and the ensuing microscopic ion-acoustic instability that generates electric double layers and accelerates aurora electrons. These results are in agreement with rocket and satellite observations. This shows that the proposed holistic algorithm could be a reliable tool to reveal complex real dramatic events and become, in the near future, a viable scientifically secure prediction tool for natural disasters such as earthquakes, landslides and floods caused by typhoons.

Improved Differential Evolution Algorithm for Wireless Sensor Network Coverage Optimization

Directory of Open Access Journals (Sweden)

Xing Xu

2014-04-01

Full Text Available In order to serve for the ecological monitoring efficiency of Poyang Lake, an improved hybrid algorithm, mixed with differential evolution and particle swarm optimization, is proposed and applied to optimize the coverage problem of wireless sensor network. And then, the affect of the population size and the number of iterations on the coverage performance are both discussed and analyzed. The four kinds of statistical results about the coverage rate are obtained through lots of simulation experiments.

A Markov chain Monte Carlo Expectation Maximization Algorithm for Statistical Analysis of DNA Sequence Evolution with Neighbor-Dependent Substitution Rates

DEFF Research Database (Denmark)

Hobolth, Asger

2008-01-01

-dimensional integrals required in the EM algorithm are estimated using MCMC sampling. The MCMC sampler requires simulation of sample paths from a continuous time Markov process, conditional on the beginning and ending states and the paths of the neighboring sites. An exact path sampling algorithm is developed......The evolution of DNA sequences can be described by discrete state continuous time Markov processes on a phylogenetic tree. We consider neighbor-dependent evolutionary models where the instantaneous rate of substitution at a site depends on the states of the neighboring sites. Neighbor......-dependent substitution models are analytically intractable and must be analyzed using either approximate or simulation-based methods. We describe statistical inference of neighbor-dependent models using a Markov chain Monte Carlo expectation maximization (MCMC-EM) algorithm. In the MCMC-EM algorithm, the high...

Hybrid Firefly Variants Algorithm for Localization Optimization in WSN

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P. SrideviPonmalar

2017-01-01

Full Text Available Localization is one of the key issues in wireless sensor networks. Several algorithms and techniques have been introduced for localization. Localization is a procedural technique of estimating the sensor node location. In this paper, a novel three hybrid algorithms based on firefly is proposed for localization problem. Hybrid Genetic Algorithm-Firefly Localization Algorithm (GA-FFLA, Hybrid Differential Evolution-Firefly Localization Algorithm (DE-FFLA and Hybrid Particle Swarm Optimization -Firefly Localization Algorithm (PSO-FFLA are analyzed, designed and implemented to optimize the localization error. The localization algorithms are compared based on accuracy of estimation of location, time complexity and iterations required to achieve the accuracy. All the algorithms have hundred percent estimation accuracy but with variations in the number of firefliesr requirements, variation in time complexity and number of iteration requirements. Keywords: Localization; Genetic Algorithm; Differential Evolution; Particle Swarm Optimization

Culture belief based multi-objective hybrid differential evolutionary algorithm in short term hydrothermal scheduling

International Nuclear Information System (INIS)

Zhang Huifeng; Zhou Jianzhong; Zhang Yongchuan; Lu Youlin; Wang Yongqiang

2013-01-01

Highlights: ► Culture belief is integrated into multi-objective differential evolution. ► Chaotic sequence is imported to improve evolutionary population diversity. ► The priority of convergence rate is proved in solving hydrothermal problem. ► The results show the quality and potential of proposed algorithm. - Abstract: A culture belief based multi-objective hybrid differential evolution (CB-MOHDE) is presented to solve short term hydrothermal optimal scheduling with economic emission (SHOSEE) problem. This problem is formulated for compromising thermal cost and emission issue while considering its complicated non-linear constraints with non-smooth and non-convex characteristics. The proposed algorithm integrates a modified multi-objective differential evolutionary algorithm into the computation model of culture algorithm (CA) as well as some communication protocols between population space and belief space, three knowledge structures in belief space are redefined according to these problem-solving characteristics, and in the differential evolution a chaotic factor is embedded into mutation operator for avoiding the premature convergence by enlarging the search scale when the search trajectory reaches local optima. Furthermore, a new heuristic constraint-handling technique is utilized to handle those complex equality and inequality constraints of SHOSEE problem. After the application on hydrothermal scheduling system, the efficiency and stability of the proposed CB-MOHDE is verified by its more desirable results in comparison to other method established recently, and the simulation results also reveal that CB-MOHDE can be a promising alternative for solving SHOSEE.

Genetic-evolution-based optimization methods for engineering design

Science.gov (United States)

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.

Model parameter estimations from residual gravity anomalies due to simple-shaped sources using Differential Evolution Algorithm

Science.gov (United States)

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

Battery parameterisation based on differential evolution via a boundary evolution strategy

DEFF Research Database (Denmark)

Yang, Guangya

2013-01-01

the advances of evolutionary algorithms (EAs). Differential evolution (DE) is selected and modified to parameterise an equivalent circuit model of lithium-ion batteries. A boundary evolution strategy (BES) is developed and incorporated into the DE to update the parameter boundaries during the parameterisation......, 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...

Optimal Design for PID Controller Based on DE Algorithm in Omnidirectional Mobile Robot

Directory of Open Access Journals (Sweden)

Wu Peizhang

2017-01-01

Full Text Available This paper introduces a omnidirectional mobile robot based on Mecanum wheel, which is used for conveying heavy load in a small space of the automatic warehousing logistics center. Then analyzes and establishes the omnidirectional chassis inverse and forward kinematic model. In order to improve the performance of motion, the paper proposes the optimal PID controller based on differential evolution algorithm. Finally, through MATLAB simulation, the results show that the kinematic model of mobile robot chassis is correct, further more the controller optimized by the DE algorithm working better than the traditional Z-N PID tuned. So the optimal scheme is reasonable and feasible, which has a value for engineering applications.

A Novel Hybrid Firefly Algorithm for Global Optimization.

Directory of Open Access Journals (Sweden)

Lina Zhang

Full Text Available Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA, is proposed by combining the advantages of both the firefly algorithm (FA and differential evolution (DE. FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA, differential evolution (DE and particle swarm optimization (PSO in the sense of avoiding local minima and increasing the convergence rate.

Opposition-Based Adaptive Fireworks Algorithm

OpenAIRE

Chibing Gong

2016-01-01

A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based a...

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

Institute of Scientific and Technical Information of China (English)

Lili Tao; Bin Xu; Zhihua Hu; Weimin Zhong

2017-01-01

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta-neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob-lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

Dynamic route guidance algorithm based algorithm based on artificial immune system

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.

Differential evolution enhanced with multiobjective sorting-based mutation operators.

Science.gov (United States)

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.

Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

Directory of Open Access Journals (Sweden)

Wei Li

2015-01-01

Full Text Available We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

Institute of Scientific and Technical Information of China (English)

Haidong Xu; Mingyan Jiang; Kun Xu

2015-01-01

The artificial bee colony (ABC) algorithm is a com-petitive stochastic population-based optimization algorithm. How-ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in-sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA cal ed Archimedean copula estima-tion of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench-mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen-tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.

Majorization arrow in quantum-algorithm design

International Nuclear Information System (INIS)

Latorre, J.I.; Martin-Delgado, M.A.

2002-01-01

We apply majorization theory to study the quantum algorithms known so far and find that there is a majorization principle underlying the way they operate. Grover's algorithm is a neat instance of this principle where majorization works step by step until the optimal target state is found. Extensions of this situation are also found in algorithms based in quantum adiabatic evolution and the family of quantum phase-estimation algorithms, including Shor's algorithm. We state that in quantum algorithms the time arrow is a majorization arrow

pSum-SaDE: A Modified p-Median Problem and Self-Adaptive Differential Evolution Algorithm for Text Summarization

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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.

Genetic algorithm based on qubits and quantum gates

International Nuclear Information System (INIS)

Silva, Joao Batista Rosa; Ramos, Rubens Viana

2003-01-01

Full text: Genetic algorithm, a computational technique based on the evolution of the species, in which a possible solution of the problem is coded in a binary string, called chromosome, has been used successfully in several kinds of problems, where the search of a minimal or a maximal value is necessary, even when local minima are present. A natural generalization of a binary string is a qubit string. Hence, it is possible to use the structure of a genetic algorithm having a sequence of qubits as a chromosome and using quantum operations in the reproduction in order to find the best solution in some problems of quantum information. For example, given a unitary matrix U what is the pair of qubits that, when applied at the input, provides the output state with maximal entanglement? In order to solve this problem, a population of chromosomes of two qubits was created. The crossover was performed applying the quantum gates CNOT and SWAP at the pair of qubits, while the mutation was performed applying the quantum gates Hadamard, Z and Not in a single qubit. The result was compared with a classical genetic algorithm used to solve the same problem. A hundred simulations using the same U matrix was performed. Both algorithms, hereafter named by CGA (classical) and QGA (using qu bits), reached good results close to 1 however, the number of generations needed to find the best result was lower for the QGA. Another problem where the QGA can be useful is in the calculation of the relative entropy of entanglement. We have tested our algorithm using 100 pure states chosen randomly. The stop criterion used was the error lower than 0.01. The main advantages of QGA are its good precision, robustness and very easy implementation. The main disadvantage is its low velocity, as happen for all kind of genetic algorithms. (author)

An Improved Routing Optimization Algorithm Based on Travelling Salesman Problem for Social Networks

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Naixue Xiong

2017-06-01

Full Text Available A social network is a social structure, which is organized by the relationships or interactions between individuals or groups. Humans link the physical network with social network, and the services in the social world are based on data and analysis, which directly influence decision making in the physical network. In this paper, we focus on a routing optimization algorithm, which solves a well-known and popular problem. Ant colony algorithm is proposed to solve this problem effectively, but random selection strategy of the traditional algorithm causes evolution speed to be slow. Meanwhile, positive feedback and distributed computing model make the algorithm quickly converge. Therefore, how to improve convergence speed and search ability of algorithm is the focus of the current research. The paper proposes the improved scheme. Considering the difficulty about searching for next better city, new parameters are introduced to improve probability of selection, and delay convergence speed of algorithm. To avoid the shortest path being submerged, and improve sensitive speed of finding the shortest path, it updates pheromone regulation formula. The results show that the improved algorithm can effectively improve convergence speed and search ability for achieving higher accuracy and optimal results.

Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

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Longquan Yong

2012-01-01

Full Text Available Harmony search (HS method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator (IHSDE is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and variance of IHSDE from theoretical viewpoint. Numerical results, compared with the HSDE, NGHS, show that the IHSDE method has good convergence property over a test-suite of well-known benchmark functions.

Multilevel Image Segmentation Based on an Improved Firefly Algorithm

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Kai Chen

2016-01-01

Full Text Available Multilevel image segmentation is time-consuming and involves large computation. The firefly algorithm has been applied to enhancing the efficiency of multilevel image segmentation. However, in some cases, firefly algorithm is easily trapped into local optima. In this paper, an improved firefly algorithm (IFA is proposed to search multilevel thresholds. In IFA, in order to help fireflies escape from local optima and accelerate the convergence, two strategies (i.e., diversity enhancing strategy with Cauchy mutation and neighborhood strategy are proposed and adaptively chosen according to different stagnation stations. The proposed IFA is compared with three benchmark optimal algorithms, that is, Darwinian particle swarm optimization, hybrid differential evolution optimization, and firefly algorithm. The experimental results show that the proposed method can efficiently segment multilevel images and obtain better performance than the other three methods.

Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms

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Fernando A. Auat Cheein

2013-01-01

Full Text Available Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter, the EKF (Extended Kalman Filter and the UKF (Unscented Kalman Filter. Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.

Algorithm for predicting the evolution of series of dynamics of complex systems in solving information problems

Science.gov (United States)

Kasatkina, T. I.; Dushkin, A. V.; Pavlov, V. A.; Shatovkin, R. R.

2018-03-01

In the development of information, systems and programming to predict the series of dynamics, neural network methods have recently been applied. They are more flexible, in comparison with existing analogues and are capable of taking into account the nonlinearities of the series. In this paper, we propose a modified algorithm for predicting the series of dynamics, which includes a method for training neural networks, an approach to describing and presenting input data, based on the prediction by the multilayer perceptron method. To construct a neural network, the values of a series of dynamics at the extremum points and time values corresponding to them, formed based on the sliding window method, are used as input data. The proposed algorithm can act as an independent approach to predicting the series of dynamics, and be one of the parts of the forecasting system. The efficiency of predicting the evolution of the dynamics series for a short-term one-step and long-term multi-step forecast by the classical multilayer perceptron method and a modified algorithm using synthetic and real data is compared. The result of this modification was the minimization of the magnitude of the iterative error that arises from the previously predicted inputs to the inputs to the neural network, as well as the increase in the accuracy of the iterative prediction of the neural network.

Energetic and exergetic efficiency modeling of a cargo aircraft by a topology improving neuro-evolution algorithm

International Nuclear Information System (INIS)

Baklacioglu, Tolga; Aydin, Hakan; Turan, Onder

2016-01-01

An aircraft is a complex system that requires methodologies for an efficient thermodynamic design process. So, it is important to gain a deeper understanding of energy and exergy use throughout an aircraft. The aim of this study is to propose a topology improving NE (neuro-evolution) algorithm modeling for assessing energy and exergy efficiency of a cargo aircraft for the phases of a flight. In this regard, energy and exergy data of the aircraft achieved from several engine runs at different power settings have been utilized to derive the ANN (artificial neural network) models optimized by a GA (genetic algorithm). NE of feed-forward networks trained by a BP (backpropagation) algorithm with momentum has assured the accomplishment of optimum initial network weights as well as the improvement of the network topology. The linear correlation coefficients very close to unity obtained for the derived ANN models have proved the tight fitting of the real data and the estimated values of the efficiencies provided by the models. Finally, compared to the trial-and-error case, evolving the networks by GAs has enhanced the accuracy of the modeling simply further as the reduction in the MSE (mean squared errors) for the energy and exergy efficiencies indicates. - Highlights: • Optimization using neuro-evolution algorithm. • Improved backpropagation algorithm using momentum factor. • Turboprop parameters as independent variables. • Energy and exergy efficiency modeling of a cargo aircraft.

Design of 2-D Recursive Filters Using Self-adaptive Mutation Differential Evolution Algorithm

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Lianghong Wu

2011-08-01

Full Text Available This paper investigates a novel approach to the design of two-dimensional recursive digital filters using differential evolution (DE algorithm. The design task is reformulated as a constrained minimization problem and is solved by an Self-adaptive Mutation DE algorithm (SAMDE, which adopts an adaptive mutation operator that combines with the advantages of the DE/rand/1/bin strategy and the DE/best/2/bin strategy. As a result, its convergence performance is improved greatly. Numerical experiment results confirm the conclusion. The proposedSAMDE approach is effectively applied to test a numerical example and is compared with previous design methods. The computational experiments show that the SAMDE approach can obtain better results than previous design methods.

Normalization based K means Clustering Algorithm

OpenAIRE

Virmani, Deepali; Taneja, Shweta; Malhotra, Geetika

2015-01-01

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights. Experimental results prove the betterment of proposed N-K means clustering algorithm over existing...

Solving Partial Differential Equations Using a New Differential Evolution Algorithm

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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.

Trans gene regulation in adaptive evolution: a genetic algorithm model.

Science.gov (United States)

Behera, N; Nanjundiah, V

1997-09-21

This is a continuation of earlier studies on the evolution of infinite populations of haploid genotypes within a genetic algorithm framework. We had previously explored the evolutionary consequences of the existence of indeterminate-"plastic"-loci, where a plastic locus had a finite probability in each generation of functioning (being switched "on") or not functioning (being switched "off"). The relative probabilities of the two outcomes were assigned on a stochastic basis. The present paper examines what happens when the transition probabilities are biased by the presence of regulatory genes. We find that under certain conditions regulatory genes can improve the adaptation of the population and speed up the rate of evolution (on occasion at the cost of lowering the degree of adaptation). Also, the existence of regulatory loci potentiates selection in favour of plasticity. There is a synergistic effect of regulatory genes on plastic alleles: the frequency of such alleles increases when regulatory loci are present. Thus, phenotypic selection alone can be a potentiating factor in a favour of better adaptation. Copyright 1997 Academic Press Limited.

Differential evolution-simulated annealing for multiple sequence alignment

Science.gov (United States)

Addawe, R. C.; Addawe, J. M.; Sueño, M. R. K.; Magadia, J. C.

2017-10-01

Multiple sequence alignments (MSA) are used in the analysis of molecular evolution and sequence structure relationships. In this paper, a hybrid algorithm, Differential Evolution - Simulated Annealing (DESA) is applied in optimizing multiple sequence alignments (MSAs) based on structural information, non-gaps percentage and totally conserved columns. DESA is a robust algorithm characterized by self-organization, mutation, crossover, and SA-like selection scheme of the strategy parameters. Here, the MSA problem is treated as a multi-objective optimization problem of the hybrid evolutionary algorithm, DESA. Thus, we name the algorithm as DESA-MSA. Simulated sequences and alignments were generated to evaluate the accuracy and efficiency of DESA-MSA using different indel sizes, sequence lengths, deletion rates and insertion rates. The proposed hybrid algorithm obtained acceptable solutions particularly for the MSA problem evaluated based on the three objectives.

Duality quantum algorithm efficiently simulates open quantum systems

Science.gov (United States)

Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu

2016-01-01

Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d3) in contrast to O(d4) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm. PMID:27464855

Machine learning for evolution strategies

CERN Document Server

Kramer, Oliver

2016-01-01

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

Wavelet-LMS algorithm-based echo cancellers

Science.gov (United States)

Seetharaman, Lalith K.; Rao, Sathyanarayana S.

2002-12-01

This paper presents Echo Cancellers based on the Wavelet-LMS Algorithm. The performance of the Least Mean Square Algorithm in Wavelet transform domain is observed and its application in Echo cancellation is analyzed. The Widrow-Hoff Least Mean Square Algorithm is most widely used algorithm for Adaptive filters that function as Echo Cancellers. The present day communication signals are widely non-stationary in nature and some errors crop up when Least Mean Square Algorithm is used for the Echo Cancellers handling such signals. The analysis of non-stationary signals often involves a compromise between how well transitions or discontinuities can be located. The multi-scale or multi-resolution of signal analysis, which is the essence of wavelet transform, makes Wavelets popular in non-stationary signal analysis. In this paper, we present a Wavelet-LMS algorithm wherein the wavelet coefficients of a signal are modified adaptively using the Least Mean Square Algorithm and then reconstructed to give an Echo-free signal. The Echo Canceller based on this Algorithm is found to have a better convergence and a comparatively lesser MSE (Mean Square error).

Using 2-Opt based evolution strategy for travelling salesman problem

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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.

A Differential Evolution Based MPPT Method for Photovoltaic Modules under Partial Shading Conditions

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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.

Introduction to Evolutionary Algorithms

CERN Document Server

Yu, Xinjie

2010-01-01

Evolutionary algorithms (EAs) are becoming increasingly attractive for researchers from various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science, economics, etc. This book presents an insightful, comprehensive, and up-to-date treatment of EAs, such as genetic algorithms, differential evolution, evolution strategy, constraint optimization, multimodal optimization, multiobjective optimization, combinatorial optimization, evolvable hardware, estimation of distribution algorithms, ant colony optimization, particle swarm opti

Optimum Parameters for Tuned Mass Damper Using Shuffled Complex Evolution (SCE Algorithm

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Hessamoddin Meshkat Razavi

2015-06-01

Full Text Available This study is investigated the optimum parameters for a tuned mass damper (TMD under the seismic excitation. Shuffled complex evolution (SCE is a meta-heuristic optimization method which is used to find the optimum damping and tuning frequency ratio for a TMD. The efficiency of the TMD is evaluated by decreasing the structural displacement dynamic magnification factor (DDMF and acceleration dynamic magnification factor (ADMF for a specific vibration mode of the structure. The optimum TMD parameters and the corresponding optimized DDMF and ADMF are achieved for two control levels (displacement control and acceleration control, different structural damping ratio and mass ratio of the TMD system. The optimum TMD parameters are checked for a 10-storey building under earthquake excitations. The maximum storey displacement and acceleration obtained by SCE method are compared with the results of other existing approaches. The results show that the peak building response decreased with decreases of about 20% for displacement and 30% for acceleration of the top floor. To show the efficiency of the adopted algorithm (SCE, a comparison is also made between SCE and other meta-heuristic optimization methods such as genetic algorithm (GA, particle swarm optimization (PSO method and harmony search (HS algorithm in terms of success rate and computational processing time. The results show that the proposed algorithm outperforms other meta-heuristic optimization methods.

An x-space analysis of evolution equations: Soffer's inequality and the non-forward evolution

International Nuclear Information System (INIS)

Cafarella, Alessandro; Coriano, Claudio; Guzzi, Marco

2003-01-01

We analyze the use of algorithms based in x-space for the solution of renormalization group equations of DGLAP-type and test their consistency by studying bounds among partons distributions - in our specific case Soffer's inequality and the perturbative behaviour of the nucleon tensor charge - to next-to-leading order in QCD. A discussion of the perturbative resummation implicit in these expansions using Mellin moments is included. We also comment on the (kinetic) proof of positivity of the evolution of h1, using a kinetic analogy and illustrate the extension of the algorithm to the evolution of generalized parton distributions. We prove positivity of the non-forward evolution in a special case and illustrate a Fokker-Planck approximation to it. (author)

A Memetic Differential Evolution Algorithm Based on Dynamic Preference for Constrained Optimization Problems

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Ning Dong

2014-01-01

functions are executed, and comparisons with five state-of-the-art algorithms are made. The results illustrate that the proposed algorithm is competitive with and in some cases superior to the compared ones in terms of the quality, efficiency, and the robustness of the obtained results.

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

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Kerim Guney

2015-01-01

Full Text Available An evolutionary method based on backtracking search optimization algorithm (BSA is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO, genetic algorithm (GA, modified touring ant colony algorithm (MTACO, quadratic programming method (QPM, bacterial foraging algorithm (BFA, bees algorithm (BA, clonal selection algorithm (CLONALG, plant growth simulation algorithm (PGSA, tabu search algorithm (TSA, memetic algorithm (MA, nondominated sorting GA-2 (NSGA-2, multiobjective differential evolution (MODE, decomposition with differential evolution (MOEA/D-DE, comprehensive learning PSO (CLPSO, harmony search algorithm (HSA, seeker optimization algorithm (SOA, and mean variance mapping optimization (MVMO. The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.

Amplitude inversion of the 2D analytic signal of magnetic anomalies through the differential evolution algorithm

Science.gov (United States)

Ekinci, Yunus Levent; Özyalın, Şenol; Sındırgı, Petek; Balkaya, Çağlayan; Göktürkler, Gökhan

2017-12-01

In this work, analytic signal amplitude (ASA) inversion of total field magnetic anomalies has been achieved by differential evolution (DE) which is a population-based evolutionary metaheuristic algorithm. Using an elitist strategy, the applicability and effectiveness of the proposed inversion algorithm have been evaluated through the anomalies due to both hypothetical model bodies and real isolated geological structures. Some parameter tuning studies relying mainly on choosing the optimum control parameters of the algorithm have also been performed to enhance the performance of the proposed metaheuristic. Since ASAs of magnetic anomalies are independent of both ambient field direction and the direction of magnetization of the causative sources in a two-dimensional (2D) case, inversions of synthetic noise-free and noisy single model anomalies have produced satisfactory solutions showing the practical applicability of the algorithm. Moreover, hypothetical studies using multiple model bodies have clearly showed that the DE algorithm is able to cope with complicated anomalies and some interferences from neighbouring sources. The proposed algorithm has then been used to invert small- (120 m) and large-scale (40 km) magnetic profile anomalies of an iron deposit (Kesikköprü-Bala, Turkey) and a deep-seated magnetized structure (Sea of Marmara, Turkey), respectively to determine depths, geometries and exact origins of the source bodies. Inversion studies have yielded geologically reasonable solutions which are also in good accordance with the results of normalized full gradient and Euler deconvolution techniques. Thus, we propose the use of DE not only for the amplitude inversion of 2D analytical signals of magnetic profile anomalies having induced or remanent magnetization effects but also the low-dimensional data inversions in geophysics. A part of this paper was presented as an abstract at the 2nd International Conference on Civil and Environmental Engineering, 8

Hybrid employment recommendation algorithm based on Spark

Science.gov (United States)

Li, Zuoquan; Lin, Yubei; Zhang, Xingming

2017-08-01

Aiming at the real-time application of collaborative filtering employment recommendation algorithm (CF), a clustering collaborative filtering recommendation algorithm (CCF) is developed, which applies hierarchical clustering to CF and narrows the query range of neighbour items. In addition, to solve the cold-start problem of content-based recommendation algorithm (CB), a content-based algorithm with users’ information (CBUI) is introduced for job recommendation. Furthermore, a hybrid recommendation algorithm (HRA) which combines CCF and CBUI algorithms is proposed, and implemented on Spark platform. The experimental results show that HRA can overcome the problems of cold start and data sparsity, and achieve good recommendation accuracy and scalability for employment recommendation.

Differential Evolution and Particle Swarm Optimization for Partitional Clustering

DEFF Research Database (Denmark)

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......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...

An Enhanced Jaya Algorithm with a Two Group Adaption

Directory of Open Access Journals (Sweden)

Chibing Gong

2017-01-01

Full Text Available This paper proposes a novel performance enhanced Jaya algorithm with a two group adaption (E-Jaya. Two improvements are presented in E-Jaya. First, instead of using the best and the worst values in Jaya algorithm, EJaya separates all candidates into two groups: the better and the worse groups based on their fitness values, then the mean of the better group and the mean of the worse group are used. Second, in order to add non algorithm-specific parameters in E-Jaya, a novel adaptive method of dividing the two groups has been developed. Finally, twelve benchmark functions with different dimensionality, such as 40, 60, and 100, were evaluated using the proposed EJaya algorithm. The results show that E-Jaya significantly outperformed Jaya algorithm in terms of the solution accuracy. Additionally, E-Jaya was also compared with a differential evolution (DE, a self-adapting control parameters in differential evolution (jDE, a firefly algorithm (FA, and a standard particle swarm optimization 2011 (SPSO2011 algorithm. E-Jaya algorithm outperforms all the algorithms.

2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm

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Zhiwei Ye

2018-03-01

Full Text Available Image segmentation is a significant step in image analysis and computer vision. Many entropy based approaches have been presented in this topic; among them, Tsallis entropy is one of the best performing methods. However, 1D Tsallis entropy does not consider make use of the spatial correlation information within the neighborhood results might be ruined by noise. Therefore, 2D Tsallis entropy is proposed to solve the problem, and results are compared with 1D Fisher, 1D maximum entropy, 1D cross entropy, 1D Tsallis entropy, fuzzy entropy, 2D Fisher, 2D maximum entropy and 2D cross entropy. On the other hand, due to the existence of huge computational costs, meta-heuristics algorithms like genetic algorithm (GA, particle swarm optimization (PSO, ant colony optimization algorithm (ACO and differential evolution algorithm (DE are used to accelerate the 2D Tsallis entropy thresholding method. In this paper, considering 2D Tsallis entropy as a constrained optimization problem, the optimal thresholds are acquired by maximizing the objective function using a modified chaotic Bat algorithm (MCBA. The proposed algorithm has been tested on some actual and infrared images. The results are compared with that of PSO, GA, ACO and DE and demonstrate that the proposed method outperforms other approaches involved in the paper, which is a feasible and effective option for image segmentation.

Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

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Guo Zhao

2015-01-01

Full Text Available Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell genetic algorithm, an improved differential evolution cellular (IDECell genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.

Online Evolution for Multi-Action Adversarial Games

OpenAIRE

Justesen, Niels; Mahlmann, Tobias; Togelius, Julian

2016-01-01

We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare i...

A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems

DEFF Research Database (Denmark)

Vesterstrøm, Jacob Svaneborg; Thomsen, Rene

2004-01-01

Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance...... in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally...... outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA....

Robust MST-Based Clustering Algorithm.

Science.gov (United States)

Liu, Qidong; Zhang, Ruisheng; Zhao, Zhili; Wang, Zhenghai; Jiao, Mengyao; Wang, Guangjing

2018-06-01

Minimax similarity stresses the connectedness of points via mediating elements rather than favoring high mutual similarity. The grouping principle yields superior clustering results when mining arbitrarily-shaped clusters in data. However, it is not robust against noises and outliers in the data. There are two main problems with the grouping principle: first, a single object that is far away from all other objects defines a separate cluster, and second, two connected clusters would be regarded as two parts of one cluster. In order to solve such problems, we propose robust minimum spanning tree (MST)-based clustering algorithm in this letter. First, we separate the connected objects by applying a density-based coarsening phase, resulting in a low-rank matrix in which the element denotes the supernode by combining a set of nodes. Then a greedy method is presented to partition those supernodes through working on the low-rank matrix. Instead of removing the longest edges from MST, our algorithm groups the data set based on the minimax similarity. Finally, the assignment of all data points can be achieved through their corresponding supernodes. Experimental results on many synthetic and real-world data sets show that our algorithm consistently outperforms compared clustering algorithms.

Rolling scheduling of electric power system with wind power based on improved NNIA algorithm

Science.gov (United States)

Xu, Q. S.; Luo, C. J.; Yang, D. J.; Fan, Y. H.; Sang, Z. X.; Lei, H.

2017-11-01

This paper puts forth a rolling modification strategy for day-ahead scheduling of electric power system with wind power, which takes the operation cost increment of unit and curtailed wind power of power grid as double modification functions. Additionally, an improved Nondominated Neighbor Immune Algorithm (NNIA) is proposed for solution. The proposed rolling scheduling model has further improved the operation cost of system in the intra-day generation process, enhanced the system’s accommodation capacity of wind power, and modified the key transmission section power flow in a rolling manner to satisfy the security constraint of power grid. The improved NNIA algorithm has defined an antibody preference relation model based on equal incremental rate, regulation deviation constraints and maximum & minimum technical outputs of units. The model can noticeably guide the direction of antibody evolution, and significantly speed up the process of algorithm convergence to final solution, and enhance the local search capability.

Identification of time-varying nonlinear systems using differential evolution algorithm

DEFF Research Database (Denmark)

Perisic, Nevena; Green, Peter L; Worden, Keith

2013-01-01

(DE) algorithm for the identification of time-varying systems. DE is an evolutionary optimisation method developed to perform direct search in a continuous space without requiring any derivative estimation. DE is modified so that the objective function changes with time to account for the continuing......, thus identification of time-varying systems with nonlinearities can be a very challenging task. In order to avoid conventional least squares and gradient identification methods which require uni-modal and double differentiable objective functions, this work proposes a modified differential evolution...... inclusion of new data within an error metric. This paper presents results of identification of a time-varying SDOF system with Coulomb friction using simulated noise-free and noisy data for the case of time-varying friction coefficient, stiffness and damping. The obtained results are promising and the focus...

A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection

Directory of Open Access Journals (Sweden)

Stéphanie Jehan-Besson

2002-06-01

Full Text Available We propose a 3-step algorithm for the automatic detection of moving objects in video sequences using region-based active contours. First, we introduce a very full general framework for region-based active contours with a new Eulerian method to compute the evolution equation of the active contour from a criterion including both region-based and boundary-based terms. This framework can be easily adapted to various applications, thanks to the introduction of functions named descriptors of the different regions. With this new Eulerian method based on shape optimization principles, we can easily take into account the case of descriptors depending upon features globally attached to the regions. Second, we propose a 3-step algorithm for detection of moving objects, with a static or a mobile camera, using region-based active contours. The basic idea is to hierarchically associate temporal and spatial information. The active contour evolves with successively three sets of descriptors: a temporal one, and then two spatial ones. The third spatial descriptor takes advantage of the segmentation of the image in intensity homogeneous regions. User interaction is reduced to the choice of a few parameters at the beginning of the process. Some experimental results are supplied.

Game Algorithm for Resource Allocation Based on Intelligent Gradient in HetNet

Directory of Open Access Journals (Sweden)

Fang Ye

2017-02-01

Full Text Available In order to improve system performance such as throughput, heterogeneous network (HetNet has become an effective solution in Long Term Evolution-Advanced (LET-A. However, co-channel interference leads to degradation of the HetNet throughput, because femtocells are always arranged to share the spectrum with the macro base station. In this paper, in view of the serious cross-layer interference in double layer HetNet, the Stackelberg game model is adopted to analyze the resource allocation methods of the network. Unlike the traditional system models only focusing on macro base station performance improvement, we take into account the overall system performance and build a revenue function with convexity. System utility functions are defined as the average throughput, which does not adopt frequency spectrum trading method, so as to avoid excessive signaling overhead. Due to the value scope of continuous Nash equilibrium of the built game model, the gradient iterative algorithm is introduced to reduce the computational complexity. As for the solution of Nash equilibrium, one kind of gradient iterative algorithm is proposed, which is able to intelligently choose adjustment factors. The Nash equilibrium can be quickly solved; meanwhile, the step of presetting adjustment factors is avoided according to network parameters in traditional linear iterative model. Simulation results show that the proposed algorithm enhances the overall performance of the system.

On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex

Directory of Open Access Journals (Sweden)

Utku Kose

2015-07-01

Full Text Available In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution space via new swarm members. In order have better idea about success of the algorithm; it has been tested via some benchmark functions. At this point, the obtained results show that the algorithm can be an alternative to the literature in terms of single-objective optimizationsolution ways. Vortex Optimization Algorithm (VOA is the name suggestion by the authors; for this new idea of intelligent optimization approach.

An Evolutionary Algorithm to Mine High-Utility Itemsets

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

Full Text Available High-utility itemset mining (HUIM is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM of association rules (ARs. In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs based on the binary particle swarm optimization. A maximal pattern (MP-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

From determinism and probability to chaos: chaotic evolution towards philosophy and methodology of chaotic optimization.

Science.gov (United States)

Pei, Yan

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization

Science.gov (United States)

2015-01-01

We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE) algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE) algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC) algorithm, interactive chaotic evolution (ICE) that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed. PMID:25879067

From Determinism and Probability to Chaos: Chaotic Evolution towards Philosophy and Methodology of Chaotic Optimization

Directory of Open Access Journals (Sweden)

Yan Pei

2015-01-01

Full Text Available We present and discuss philosophy and methodology of chaotic evolution that is theoretically supported by chaos theory. We introduce four chaotic systems, that is, logistic map, tent map, Gaussian map, and Hénon map, in a well-designed chaotic evolution algorithm framework to implement several chaotic evolution (CE algorithms. By comparing our previous proposed CE algorithm with logistic map and two canonical differential evolution (DE algorithms, we analyse and discuss optimization performance of CE algorithm. An investigation on the relationship between optimization capability of CE algorithm and distribution characteristic of chaotic system is conducted and analysed. From evaluation result, we find that distribution of chaotic system is an essential factor to influence optimization performance of CE algorithm. We propose a new interactive EC (IEC algorithm, interactive chaotic evolution (ICE that replaces fitness function with a real human in CE algorithm framework. There is a paired comparison-based mechanism behind CE search scheme in nature. A simulation experimental evaluation is conducted with a pseudo-IEC user to evaluate our proposed ICE algorithm. The evaluation result indicates that ICE algorithm can obtain a significant better performance than or the same performance as interactive DE. Some open topics on CE, ICE, fusion of these optimization techniques, algorithmic notation, and others are presented and discussed.

Immune algorithm based active PID control for structure systems

International Nuclear Information System (INIS)

Lee, Young Jin; Cho, Hyun Cheol; Lee, Kwon Soon

2006-01-01

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I P ID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect

Estimating Traffic Accidents in Turkey Using Differential Evolution Algorithm

Science.gov (United States)

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.

Seizure detection algorithms based on EMG signals

DEFF Research Database (Denmark)

Conradsen, Isa

Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....

Algorithm for Stabilizing a POD-Based Dynamical System

Science.gov (United States)

Kalb, Virginia L.

2010-01-01

This algorithm provides a new way to improve the accuracy and asymptotic behavior of a low-dimensional system based on the proper orthogonal decomposition (POD). Given a data set representing the evolution of a system of partial differential equations (PDEs), such as the Navier-Stokes equations for incompressible flow, one may obtain a low-dimensional model in the form of ordinary differential equations (ODEs) that should model the dynamics of the flow. Temporal sampling of the direct numerical simulation of the PDEs produces a spatial time series. The POD extracts the temporal and spatial eigenfunctions of this data set. Truncated to retain only the most energetic modes followed by Galerkin projection of these modes onto the PDEs obtains a dynamical system of ordinary differential equations for the time-dependent behavior of the flow. In practice, the steps leading to this system of ODEs entail numerically computing first-order derivatives of the mean data field and the eigenfunctions, and the computation of many inner products. This is far from a perfect process, and often results in the lack of long-term stability of the system and incorrect asymptotic behavior of the model. This algorithm describes a new stabilization method that utilizes the temporal eigenfunctions to derive correction terms for the coefficients of the dynamical system to significantly reduce these errors.

Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes

Directory of Open Access Journals (Sweden)

Galperin Michael Y

2003-01-01

Full Text Available Abstract Background Comparative analysis of sequenced genomes reveals numerous instances of apparent horizontal gene transfer (HGT, at least in prokaryotes, and indicates that lineage-specific gene loss might have been even more common in evolution. This complicates the notion of a species tree, which needs to be re-interpreted as a prevailing evolutionary trend, rather than the full depiction of evolution, and makes reconstruction of ancestral genomes a non-trivial task. Results We addressed the problem of constructing parsimonious scenarios for individual sets of orthologous genes given a species tree. The orthologous sets were taken from the database of Clusters of Orthologous Groups of proteins (COGs. We show that the phyletic patterns (patterns of presence-absence in completely sequenced genomes of almost 90% of the COGs are inconsistent with the hypothetical species tree. Algorithms were developed to reconcile the phyletic patterns with the species tree by postulating gene loss, COG emergence and HGT (the latter two classes of events were collectively treated as gene gains. We prove that each of these algorithms produces a parsimonious evolutionary scenario, which can be represented as mapping of loss and gain events on the species tree. The distribution of the evolutionary events among the tree nodes substantially depends on the underlying assumptions of the reconciliation algorithm, e.g. whether or not independent gene gains (gain after loss after gain are permitted. Biological considerations suggest that, on average, gene loss might be a more likely event than gene gain. Therefore different gain penalties were used and the resulting series of reconstructed gene sets for the last universal common ancestor (LUCA of the extant life forms were analysed. The number of genes in the reconstructed LUCA gene sets grows as the gain penalty increases. However, qualitative examination of the LUCA versions reconstructed with different gain penalties

Verification-Based Interval-Passing Algorithm for Compressed Sensing

OpenAIRE

Wu, Xiaofu; Yang, Zhen

2013-01-01

We propose a verification-based Interval-Passing (IP) algorithm for iteratively reconstruction of nonnegative sparse signals using parity check matrices of low-density parity check (LDPC) codes as measurement matrices. The proposed algorithm can be considered as an improved IP algorithm by further incorporation of the mechanism of verification algorithm. It is proved that the proposed algorithm performs always better than either the IP algorithm or the verification algorithm. Simulation resul...

A Trust-region-based Sequential Quadratic Programming Algorithm

DEFF Research Database (Denmark)

Henriksen, Lars Christian; Poulsen, Niels Kjølstad

This technical note documents the trust-region-based sequential quadratic programming algorithm used in other works by the authors. The algorithm seeks to minimize a convex nonlinear cost function subject to linear inequalty constraints and nonlinear equality constraints.......This technical note documents the trust-region-based sequential quadratic programming algorithm used in other works by the authors. The algorithm seeks to minimize a convex nonlinear cost function subject to linear inequalty constraints and nonlinear equality constraints....

Monte Carlo molecular simulations: improving the statistical efficiency of samples with the help of artificial evolution algorithms; Simulations moleculaires de Monte Carlo: amelioration de l'efficacite statistique de l'echantillonnage grace aux algorithmes d'evolution artificielle

Energy Technology Data Exchange (ETDEWEB)

Leblanc, B.

2002-03-01

Molecular simulation aims at simulating particles in interaction, describing a physico-chemical system. When considering Markov Chain Monte Carlo sampling in this context, we often meet the same problem of statistical efficiency as with Molecular Dynamics for the simulation of complex molecules (polymers for example). The search for a correct sampling of the space of possible configurations with respect to the Boltzmann-Gibbs distribution is directly related to the statistical efficiency of such algorithms (i.e. the ability of rapidly providing uncorrelated states covering all the configuration space). We investigated how to improve this efficiency with the help of Artificial Evolution (AE). AE algorithms form a class of stochastic optimization algorithms inspired by Darwinian evolution. Efficiency measures that can be turned into efficiency criteria have been first searched before identifying parameters that could be optimized. Relative frequencies for each type of Monte Carlo moves, usually empirically chosen in reasonable ranges, were first considered. We combined parallel simulations with a 'genetic server' in order to dynamically improve the quality of the sampling during the simulations progress. Our results shows that in comparison with some reference settings, it is possible to improve the quality of samples with respect to the chosen criterion. The same algorithm has been applied to improve the Parallel Tempering technique, in order to optimize in the same time the relative frequencies of Monte Carlo moves and the relative frequencies of swapping between sub-systems simulated at different temperatures. Finally, hints for further research in order to optimize the choice of additional temperatures are given. (author)

Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

Directory of Open Access Journals (Sweden)

Feifei Dong

2014-01-01

Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.

Simple sorting algorithm test based on CUDA

OpenAIRE

Meng, Hongyu; Guo, Fangjin

2015-01-01

With the development of computing technology, CUDA has become a very important tool. In computer programming, sorting algorithm is widely used. There are many simple sorting algorithms such as enumeration sort, bubble sort and merge sort. In this paper, we test some simple sorting algorithm based on CUDA and draw some useful conclusions.

SIFT based algorithm for point feature tracking

Directory of Open Access Journals (Sweden)

Adrian BURLACU

2007-12-01

Full Text Available In this paper a tracking algorithm for SIFT features in image sequences is developed. For each point feature extracted using SIFT algorithm a descriptor is computed using information from its neighborhood. Using an algorithm based on minimizing the distance between two descriptors tracking point features throughout image sequences is engaged. Experimental results, obtained from image sequences that capture scaling of different geometrical type object, reveal the performances of the tracking algorithm.

Real-Coded Quantum-Inspired Genetic Algorithm-Based BP Neural Network Algorithm

Directory of Open Access Journals (Sweden)

Jianyong Liu

2015-01-01

Full Text Available The method that the real-coded quantum-inspired genetic algorithm (RQGA used to optimize the weights and threshold of BP neural network is proposed to overcome the defect that the gradient descent method makes the algorithm easily fall into local optimal value in the learning process. Quantum genetic algorithm (QGA is with good directional global optimization ability, but the conventional QGA is based on binary coding; the speed of calculation is reduced by the coding and decoding processes. So, RQGA is introduced to explore the search space, and the improved varied learning rate is adopted to train the BP neural network. Simulation test shows that the proposed algorithm is effective to rapidly converge to the solution conformed to constraint conditions.

Eigenvalue Decomposition-Based Modified Newton Algorithm

Directory of Open Access Journals (Sweden)

Wen-jun Wang

2013-01-01

Full Text Available When the Hessian matrix is not positive, the Newton direction may not be the descending direction. A new method named eigenvalue decomposition-based modified Newton algorithm is presented, which first takes the eigenvalue decomposition of the Hessian matrix, then replaces the negative eigenvalues with their absolute values, and finally reconstructs the Hessian matrix and modifies the searching direction. The new searching direction is always the descending direction. The convergence of the algorithm is proven and the conclusion on convergence rate is presented qualitatively. Finally, a numerical experiment is given for comparing the convergence domains of the modified algorithm and the classical algorithm.

Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

Science.gov (United States)

Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

2017-09-01

Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing

Complex-based OCT angiography algorithm recovers microvascular information better than amplitude- or phase-based algorithms in phase-stable systems.

Science.gov (United States)

Xu, Jingjiang; Song, Shaozhen; Li, Yuandong; Wang, Ruikang K

2017-12-19

Optical coherence tomography angiography (OCTA) is increasingly becoming a popular inspection tool for biomedical imaging applications. By exploring the amplitude, phase and complex information available in OCT signals, numerous algorithms have been proposed that contrast functional vessel networks within microcirculatory tissue beds. However, it is not clear which algorithm delivers optimal imaging performance. Here, we investigate systematically how amplitude and phase information have an impact on the OCTA imaging performance, to establish the relationship of amplitude and phase stability with OCT signal-to-noise ratio (SNR), time interval and particle dynamics. With either repeated A-scan or repeated B-scan imaging protocols, the amplitude noise increases with the increase of OCT SNR; however, the phase noise does the opposite, i.e. it increases with the decrease of OCT SNR. Coupled with experimental measurements, we utilize a simple Monte Carlo (MC) model to simulate the performance of amplitude-, phase- and complex-based algorithms for OCTA imaging, the results of which suggest that complex-based algorithms deliver the best performance when the phase noise is  algorithm delivers better performance than either the amplitude- or phase-based algorithms for both the repeated A-scan and the B-scan imaging protocols, which agrees well with the conclusion drawn from the MC simulations.

A modified estimation distribution algorithm based on extreme elitism.

Science.gov (United States)

Gao, Shujun; de Silva, Clarence W

2016-12-01

An existing estimation distribution algorithm (EDA) with univariate marginal Gaussian model was improved by designing and incorporating an extreme elitism selection method. This selection method highlighted the effect of a few top best solutions in the evolution and advanced EDA to form a primary evolution direction and obtain a fast convergence rate. Simultaneously, this selection can also keep the population diversity to make EDA avoid premature convergence. Then the modified EDA was tested by means of benchmark low-dimensional and high-dimensional optimization problems to illustrate the gains in using this extreme elitism selection. Besides, no-free-lunch theorem was implemented in the analysis of the effect of this new selection on EDAs. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Micro-droplet based directed evolution outperforms conventional laboratory evolution

DEFF Research Database (Denmark)

Sjostrom, Staffan L.; Huang, Mingtao; Nielsen, Jens

2014-01-01

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 are con...... a whole-genome mutated library of yeast cells for α-amylase activity....

A Hybrid Genetic Algorithm Approach for Optimal Power Flow

Directory of Open Access Journals (Sweden)

Sydulu Maheswarapu

2011-08-01

Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

Multiple Convective Cell Identification and Tracking Algorithm for documenting time-height evolution of measured polarimetric radar and lightning properties

Science.gov (United States)

Rosenfeld, D.; Hu, J.; Zhang, P.; Snyder, J.; Orville, R. E.; Ryzhkov, A.; Zrnic, D.; Williams, E.; Zhang, R.

2017-12-01

A methodology to track the evolution of the hydrometeors and electrification of convective cells is presented and applied to various convective clouds from warm showers to super-cells. The input radar data are obtained from the polarimetric NEXRAD weather radars, The information on cloud electrification is obtained from Lightning Mapping Arrays (LMA). The development time and height of the hydrometeors and electrification requires tracking the evolution and lifecycle of convective cells. A new methodology for Multi-Cell Identification and Tracking (MCIT) is presented in this study. This new algorithm is applied to time series of radar volume scans. A cell is defined as a local maximum in the Vertical Integrated Liquid (VIL), and the echo area is divided between cells using a watershed algorithm. The tracking of the cells between radar volume scans is done by identifying the two cells in consecutive radar scans that have maximum common VIL. The vertical profile of the polarimetric radar properties are used for constructing the time-height cross section of the cell properties around the peak reflectivity as a function of height. The LMA sources that occur within the cell area are integrated as a function of height as well for each time step, as determined by the radar volume scans. The result of the tracking can provide insights to the evolution of storms, hydrometer types, precipitation initiation and cloud electrification under different thermodynamic, aerosol and geographic conditions. The details of the MCIT algorithm, its products and their performance for different types of storm are described in this poster.

Study on Parameter Optimization Design of Drum Brake Based on Hybrid Cellular Multiobjective Genetic Algorithm

Directory of Open Access Journals (Sweden)

Yi Zhang

2012-01-01

Full Text Available In consideration of the significant role the brake plays in ensuring the fast and safe running of vehicles, and since the present parameter optimization design models of brake are far from the practical application, this paper proposes a multiobjective optimization model of drum brake, aiming at maximizing the braking efficiency and minimizing the volume and temperature rise of drum brake. As the commonly used optimization algorithms are of some deficiency, we present a differential evolution cellular multiobjective genetic algorithm (DECell by introducing differential evolution strategy into the canonical cellular genetic algorithm for tackling this problem. For DECell, the gained Pareto front could be as close as possible to the exact Pareto front, and also the diversity of nondominated individuals could be better maintained. The experiments on the test functions reveal that DECell is of good performance in solving high-dimension nonlinear multiobjective problems. And the results of optimizing the new brake model indicate that DECell obviously outperforms the compared popular algorithm NSGA-II concerning the number of obtained brake design parameter sets, the speed, and stability for finding them.

Pilot-based parametric channel estimation algorithm for DCO-OFDM-based visual light communications

Science.gov (United States)

Qian, Xuewen; Deng, Honggui; He, Hailang

2017-10-01

Due to wide modulation bandwidth in optical communication, multipath channels may be non-sparse and deteriorate communication performance heavily. Traditional compressive sensing-based channel estimation algorithm cannot be employed in this kind of situation. In this paper, we propose a practical parametric channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based visual light communication (VLC) systems based on modified zero correlation code (ZCC) pair that has the impulse-like correlation property. Simulation results show that the proposed algorithm achieves better performances than existing least squares (LS)-based algorithm in both bit error ratio (BER) and frequency response estimation.

Structure-Based Algorithms for Microvessel Classification

KAUST Repository

Smith, Amy F.

2015-02-01

© 2014 The Authors. Microcirculation published by John Wiley & Sons Ltd. Objective: Recent developments in high-resolution imaging techniques have enabled digital reconstruction of three-dimensional sections of microvascular networks down to the capillary scale. To better interpret these large data sets, our goal is to distinguish branching trees of arterioles and venules from capillaries. Methods: Two novel algorithms are presented for classifying vessels in microvascular anatomical data sets without requiring flow information. The algorithms are compared with a classification based on observed flow directions (considered the gold standard), and with an existing resistance-based method that relies only on structural data. Results: The first algorithm, developed for networks with one arteriolar and one venular tree, performs well in identifying arterioles and venules and is robust to parameter changes, but incorrectly labels a significant number of capillaries as arterioles or venules. The second algorithm, developed for networks with multiple inlets and outlets, correctly identifies more arterioles and venules, but is more sensitive to parameter changes. Conclusions: The algorithms presented here can be used to classify microvessels in large microvascular data sets lacking flow information. This provides a basis for analyzing the distinct geometrical properties and modelling the functional behavior of arterioles, capillaries, and venules.

Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

Science.gov (United States)

Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

2015-01-01

Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

Selfish Gene Algorithm Vs Genetic Algorithm: A Review

Science.gov (United States)

Ariff, Norharyati Md; Khalid, Noor Elaiza Abdul; Hashim, Rathiah; Noor, Noorhayati Mohamed

2016-11-01

Evolutionary algorithm is one of the algorithms inspired by the nature. Within little more than a decade hundreds of papers have reported successful applications of EAs. In this paper, the Selfish Gene Algorithms (SFGA), as one of the latest evolutionary algorithms (EAs) inspired from the Selfish Gene Theory which is an interpretation of Darwinian Theory ideas from the biologist Richards Dawkins on 1989. In this paper, following a brief introduction to the Selfish Gene Algorithm (SFGA), the chronology of its evolution is presented. It is the purpose of this paper is to present an overview of the concepts of Selfish Gene Algorithm (SFGA) as well as its opportunities and challenges. Accordingly, the history, step involves in the algorithm are discussed and its different applications together with an analysis of these applications are evaluated.

A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces

NARCIS (Netherlands)

Braak, ter C.J.F.

2006-01-01

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

Novel prediction- and subblock-based algorithm for fractal image compression

International Nuclear Information System (INIS)

Chung, K.-L.; Hsu, C.-H.

2006-01-01

Fractal encoding is the most consuming part in fractal image compression. In this paper, a novel two-phase prediction- and subblock-based fractal encoding algorithm is presented. Initially the original gray image is partitioned into a set of variable-size blocks according to the S-tree- and interpolation-based decomposition principle. In the first phase, each current block of variable-size range block tries to find the best matched domain block based on the proposed prediction-based search strategy which utilizes the relevant neighboring variable-size domain blocks. The first phase leads to a significant computation-saving effect. If the domain block found within the predicted search space is unacceptable, in the second phase, a subblock strategy is employed to partition the current variable-size range block into smaller blocks to improve the image quality. Experimental results show that our proposed prediction- and subblock-based fractal encoding algorithm outperforms the conventional full search algorithm and the recently published spatial-correlation-based algorithm by Truong et al. in terms of encoding time and image quality. In addition, the performance comparison among our proposed algorithm and the other two algorithms, the no search-based algorithm and the quadtree-based algorithm, are also investigated

Algebraic dynamics solutions and algebraic dynamics algorithm for nonlinear ordinary differential equations

Institute of Scientific and Technical Information of China (English)

WANG; Shunjin; ZHANG; Hua

2006-01-01

The problem of preserving fidelity in numerical computation of nonlinear ordinary differential equations is studied in terms of preserving local differential structure and approximating global integration structure of the dynamical system.The ordinary differential equations are lifted to the corresponding partial differential equations in the framework of algebraic dynamics,and a new algorithm-algebraic dynamics algorithm is proposed based on the exact analytical solutions of the ordinary differential equations by the algebraic dynamics method.In the new algorithm,the time evolution of the ordinary differential system is described locally by the time translation operator and globally by the time evolution operator.The exact analytical piece-like solution of the ordinary differential equations is expressd in terms of Taylor series with a local convergent radius,and its finite order truncation leads to the new numerical algorithm with a controllable precision better than Runge Kutta Algorithm and Symplectic Geometric Algorithm.

A Generalized National Planning Approach for Admission Capacity in Higher Education: A Nonlinear Integer Goal Programming Model with a Novel Differential Evolution Algorithm.

Science.gov (United States)

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.

Detecting microsatellites within genomes: significant variation among algorithms

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Rivals Eric

2007-04-01

Full Text Available Abstract Background Microsatellites are short, tandemly-repeated DNA sequences which are widely distributed among genomes. Their structure, role and evolution can be analyzed based on exhaustive extraction from sequenced genomes. Several dedicated algorithms have been developed for this purpose. Here, we compared the detection efficiency of five of them (TRF, Mreps, Sputnik, STAR, and RepeatMasker. Results Our analysis was first conducted on the human X chromosome, and microsatellite distributions were characterized by microsatellite number, length, and divergence from a pure motif. The algorithms work with user-defined parameters, and we demonstrate that the parameter values chosen can strongly influence microsatellite distributions. The five algorithms were then compared by fixing parameters settings, and the analysis was extended to three other genomes (Saccharomyces cerevisiae, Neurospora crassa and Drosophila melanogaster spanning a wide range of size and structure. Significant differences for all characteristics of microsatellites were observed among algorithms, but not among genomes, for both perfect and imperfect microsatellites. Striking differences were detected for short microsatellites (below 20 bp, regardless of motif. Conclusion Since the algorithm used strongly influences empirical distributions, studies analyzing microsatellite evolution based on a comparison between empirical and theoretical size distributions should therefore be considered with caution. We also discuss why a typological definition of microsatellites limits our capacity to capture their genomic distributions.

Bioinformatics algorithm based on a parallel implementation of a machine learning approach using transducers

International Nuclear Information System (INIS)

Roche-Lima, Abiel; Thulasiram, Ruppa K

2012-01-01

Finite automata, in which each transition is augmented with an output label in addition to the familiar input label, are considered finite-state transducers. Transducers have been used to analyze some fundamental issues in bioinformatics. Weighted finite-state transducers have been proposed to pairwise alignments of DNA and protein sequences; as well as to develop kernels for computational biology. Machine learning algorithms for conditional transducers have been implemented and used for DNA sequence analysis. Transducer learning algorithms are based on conditional probability computation. It is calculated by using techniques, such as pair-database creation, normalization (with Maximum-Likelihood normalization) and parameters optimization (with Expectation-Maximization - EM). These techniques are intrinsically costly for computation, even worse when are applied to bioinformatics, because the databases sizes are large. In this work, we describe a parallel implementation of an algorithm to learn conditional transducers using these techniques. The algorithm is oriented to bioinformatics applications, such as alignments, phylogenetic trees, and other genome evolution studies. Indeed, several experiences were developed using the parallel and sequential algorithm on Westgrid (specifically, on the Breeze cluster). As results, we obtain that our parallel algorithm is scalable, because execution times are reduced considerably when the data size parameter is increased. Another experience is developed by changing precision parameter. In this case, we obtain smaller execution times using the parallel algorithm. Finally, number of threads used to execute the parallel algorithm on the Breezy cluster is changed. In this last experience, we obtain as result that speedup is considerably increased when more threads are used; however there is a convergence for number of threads equal to or greater than 16.

MVDR Algorithm Based on Estimated Diagonal Loading for Beamforming

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Yuteng Xiao

2017-01-01

Full Text Available Beamforming algorithm is widely used in many signal processing fields. At present, the typical beamforming algorithm is MVDR (Minimum Variance Distortionless Response. However, the performance of MVDR algorithm relies on the accurate covariance matrix. The MVDR algorithm declines dramatically with the inaccurate covariance matrix. To solve the problem, studying the beamforming array signal model and beamforming MVDR algorithm, we improve MVDR algorithm based on estimated diagonal loading for beamforming. MVDR optimization model based on diagonal loading compensation is established and the interval of the diagonal loading compensation value is deduced on the basis of the matrix theory. The optimal diagonal loading value in the interval is also determined through the experimental method. The experimental results show that the algorithm compared with existing algorithms is practical and effective.

Contemporary evolution strategies

CERN Document Server

Bäck, Thomas; Krause, Peter

2013-01-01

Evolution strategies have more than 50 years of history in the field of evolutionary computation. Since the early 1990s, many algorithmic variations of evolution strategies have been developed, characterized by the fact that they use the so-called derandomization concept for strategy parameter adaptation. Most importantly, the covariance matrix adaptation strategy (CMA-ES) and its successors are the key representatives of this group of contemporary evolution strategies. This book provides an overview of the key algorithm developments between 1990 and 2012, including brief descriptions of the a

An investigation of genetic algorithms

International Nuclear Information System (INIS)

Douglas, S.R.

1995-04-01

Genetic algorithms mimic biological evolution by natural selection in their search for better individuals within a changing population. they can be used as efficient optimizers. This report discusses the developing field of genetic algorithms. It gives a simple example of the search process and introduces the concept of schema. It also discusses modifications to the basic genetic algorithm that result in species and niche formation, in machine learning and artificial evolution of computer programs, and in the streamlining of human-computer interaction. (author). 3 refs., 1 tab., 2 figs

An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

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Fuqing Zhao

2016-01-01

Full Text Available A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.

Battery parameterisation based on differential evolution via a boundary evolution strategy

Science.gov (United States)

Yang, Guangya

2014-01-01

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, 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 advances of evolutionary algorithms (EAs). Differential evolution (DE) is selected and modified to parameterise an equivalent circuit model of lithium-ion batteries. A boundary evolution strategy (BES) is developed and incorporated into the DE to update the parameter boundaries during the parameterisation. 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.

Algorithm Research of Individualized Travelling Route Recommendation Based on Similarity

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Xue Shan

2015-01-01

Full Text Available Although commercial recommendation system has made certain achievement in travelling route development, the recommendation system is facing a series of challenges because of people’s increasing interest in travelling. It is obvious that the core content of the recommendation system is recommendation algorithm. The advantages of recommendation algorithm can bring great effect to the recommendation system. Based on this, this paper applies traditional collaborative filtering algorithm for analysis. Besides, illustrating the deficiencies of the algorithm, such as the rating unicity and rating matrix sparsity, this paper proposes an improved algorithm combing the multi-similarity algorithm based on user and the element similarity algorithm based on user, so as to compensate for the deficiencies that traditional algorithm has within a controllable range. Experimental results have shown that the improved algorithm has obvious advantages in comparison with the traditional one. The improved algorithm has obvious effect on remedying the rating matrix sparsity and rating unicity.

Duality based optical flow algorithms with applications

DEFF Research Database (Denmark)

Rakêt, Lars Lau

We consider the popular TV-L1 optical flow formulation, and the so-called duality based algorithm for minimizing the TV-L1 energy. The original formulation is extended to allow for vector valued images, and minimization results are given. In addition we consider different definitions of total...... variation regularization, and related formulations of the optical flow problem that may be used with a duality based algorithm. We present a highly optimized algorithmic setup to estimate optical flows, and give five novel applications. The first application is registration of medical images, where X......-ray images of different hands, taken using different imaging devices are registered using a TV-L1 optical flow algorithm. We propose to regularize the input images, using sparsity enhancing regularization of the image gradient to improve registration results. The second application is registration of 2D...

Optimization in optical systems revisited: Beyond genetic algorithms

Science.gov (United States)

Gagnon, Denis; Dumont, Joey; Dubé, Louis

2013-05-01

Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).

A new hybrid optimization algorithm CRO-DE for optimal coordination of overcurrent relays in complex power systems

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Mohamed Zellagui

2017-09-01

Full Text Available The paper presents a new hybrid global optimization algorithm based on Chemical Reaction based Optimization (CRO and Di¤erential evolution (DE algorithm for nonlinear constrained optimization problems. This approach proposed for the optimal coordination and setting relays of directional overcurrent relays in complex power systems. In protection coordination problem, the objective function to be minimized is the sum of the operating time of all main relays. The optimization problem is subject to a number of constraints which are mainly focused on the operation of the backup relay, which should operate if a primary relay fails to respond to the fault near to it, Time Dial Setting (TDS, Plug Setting (PS and the minimum operating time of a relay. The hybrid global proposed optimization algorithm aims to minimize the total operating time of each protection relay. Two systems are used as case study to check the effeciency of the optimization algorithm which are IEEE 4-bus and IEEE 6-bus models. Results are obtained and presented for CRO and DE and hybrid CRO-DE algorithms. The obtained results for the studied cases are compared with those results obtained when using other optimization algorithms which are Teaching Learning-Based Optimization (TLBO, Chaotic Differential Evolution Algorithm (CDEA and Modiffied Differential Evolution Algorithm (MDEA, and Hybrid optimization algorithms (PSO-DE, IA-PSO, and BFOA-PSO. From analysing the obtained results, it has been concluded that hybrid CRO-DO algorithm provides the most optimum solution with the best convergence rate.

Research and Application on Fractional-Order Darwinian PSO Based Adaptive Extended Kalman Filtering Algorithm

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Qiguang Zhu

2014-05-01

Full Text Available To resolve the difficulty in establishing accurate priori noise model for the extended Kalman filtering algorithm, propose the fractional-order Darwinian particle swarm optimization (PSO algorithm has been proposed and introduced into the fuzzy adaptive extended Kalman filtering algorithm. The natural selection method has been adopted to improve the standard particle swarm optimization algorithm, which enhanced the diversity of particles and avoided the premature. In addition, the fractional calculus has been used to improve the evolution speed of particles. The PSO algorithm after improved has been applied to train fuzzy adaptive extended Kalman filter and achieve the simultaneous localization and mapping. The simulation results have shown that compared with the geese particle swarm optimization training of fuzzy adaptive extended Kalman filter localization and mapping algorithm, has been greatly improved in terms of localization and mapping.

Evidence-based algorithm for heparin dosing before cardiopulmonary bypass. Part 1: Development of the algorithm.

Science.gov (United States)

McKinney, Mark C; Riley, Jeffrey B

2007-12-01

The incidence of heparin resistance during adult cardiac surgery with cardiopulmonary bypass has been reported at 15%-20%. The consistent use of a clinical decision-making algorithm may increase the consistency of patient care and likely reduce the total required heparin dose and other problems associated with heparin dosing. After a directed survey of practicing perfusionists regarding treatment of heparin resistance and a literature search for high-level evidence regarding the diagnosis and treatment of heparin resistance, an evidence-based decision-making algorithm was constructed. The face validity of the algorithm decisive steps and logic was confirmed by a second survey of practicing perfusionists. The algorithm begins with review of the patient history to identify predictors for heparin resistance. The definition for heparin resistance contained in the algorithm is an activated clotting time 450 IU/kg heparin loading dose. Based on the literature, the treatment for heparin resistance used in the algorithm is anti-thrombin III supplement. The algorithm seems to be valid and is supported by high-level evidence and clinician opinion. The next step is a human randomized clinical trial to test the clinical procedure guideline algorithm vs. current standard clinical practice.

A range-based predictive localization algorithm for WSID networks

Science.gov (United States)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture series

CERN Multimedia

Françoise Benz

2004-01-01

ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...

A difference tracking algorithm based on discrete sine transform

Science.gov (United States)

Liu, HaoPeng; Yao, Yong; Lei, HeBing; Wu, HaoKun

2018-04-01

Target tracking is an important field of computer vision. The template matching tracking algorithm based on squared difference matching (SSD) and standard correlation coefficient (NCC) matching is very sensitive to the gray change of image. When the brightness or gray change, the tracking algorithm will be affected by high-frequency information. Tracking accuracy is reduced, resulting in loss of tracking target. In this paper, a differential tracking algorithm based on discrete sine transform is proposed to reduce the influence of image gray or brightness change. The algorithm that combines the discrete sine transform and the difference algorithm maps the target image into a image digital sequence. The Kalman filter predicts the target position. Using the Hamming distance determines the degree of similarity between the target and the template. The window closest to the template is determined the target to be tracked. The target to be tracked updates the template. Based on the above achieve target tracking. The algorithm is tested in this paper. Compared with SSD and NCC template matching algorithms, the algorithm tracks target stably when image gray or brightness change. And the tracking speed can meet the read-time requirement.

A random network based, node attraction facilitated network evolution method

Directory of Open Access Journals (Sweden)

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.

Generalized phase retrieval algorithm based on information measures

OpenAIRE

Shioya, Hiroyuki; Gohara, Kazutoshi

2006-01-01

An iterative phase retrieval algorithm based on the maximum entropy method (MEM) is presented. Introducing a new generalized information measure, we derive a novel class of algorithms which includes the conventionally used error reduction algorithm and a MEM-type iterative algorithm which is presented for the first time. These different phase retrieval methods are unified on the basis of the framework of information measures used in information theory.

Efficient sampling algorithms for Monte Carlo based treatment planning

International Nuclear Information System (INIS)

DeMarco, J.J.; Solberg, T.D.; Chetty, I.; Smathers, J.B.

1998-01-01

Efficient sampling algorithms are necessary for producing a fast Monte Carlo based treatment planning code. This study evaluates several aspects of a photon-based tracking scheme and the effect of optimal sampling algorithms on the efficiency of the code. Four areas were tested: pseudo-random number generation, generalized sampling of a discrete distribution, sampling from the exponential distribution, and delta scattering as applied to photon transport through a heterogeneous simulation geometry. Generalized sampling of a discrete distribution using the cutpoint method can produce speedup gains of one order of magnitude versus conventional sequential sampling. Photon transport modifications based upon the delta scattering method were implemented and compared with a conventional boundary and collision checking algorithm. The delta scattering algorithm is faster by a factor of six versus the conventional algorithm for a boundary size of 5 mm within a heterogeneous geometry. A comparison of portable pseudo-random number algorithms and exponential sampling techniques is also discussed

List-Based Simulated Annealing Algorithm for Traveling Salesman Problem

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Shi-hua Zhan

2016-01-01

Full Text Available Simulated annealing (SA algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters’ setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA algorithm to solve traveling salesman problem (TSP. LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.

Research on personalized recommendation algorithm based on spark

Science.gov (United States)

Li, Zeng; Liu, Yu

2018-04-01

With the increasing amount of data in the past years, the traditional recommendation algorithm has been unable to meet people's needs. Therefore, how to better recommend their products to users of interest, become the opportunities and challenges of the era of big data development. At present, each platform enterprise has its own recommendation algorithm, but how to make efficient and accurate push information is still an urgent problem for personalized recommendation system. In this paper, a hybrid algorithm based on user collaborative filtering and content-based recommendation algorithm is proposed on Spark to improve the efficiency and accuracy of recommendation by weighted processing. The experiment shows that the recommendation under this scheme is more efficient and accurate.

Fast image matching algorithm based on projection characteristics

Science.gov (United States)

Zhou, Lijuan; Yue, Xiaobo; Zhou, Lijun

2011-06-01

Based on analyzing the traditional template matching algorithm, this paper identified the key factors restricting the speed of matching and put forward a brand new fast matching algorithm based on projection. Projecting the grayscale image, this algorithm converts the two-dimensional information of the image into one-dimensional one, and then matches and identifies through one-dimensional correlation, meanwhile, because of normalization has been done, when the image brightness or signal amplitude increasing in proportion, it could also perform correct matching. Experimental results show that the projection characteristics based image registration method proposed in this article could greatly improve the matching speed, which ensuring the matching accuracy as well.

GPU-based parallel algorithm for blind image restoration using midfrequency-based methods

Science.gov (United States)

Xie, Lang; Luo, Yi-han; Bao, Qi-liang

2013-08-01

GPU-based general-purpose computing is a new branch of modern parallel computing, so the study of parallel algorithms specially designed for GPU hardware architecture is of great significance. In order to solve the problem of high computational complexity and poor real-time performance in blind image restoration, the midfrequency-based algorithm for blind image restoration was analyzed and improved in this paper. Furthermore, a midfrequency-based filtering method is also used to restore the image hardly with any recursion or iteration. Combining the algorithm with data intensiveness, data parallel computing and GPU execution model of single instruction and multiple threads, a new parallel midfrequency-based algorithm for blind image restoration is proposed in this paper, which is suitable for stream computing of GPU. In this algorithm, the GPU is utilized to accelerate the estimation of class-G point spread functions and midfrequency-based filtering. Aiming at better management of the GPU threads, the threads in a grid are scheduled according to the decomposition of the filtering data in frequency domain after the optimization of data access and the communication between the host and the device. The kernel parallelism structure is determined by the decomposition of the filtering data to ensure the transmission rate to get around the memory bandwidth limitation. The results show that, with the new algorithm, the operational speed is significantly increased and the real-time performance of image restoration is effectively improved, especially for high-resolution images.

Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

OpenAIRE

Yumin, Dong; Li, Zhao

2014-01-01

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarm and following activities, meanwhile using the a...

Algorithm of Particle Data Association for SLAM Based on Improved Ant Algorithm

Directory of Open Access Journals (Sweden)

KeKe Gen

2015-01-01

Full Text Available The article considers a problem of data association algorithm for simultaneous localization and mapping guidelines in determining the route of unmanned aerial vehicles (UAVs. Currently, these equipments are already widely used, but mainly controlled from the remote operator. An urgent task is to develop a control system that allows for autonomous flight. Algorithm SLAM (simultaneous localization and mapping, which allows to predict the location, speed, the ratio of flight parameters and the coordinates of landmarks and obstacles in an unknown environment, is one of the key technologies to achieve real autonomous UAV flight. The aim of this work is to study the possibility of solving this problem by using an improved ant algorithm.The data association for SLAM algorithm is meant to establish a matching set of observed landmarks and landmarks in the state vector. Ant algorithm is one of the widely used optimization algorithms with positive feedback and the ability to search in parallel, so the algorithm is suitable for solving the problem of data association for SLAM. But the traditional ant algorithm in the process of finding routes easily falls into local optimum. Adding random perturbations in the process of updating the global pheromone to avoid local optima. Setting limits pheromone on the route can increase the search space with a reasonable amount of calculations for finding the optimal route.The paper proposes an algorithm of the local data association for SLAM algorithm based on an improved ant algorithm. To increase the speed of calculation, local data association is used instead of the global data association. The first stage of the algorithm defines targets in the matching space and the observed landmarks with the possibility of association by the criterion of individual compatibility (IC. The second stage defines the matched landmarks and their coordinates using improved ant algorithm. Simulation results confirm the efficiency and

Parameter Selection for Ant Colony Algorithm Based on Bacterial Foraging Algorithm

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Peng Li

2016-01-01

Full Text Available The optimal performance of the ant colony algorithm (ACA mainly depends on suitable parameters; therefore, parameter selection for ACA is important. We propose a parameter selection method for ACA based on the bacterial foraging algorithm (BFA, considering the effects of coupling between different parameters. Firstly, parameters for ACA are mapped into a multidimensional space, using a chemotactic operator to ensure that each parameter group approaches the optimal value, speeding up the convergence for each parameter set. Secondly, the operation speed for optimizing the entire parameter set is accelerated using a reproduction operator. Finally, the elimination-dispersal operator is used to strengthen the global optimization of the parameters, which avoids falling into a local optimal solution. In order to validate the effectiveness of this method, the results were compared with those using a genetic algorithm (GA and a particle swarm optimization (PSO, and simulations were conducted using different grid maps for robot path planning. The results indicated that parameter selection for ACA based on BFA was the superior method, able to determine the best parameter combination rapidly, accurately, and effectively.

A Flocking Based algorithm for Document Clustering Analysis

Energy Technology Data Exchange (ETDEWEB)

Cui, Xiaohui [ORNL; Gao, Jinzhu [ORNL; Potok, Thomas E [ORNL

2006-01-01

Social animals or insects in nature often exhibit a form of emergent collective behavior known as flocking. In this paper, we present a novel Flocking based approach for document clustering analysis. Our Flocking clustering algorithm uses stochastic and heuristic principles discovered from observing bird flocks or fish schools. Unlike other partition clustering algorithm such as K-means, the Flocking based algorithm does not require initial partitional seeds. The algorithm generates a clustering of a given set of data through the embedding of the high-dimensional data items on a two-dimensional grid for easy clustering result retrieval and visualization. Inspired by the self-organized behavior of bird flocks, we represent each document object with a flock boid. The simple local rules followed by each flock boid result in the entire document flock generating complex global behaviors, which eventually result in a clustering of the documents. We evaluate the efficiency of our algorithm with both a synthetic dataset and a real document collection that includes 100 news articles collected from the Internet. Our results show that the Flocking clustering algorithm achieves better performance compared to the K- means and the Ant clustering algorithm for real document clustering.

AdaBoost-based algorithm for network intrusion detection.

Science.gov (United States)

Hu, Weiming; Hu, Wei; Maybank, Steve

2008-04-01

Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

Science.gov (United States)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

[The motive force of evolution based on the principle of organismal adjustment evolution.].

Science.gov (United States)

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.

Q-learning-based adjustable fixed-phase quantum Grover search algorithm

International Nuclear Information System (INIS)

Guo Ying; Shi Wensha; Wang Yijun; Hu, Jiankun

2017-01-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one. (author)

Geometric differential evolution for combinatorial and programs spaces.

Science.gov (United States)

Moraglio, A; Togelius, J; Silva, S

2013-01-01

Geometric differential evolution (GDE) is a recently introduced formal generalization of traditional differential evolution (DE) that can be used to derive specific differential evolution algorithms for both continuous and combinatorial spaces retaining the same geometric interpretation of the dynamics of the DE search across representations. In this article, we first review the theory behind the GDE algorithm, then, we use this framework to formally derive specific GDE for search spaces associated with binary strings, permutations, vectors of permutations and genetic programs. The resulting algorithms are representation-specific differential evolution algorithms searching the target spaces by acting directly on their underlying representations. We present experimental results for each of the new algorithms on a number of well-known problems comprising NK-landscapes, TSP, and Sudoku, for binary strings, permutations, and vectors of permutations. We also present results for the regression, artificial ant, parity, and multiplexer problems within the genetic programming domain. Experiments show that overall the new DE algorithms are competitive with well-tuned standard search algorithms.

Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

Science.gov (United States)

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.

An assembly sequence planning method based on composite algorithm

Directory of Open Access Journals (Sweden)

Enfu LIU

2016-02-01

Full Text Available To solve the combination explosion problem and the blind searching problem in assembly sequence planning of complex products, an assembly sequence planning method based on composite algorithm is proposed. In the composite algorithm, a sufficient number of feasible assembly sequences are generated using formalization reasoning algorithm as the initial population of genetic algorithm. Then fuzzy knowledge of assembly is integrated into the planning process of genetic algorithm and ant algorithm to get the accurate solution. At last, an example is conducted to verify the feasibility of composite algorithm.

Differential evolution optimization combined with chaotic sequences for image contrast enhancement

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br; Sauer, Joao Guilherme [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: joao.sauer@gmail.com; Rudek, Marcelo [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: marcelo.rudek@pucpr.br

2009-10-15

Evolutionary Algorithms (EAs) are stochastic and robust meta-heuristics of evolutionary computation field useful to solve optimization problems in image processing applications. Recently, as special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used in various designs of EAs. Three differential evolution approaches based on chaotic sequences using logistic equation for image enhancement process are proposed in this paper. Differential evolution is a simple yet powerful evolutionary optimization algorithm that has been successfully used in solving continuous problems. The proposed chaotic differential evolution schemes have fast convergence rate but also maintain the diversity of the population so as to escape from local optima. In this paper, the image contrast enhancement is approached as a constrained nonlinear optimization problem. The objective of the proposed chaotic differential evolution schemes is to maximize the fitness criterion in order to enhance the contrast and detail in the image by adapting the parameters using a contrast enhancement technique. The proposed chaotic differential evolution schemes are compared with classical differential evolution to two testing images. Simulation results on three images show that the application of chaotic sequences instead of random sequences is a possible strategy to improve the performance of classical differential evolution optimization algorithm.

Dynamic and quantitative method of analyzing service consistency evolution based on extended hierarchical finite state automata.

Science.gov (United States)

Fan, Linjun; Tang, Jun; Ling, Yunxiang; Li, Benxian

2014-01-01

This paper is concerned with the dynamic evolution analysis and quantitative measurement of primary factors that cause service inconsistency in service-oriented distributed simulation applications (SODSA). Traditional methods are mostly qualitative and empirical, and they do not consider the dynamic disturbances among factors in service's evolution behaviors such as producing, publishing, calling, and maintenance. Moreover, SODSA are rapidly evolving in terms of large-scale, reusable, compositional, pervasive, and flexible features, which presents difficulties in the usage of traditional analysis methods. To resolve these problems, a novel dynamic evolution model extended hierarchical service-finite state automata (EHS-FSA) is constructed based on finite state automata (FSA), which formally depict overall changing processes of service consistency states. And also the service consistency evolution algorithms (SCEAs) based on EHS-FSA are developed to quantitatively assess these impact factors. Experimental results show that the bad reusability (17.93% on average) is the biggest influential factor, the noncomposition of atomic services (13.12%) is the second biggest one, and the service version's confusion (1.2%) is the smallest one. Compared with previous qualitative analysis, SCEAs present good effectiveness and feasibility. This research can guide the engineers of service consistency technologies toward obtaining a higher level of consistency in SODSA.

Dynamic and Quantitative Method of Analyzing Service Consistency Evolution Based on Extended Hierarchical Finite State Automata

Directory of Open Access Journals (Sweden)

Linjun Fan

2014-01-01

Full Text Available This paper is concerned with the dynamic evolution analysis and quantitative measurement of primary factors that cause service inconsistency in service-oriented distributed simulation applications (SODSA. Traditional methods are mostly qualitative and empirical, and they do not consider the dynamic disturbances among factors in service’s evolution behaviors such as producing, publishing, calling, and maintenance. Moreover, SODSA are rapidly evolving in terms of large-scale, reusable, compositional, pervasive, and flexible features, which presents difficulties in the usage of traditional analysis methods. To resolve these problems, a novel dynamic evolution model extended hierarchical service-finite state automata (EHS-FSA is constructed based on finite state automata (FSA, which formally depict overall changing processes of service consistency states. And also the service consistency evolution algorithms (SCEAs based on EHS-FSA are developed to quantitatively assess these impact factors. Experimental results show that the bad reusability (17.93% on average is the biggest influential factor, the noncomposition of atomic services (13.12% is the second biggest one, and the service version’s confusion (1.2% is the smallest one. Compared with previous qualitative analysis, SCEAs present good effectiveness and feasibility. This research can guide the engineers of service consistency technologies toward obtaining a higher level of consistency in SODSA.

Algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales

International Nuclear Information System (INIS)

Li Tianduo; Xiao Gang; Di Yuming; Han Feng; Qiu Xiaoling

1999-01-01

The γ energy spectrum is expanded in allied energy-frequency space. By the different characterization of the evolution of wavelet transform modulus maxima across scales between energy spectrum and noise, the algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales is presented. The results show, in contrast to the methods in energy space or in frequency space, the method has the advantages that the peak of energy spectrum can be indicated accurately and the energy spectrum can be reconstructed with a good approximation

Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

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Pandiarajan K.

2014-09-01

Full Text Available This paper presents an effective method of network overload management in power systems. The three competing objectives 1 generation cost 2 transmission line overload and 3 real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO and Differential evolution (DE. Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

Reactive power dispatch considering voltage stability with seeker optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Dai, Chaohua; Chen, Weirong; Zhang, Xuexia [The School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031 (China); Zhu, Yunfang [Department of Computer and Communication Engineering, E' mei Campus, Southwest Jiaotong University, E' mei 614202 (China)

2009-10-15

Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem. (author)

Multi-objective mixture-based iterated density estimation evolutionary algorithms

NARCIS (Netherlands)

Thierens, D.; Bosman, P.A.N.

2001-01-01

We propose an algorithm for multi-objective optimization using a mixture-based iterated density estimation evolutionary algorithm (MIDEA). The MIDEA algorithm is a prob- abilistic model building evolutionary algo- rithm that constructs at each generation a mixture of factorized probability

Quantum Adiabatic Algorithms and Large Spin Tunnelling

Science.gov (United States)

Boulatov, A.; Smelyanskiy, V. N.

2003-01-01

We provide a theoretical study of the quantum adiabatic evolution algorithm with different evolution paths proposed in this paper. The algorithm is applied to a random binary optimization problem (a version of the 3-Satisfiability problem) where the n-bit cost function is symmetric with respect to the permutation of individual bits. The evolution paths are produced, using the generic control Hamiltonians H (r) that preserve the bit symmetry of the underlying optimization problem. In the case where the ground state of H(0) coincides with the totally-symmetric state of an n-qubit system the algorithm dynamics is completely described in terms of the motion of a spin-n/2. We show that different control Hamiltonians can be parameterized by a set of independent parameters that are expansion coefficients of H (r) in a certain universal set of operators. Only one of these operators can be responsible for avoiding the tunnelling in the spin-n/2 system during the quantum adiabatic algorithm. We show that it is possible to select a coefficient for this operator that guarantees a polynomial complexity of the algorithm for all problem instances. We show that a successful evolution path of the algorithm always corresponds to the trajectory of a classical spin-n/2 and provide a complete characterization of such paths.

Constructing HVS-Based Optimal Substitution Matrix Using Enhanced Differential Evolution

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Shu-Fen Tu

2013-01-01

Full Text Available Least significant bit (LSB substitution is a method of information hiding. The secret message is embedded into the last k bits of a cover-image in order to evade the notice of hackers. The security and stego-image quality are two main limitations of the LSB substitution method. Therefore, some researchers have proposed an LSB substitution matrix to address these two issues. Finding the optimal LSB substitution matrix can be conceptualized as a problem of combinatorial optimization. In this paper, we adopt a different heuristic method based on other researchers’ method, called enhanced differential evolution (EDE, to construct an optimal LSB substitution matrix. Differing from other researchers, we adopt an HVS-based measurement as a fitness function and embed the secret by modifying the pixel to a closest value rather than simply substituting the LSBs. Our scheme extracts the secret by modular operations as simple LSB substitution does. The experimental results show that the proposed embedding algorithm indeed improves imperceptibility of stego-images substantially.

A Novel Quad Harmony Search Algorithm for Grid-Based Path Finding

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Saso Koceski

2014-09-01

Full Text Available A novel approach to the problem of grid-based path finding has been introduced. The method is a block-based search algorithm, founded on the bases of two algorithms, namely the quad-tree algorithm, which offered a great opportunity for decreasing the time needed to compute the solution, and the harmony search (HS algorithm, a meta-heuristic algorithm used to obtain the optimal solution. This quad HS algorithm uses the quad-tree decomposition of free space in the grid to mark the free areas and treat them as a single node, which greatly improves the execution. The results of the quad HS algorithm have been compared to other meta-heuristic algorithms, i.e., ant colony, genetic algorithm, particle swarm optimization and simulated annealing, and it was proved to obtain the best results in terms of time and giving the optimal path.

Text Clustering Algorithm Based on Random Cluster Core

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Huang Long-Jun

2016-01-01

Full Text Available Nowadays clustering has become a popular text mining algorithm, but the huge data can put forward higher requirements for the accuracy and performance of text mining. In view of the performance bottleneck of traditional text clustering algorithm, this paper proposes a text clustering algorithm with random features. This is a kind of clustering algorithm based on text density, at the same time using the neighboring heuristic rules, the concept of random cluster is introduced, which effectively reduces the complexity of the distance calculation.

Agent-based Algorithm for Spatial Distribution of Objects

KAUST Repository

Collier, Nathan

2012-06-02

In this paper we present an agent-based algorithm for the spatial distribution of objects. The algorithm is a generalization of the bubble mesh algorithm, initially created for the point insertion stage of the meshing process of the finite element method. The bubble mesh algorithm treats objects in space as bubbles, which repel and attract each other. The dynamics of each bubble are approximated by solving a series of ordinary differential equations. We present numerical results for a meshing application as well as a graph visualization application.

Comparison of phase unwrapping algorithms for topography reconstruction based on digital speckle pattern interferometry

Science.gov (United States)

Li, Yuanbo; Cui, Xiaoqian; Wang, Hongbei; Zhao, Mengge; Ding, Hongbin

2017-10-01

Digital speckle pattern interferometry (DSPI) can diagnose the topography evolution in real-time, continuous and non-destructive, and has been considered as a most promising technique for Plasma-Facing Components (PFCs) topography diagnostic under the complicated environment of tokamak. It is important for the study of digital speckle pattern interferometry to enhance speckle patterns and obtain the real topography of the ablated crater. In this paper, two kinds of numerical model based on flood-fill algorithm has been developed to obtain the real profile by unwrapping from the wrapped phase in speckle interference pattern, which can be calculated through four intensity images by means of 4-step phase-shifting technique. During the process of phase unwrapping by means of flood-fill algorithm, since the existence of noise pollution, and other inevitable factors will lead to poor quality of the reconstruction results, this will have an impact on the authenticity of the restored topography. The calculation of the quality parameters was introduced to obtain the quality-map from the wrapped phase map, this work presents two different methods to calculate the quality parameters. Then quality parameters are used to guide the path of flood-fill algorithm, and the pixels with good quality parameters are given priority calculation, so that the quality of speckle interference pattern reconstruction results are improved. According to the comparison between the flood-fill algorithm which is suitable for speckle pattern interferometry and the quality-guided flood-fill algorithm (with two different calculation approaches), the errors which caused by noise pollution and the discontinuous of the strips were successfully reduced.

NLSE: Parameter-Based Inversion Algorithm

Science.gov (United States)

Sabbagh, Harold A.; Murphy, R. Kim; Sabbagh, Elias H.; Aldrin, John C.; Knopp, Jeremy S.

Chapter 11 introduced us to the notion of an inverse problem and gave us some examples of the value of this idea to the solution of realistic industrial problems. The basic inversion algorithm described in Chap. 11 was based upon the Gauss-Newton theory of nonlinear least-squares estimation and is called NLSE in this book. In this chapter we will develop the mathematical background of this theory more fully, because this algorithm will be the foundation of inverse methods and their applications during the remainder of this book. We hope, thereby, to introduce the reader to the application of sophisticated mathematical concepts to engineering practice without introducing excessive mathematical sophistication.

Star point centroid algorithm based on background forecast

Science.gov (United States)

Wang, Jin; Zhao, Rujin; Zhu, Nan

2014-09-01

The calculation of star point centroid is a key step of improving star tracker measuring error. A star map photoed by APS detector includes several noises which have a great impact on veracity of calculation of star point centroid. Through analysis of characteristic of star map noise, an algorithm of calculation of star point centroid based on background forecast is presented in this paper. The experiment proves the validity of the algorithm. Comparing with classic algorithm, this algorithm not only improves veracity of calculation of star point centroid, but also does not need calibration data memory. This algorithm is applied successfully in a certain star tracker.

Research on compressive sensing reconstruction algorithm based on total variation model

Science.gov (United States)

Gao, Yu-xuan; Sun, Huayan; Zhang, Tinghua; Du, Lin

2017-12-01

Compressed sensing for breakthrough Nyquist sampling theorem provides a strong theoretical , making compressive sampling for image signals be carried out simultaneously. In traditional imaging procedures using compressed sensing theory, not only can it reduces the storage space, but also can reduce the demand for detector resolution greatly. Using the sparsity of image signal, by solving the mathematical model of inverse reconfiguration, realize the super-resolution imaging. Reconstruction algorithm is the most critical part of compression perception, to a large extent determine the accuracy of the reconstruction of the image.The reconstruction algorithm based on the total variation (TV) model is more suitable for the compression reconstruction of the two-dimensional image, and the better edge information can be obtained. In order to verify the performance of the algorithm, Simulation Analysis the reconstruction result in different coding mode of the reconstruction algorithm based on the TV reconstruction algorithm. The reconstruction effect of the reconfigurable algorithm based on TV based on the different coding methods is analyzed to verify the stability of the algorithm. This paper compares and analyzes the typical reconstruction algorithm in the same coding mode. On the basis of the minimum total variation algorithm, the Augmented Lagrangian function term is added and the optimal value is solved by the alternating direction method.Experimental results show that the reconstruction algorithm is compared with the traditional classical algorithm based on TV has great advantages, under the low measurement rate can be quickly and accurately recovers target image.

Parallel image encryption algorithm based on discretized chaotic map

International Nuclear Information System (INIS)

Zhou Qing; Wong Kwokwo; Liao Xiaofeng; Xiang Tao; Hu Yue

2008-01-01

Recently, a variety of chaos-based algorithms were proposed for image encryption. Nevertheless, none of them works efficiently in parallel computing environment. In this paper, we propose a framework for parallel image encryption. Based on this framework, a new algorithm is designed using the discretized Kolmogorov flow map. It fulfills all the requirements for a parallel image encryption algorithm. Moreover, it is secure and fast. These properties make it a good choice for image encryption on parallel computing platforms

The Ground Flash Fraction Retrieval Algorithm Employing Differential Evolution: Simulations and Applications

Science.gov (United States)

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

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

Directory of Open Access Journals (Sweden)

Betania Hernández-Ocaña

2016-01-01

Full Text Available This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.

A Modularity Degree Based Heuristic Community Detection Algorithm

Directory of Open Access Journals (Sweden)

Dongming Chen

2014-01-01

Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.

An Innovative Thinking-Based Intelligent Information Fusion Algorithm

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Huimin Lu

2013-01-01

Full Text Available This study proposes an intelligent algorithm that can realize information fusion in reference to the relative research achievements in brain cognitive theory and innovative computation. This algorithm treats knowledge as core and information fusion as a knowledge-based innovative thinking process. Furthermore, the five key parts of this algorithm including information sense and perception, memory storage, divergent thinking, convergent thinking, and evaluation system are simulated and modeled. This algorithm fully develops innovative thinking skills of knowledge in information fusion and is a try to converse the abstract conception of brain cognitive science to specific and operable research routes and strategies. Furthermore, the influences of each parameter of this algorithm on algorithm performance are analyzed and compared with those of classical intelligent algorithms trough test. Test results suggest that the algorithm proposed in this study can obtain the optimum problem solution by less target evaluation times, improve optimization effectiveness, and achieve the effective fusion of information.

Image segmentation algorithm based on T-junctions cues

Science.gov (United States)

Qian, Yanyu; Cao, Fengyun; Wang, Lu; Yang, Xuejie

2016-03-01

To improve the over-segmentation and over-merge phenomenon of single image segmentation algorithm,a novel approach of combing Graph-Based algorithm and T-junctions cues is proposed in this paper. First, a method by L0 gradient minimization is applied to the smoothing of the target image eliminate artifacts caused by noise and texture detail; Then, the initial over-segmentation result of the smoothing image using the graph-based algorithm; Finally, the final results via a region fusion strategy by t-junction cues. Experimental results on a variety of images verify the new approach's efficiency in eliminating artifacts caused by noise,segmentation accuracy and time complexity has been significantly improved.

Assessing the Suitability of the ClOud Reflection Algorithm (CORA) in Modelling the Evolution of an Artificial Plasma Cloud in the Ionosphere

Science.gov (United States)

Jackson-Booth, N.

2016-12-01

Artificial Ionospheric Modification (AIM) attempts to modify the ionosphere in order to alter the propagation environment. It can be achieved through injecting the ionosphere with aerosols, chemicals or radio signals. The effects of any such release can be detected through the deployment of sensors, including ground based high frequency (HF) sounders. During the Metal Oxide Space Clouds (MOSC) experiment (undertaken in April/May 2013 in the Kwajalein Atoll, part of the Marshall Islands) several oblique ionograms were recorded from a ground based HF system. These ionograms were collected over multiple geometries and allowed the effects on the HF propagation environment to be understood. These ionograms have subsequently been used in the ClOud Reflection Algorithm (CORA) to attempt to model the evolution of the cloud following release. This paper describes the latest validation results from CORA, both from testing against ionograms, but also other independent models of cloud evolution from MOSC. For all testing the various cloud models (including that generated by CORA) were incorporated into a background ionosphere through which a 3D numerical ray trace was run to produce synthetic ionograms that could be compared with the ionograms recorded during MOSC.

Accelerating parameter identification of proton exchange membrane fuel cell model with ranking-based differential evolution

International Nuclear Information System (INIS)

Gong, Wenyin; Cai, Zhihua

2013-01-01

Parameter identification of PEM (proton exchange membrane) fuel cell model is a very active area of research. Generally, it can be treated as a numerical optimization problem with complex nonlinear and multi-variable features. DE (differential evolution), which has been successfully used in various fields, is a simple yet efficient evolutionary algorithm for global numerical optimization. In this paper, with the objective of accelerating the process of parameter identification of PEM fuel cell models and reducing the necessary computational efforts, we firstly present a generic and simple ranking-based mutation operator for the DE algorithm. Then, the ranking-based mutation operator is incorporated into five highly-competitive DE variants to solve the PEM fuel cell model parameter identification problems. The main contributions of this work are the proposed ranking-based DE variants and their application to the parameter identification problems of PEM fuel cell models. Experiments have been conducted by using both the simulated voltage–current data and the data obtained from the literature to validate the performance of our approach. The results indicate that the ranking-based DE methods provide better results with respect to the solution quality, the convergence rate, and the success rate compared with their corresponding original DE methods. In addition, the voltage–current characteristics obtained by our approach are in good agreement with the original voltage–current curves in all cases. - Highlights: • A simple and generic ranking-based mutation operator is presented in this paper. • Several DE (differential evolution) variants are used to solve the parameter identification of PEMFC (proton exchange membrane fuel cells) model. • Results show that our method accelerates the process of parameter identification. • The V–I characteristics are in very good agreement with experimental data

Multiple-algorithm parallel fusion of infrared polarization and intensity images based on algorithmic complementarity and synergy

Science.gov (United States)

Zhang, Lei; Yang, Fengbao; Ji, Linna; Lv, Sheng

2018-01-01

Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.

DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks

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Francisco José Estévez

2016-06-01

Full Text Available The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities.

Spectrum sensing algorithm based on autocorrelation energy in cognitive radio networks

Science.gov (United States)

Ren, Shengwei; Zhang, Li; Zhang, Shibing

2016-10-01

Cognitive radio networks have wide applications in the smart home, personal communications and other wireless communication. Spectrum sensing is the main challenge in cognitive radios. This paper proposes a new spectrum sensing algorithm which is based on the autocorrelation energy of signal received. By taking the autocorrelation energy of the received signal as the statistics of spectrum sensing, the effect of the channel noise on the detection performance is reduced. Simulation results show that the algorithm is effective and performs well in low signal-to-noise ratio. Compared with the maximum generalized eigenvalue detection (MGED) algorithm, function of covariance matrix based detection (FMD) algorithm and autocorrelation-based detection (AD) algorithm, the proposed algorithm has 2 11 dB advantage.

Digital Image Encryption Algorithm Design Based on Genetic Hyperchaos

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Jian Wang

2016-01-01

Full Text Available In view of the present chaotic image encryption algorithm based on scrambling (diffusion is vulnerable to choosing plaintext (ciphertext attack in the process of pixel position scrambling, we put forward a image encryption algorithm based on genetic super chaotic system. The algorithm, by introducing clear feedback to the process of scrambling, makes the scrambling effect related to the initial chaos sequence and the clear text itself; it has realized the image features and the organic fusion of encryption algorithm. By introduction in the process of diffusion to encrypt plaintext feedback mechanism, it improves sensitivity of plaintext, algorithm selection plaintext, and ciphertext attack resistance. At the same time, it also makes full use of the characteristics of image information. Finally, experimental simulation and theoretical analysis show that our proposed algorithm can not only effectively resist plaintext (ciphertext attack, statistical attack, and information entropy attack but also effectively improve the efficiency of image encryption, which is a relatively secure and effective way of image communication.

Betweenness-based algorithm for a partition scale-free graph

International Nuclear Information System (INIS)

Zhang Bai-Da; Wu Jun-Jie; Zhou Jing; Tang Yu-Hua

2011-01-01

Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom—up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top—down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches. (interdisciplinary physics and related areas of science and technology)

Analog Circuit Design Optimization Based on Evolutionary Algorithms

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Mansour Barari

2014-01-01

Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives

International Nuclear Information System (INIS)

Warmflash, Aryeh; Siggia, Eric D; Francois, Paul

2012-01-01

The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input–output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria. (paper)

Pareto evolution of gene networks: an algorithm to optimize multiple fitness objectives.

Science.gov (United States)

Warmflash, Aryeh; Francois, Paul; Siggia, Eric D

2012-10-01

The computational evolution of gene networks functions like a forward genetic screen to generate, without preconceptions, all networks that can be assembled from a defined list of parts to implement a given function. Frequently networks are subject to multiple design criteria that cannot all be optimized simultaneously. To explore how these tradeoffs interact with evolution, we implement Pareto optimization in the context of gene network evolution. In response to a temporal pulse of a signal, we evolve networks whose output turns on slowly after the pulse begins, and shuts down rapidly when the pulse terminates. The best performing networks under our conditions do not fall into categories such as feed forward and negative feedback that also encode the input-output relation we used for selection. Pareto evolution can more efficiently search the space of networks than optimization based on a single ad hoc combination of the design criteria.

Improved dynamic-programming-based algorithms for segmentation of masses in mammograms

International Nuclear Information System (INIS)

Dominguez, Alfonso Rojas; Nandi, Asoke K.

2007-01-01

In this paper, two new boundary tracing algorithms for segmentation of breast masses are presented. These new algorithms are based on the dynamic programming-based boundary tracing (DPBT) algorithm proposed in Timp and Karssemeijer, [S. Timp and N. Karssemeijer, Med. Phys. 31, 958-971 (2004)] The DPBT algorithm contains two main steps: (1) construction of a local cost function, and (2) application of dynamic programming to the selection of the optimal boundary based on the local cost function. The validity of some assumptions used in the design of the DPBT algorithm is tested in this paper using a set of 349 mammographic images. Based on the results of the tests, modifications to the computation of the local cost function have been designed and have resulted in the Improved-DPBT (IDPBT) algorithm. A procedure for the dynamic selection of the strength of the components of the local cost function is presented that makes these parameters independent of the image dataset. Incorporation of this dynamic selection procedure has produced another new algorithm which we have called ID 2 PBT. Methods for the determination of some other parameters of the DPBT algorithm that were not covered in the original paper are presented as well. The merits of the new IDPBT and ID 2 PBT algorithms are demonstrated experimentally by comparison against the DPBT algorithm. The segmentation results are evaluated with base on the area overlap measure and other segmentation metrics. Both of the new algorithms outperform the original DPBT; the improvements in the algorithms performance are more noticeable around the values of the segmentation metrics corresponding to the highest segmentation accuracy, i.e., the new algorithms produce more optimally segmented regions, rather than a pronounced increase in the average quality of all the segmented regions

LSB Based Quantum Image Steganography Algorithm

Science.gov (United States)

Jiang, Nan; Zhao, Na; Wang, Luo

2016-01-01

Quantum steganography is the technique which hides a secret message into quantum covers such as quantum images. In this paper, two blind LSB steganography algorithms in the form of quantum circuits are proposed based on the novel enhanced quantum representation (NEQR) for quantum images. One algorithm is plain LSB which uses the message bits to substitute for the pixels' LSB directly. The other is block LSB which embeds a message bit into a number of pixels that belong to one image block. The extracting circuits can regain the secret message only according to the stego cover. Analysis and simulation-based experimental results demonstrate that the invisibility is good, and the balance between the capacity and the robustness can be adjusted according to the needs of applications.

Human resource recommendation algorithm based on ensemble learning and Spark

Science.gov (United States)

Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

2017-08-01

Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

Secure image encryption algorithm design using a novel chaos based S-Box

International Nuclear Information System (INIS)

Çavuşoğlu, Ünal; Kaçar, Sezgin; Pehlivan, Ihsan; Zengin, Ahmet

2017-01-01

Highlights: • A new chaotic system is developed for creating S-Box and image encryption algorithm. • Chaos based random number generator is designed with the help of the new chaotic system. NIST tests are run on generated random numbers to verify randomness. • A new S-Box design algorithm is developed to create the chaos based S-Box to be utilized in encryption algorithm and performance tests are made. • The new developed S-Box based image encryption algorithm is introduced and image encryption application is carried out. • To show the quality and strong of the encryption process, security analysis are performed and compared with the AES and chaos algorithms. - Abstract: In this study, an encryption algorithm that uses chaos based S-BOX is developed for secure and speed image encryption. First of all, a new chaotic system is developed for creating S-Box and image encryption algorithm. Chaos based random number generator is designed with the help of the new chaotic system. Then, NIST tests are run on generated random numbers to verify randomness. A new S-Box design algorithm is developed to create the chaos based S-Box to be utilized in encryption algorithm and performance tests are made. As the next step, the new developed S-Box based image encryption algorithm is introduced in detail. Finally, image encryption application is carried out. To show the quality and strong of the encryption process, security analysis are performed. Proposed algorithm is compared with the AES and chaos algorithms. According to tests results, the proposed image encryption algorithm is secure and speed for image encryption application.

Parameterless evolutionary algorithm applied to the nuclear reload problem

International Nuclear Information System (INIS)

Caldas, Gustavo Henrique Flores; Schirru, Roberto

2008-01-01

In this work, an evolutionary algorithm with no parameters called FPBIL (parameter free PBIL) is developed based on PBIL (population-based incremental learning). Moreover, the analysis reveals how the parameters from PBIL can be replaced by self-adaptable mechanisms which appear from the radically different form by which the evolution is processed. Despite the advantages, the FPBIL reveals itself compact and relatively modest in the use of computational resources. The FPBIL is then applied to the nuclear reload problem. The experimental results observed are compared to those of other works and corroborate to affirm the superiority of the new algorithm

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

Science.gov (United States)

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

TaDb: A time-aware diffusion-based recommender algorithm

Science.gov (United States)

Li, Wen-Jun; Xu, Yuan-Yuan; Dong, Qiang; Zhou, Jun-Lin; Fu, Yan

2015-02-01

Traditional recommender algorithms usually employ the early and recent records indiscriminately, which overlooks the change of user interests over time. In this paper, we show that the interests of a user remain stable in a short-term interval and drift during a long-term period. Based on this observation, we propose a time-aware diffusion-based (TaDb) recommender algorithm, which assigns different temporal weights to the leading links existing before the target user's collection and the following links appearing after that in the diffusion process. Experiments on four real datasets, Netflix, MovieLens, FriendFeed and Delicious show that TaDb algorithm significantly improves the prediction accuracy compared with the algorithms not considering temporal effects.

Variants of Evolutionary Algorithms for Real-World Applications

CERN Document Server

Weise, Thomas; Michalewicz, Zbigniew

2012-01-01

Evolutionary Algorithms (EAs) are population-based, stochastic search algorithms that mimic natural evolution. Due to their ability to find excellent solutions for conventionally hard and dynamic problems within acceptable time, EAs have attracted interest from many researchers and practitioners in recent years. This book “Variants of Evolutionary Algorithms for Real-World Applications” aims to promote the practitioner’s view on EAs by providing a comprehensive discussion of how EAs can be adapted to the requirements of various applications in the real-world domains. It comprises 14 chapters, including an introductory chapter re-visiting the fundamental question of what an EA is and other chapters addressing a range of real-world problems such as production process planning, inventory system and supply chain network optimisation, task-based jobs assignment, planning for CNC-based work piece construction, mechanical/ship design tasks that involve runtime-intense simulations, data mining for the predictio...

Incident Light Frequency-Based Image Defogging Algorithm

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Wenbo Zhang

2017-01-01

Full Text Available To solve the color distortion problem produced by the dark channel prior algorithm, an improved method for calculating transmittance of all channels, respectively, was proposed in this paper. Based on the Beer-Lambert Law, the influence between the frequency of the incident light and the transmittance was analyzed, and the ratios between each channel’s transmittance were derived. Then, in order to increase efficiency, the input image was resized to a smaller size before acquiring the refined transmittance which will be resized to the same size of original image. Finally, all the transmittances were obtained with the help of the proportion between each color channel, and then they were used to restore the defogging image. Experiments suggest that the improved algorithm can produce a much more natural result image in comparison with original algorithm, which means the problem of high color saturation was eliminated. What is more, the improved algorithm speeds up by four to nine times compared to the original algorithm.

Evaluation of Key Dependent S-Box Based Data Security Algorithm using Hamming Distance and Balanced Output

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Balajee Maram K.

2016-02-01

Full Text Available Data security is a major issue because of rapid evolution of data communication over unsecured internetwork. Here the proposed system is concerned with the problem of randomly generated S-box. The generation of S-box depends on Pseudo-Random-Number-Generators and shared-secret-key. The process of Pseudo-Random-Number-Generator depends on large prime numbers. All Pseudo-Random-Numbers are scrambled according to shared-secret-key. After scrambling, the S-box is generated. In this research, large prime numbers are the inputs to the Pseudo-Random-Number-Generator. The proposed S-box will reduce the complexity of S-box generation. Based on S-box parameters, it experimentally investigates the quality and robustness of the proposed algorithm which was tested. It yields better results with the S-box parameters like Hamming Distance, Balanced Output and Avalanche Effect and can be embedded to popular cryptography algorithms

Quantum Computation and Algorithms

International Nuclear Information System (INIS)

Biham, O.; Biron, D.; Biham, E.; Grassi, M.; Lidar, D.A.

1999-01-01

It is now firmly established that quantum algorithms provide a substantial speedup over classical algorithms for a variety of problems, including the factorization of large numbers and the search for a marked element in an unsorted database. In this talk I will review the principles of quantum algorithms, the basic quantum gates and their operation. The combination of superposition and interference, that makes these algorithms efficient, will be discussed. In particular, Grover's search algorithm will be presented as an example. I will show that the time evolution of the amplitudes in Grover's algorithm can be found exactly using recursion equations, for any initial amplitude distribution

A Turn-Projected State-Based Conflict Resolution Algorithm

Science.gov (United States)

Butler, Ricky W.; Lewis, Timothy A.

2013-01-01

State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.

Adaptive CGFs Based on Grammatical Evolution

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Jian Yao

2015-01-01

Full Text Available Computer generated forces (CGFs play blue or red units in military simulations for personnel training and weapon systems evaluation. Traditionally, CGFs are controlled through rule-based scripts, despite the doctrine-driven behavior of CGFs being rigid and predictable. Furthermore, CGFs are often tricked by trainees or fail to adapt to new situations (e.g., changes in battle field or update in weapon systems, and, in most cases, the subject matter experts (SMEs review and redesign a large amount of CGF scripts for new scenarios or training tasks, which is both challenging and time-consuming. In an effort to overcome these limitations and move toward more true-to-life scenarios, a study using grammatical evolution (GE to generate adaptive CGFs for air combat simulations has been conducted. Expert knowledge is encoded with modular behavior trees (BTs for compatibility with the operators in genetic algorithm (GA. GE maps CGFs, represented with BTs to binary strings, and uses GA to evolve CGFs with performance feedback from the simulation. Beyond-visual-range air combat experiments between adaptive CGFs and nonadaptive baseline CGFs have been conducted to observe and study this evolutionary process. The experimental results show that the GE is an efficient framework to generate CGFs in BTs formalism and evolve CGFs via GA.

Super-Relaxed ( -Proximal Point Algorithms, Relaxed ( -Proximal Point Algorithms, Linear Convergence Analysis, and Nonlinear Variational Inclusions

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Agarwal RaviP

2009-01-01

Full Text Available We glance at recent advances to the general theory of maximal (set-valued monotone mappings and their role demonstrated to examine the convex programming and closely related field of nonlinear variational inequalities. We focus mostly on applications of the super-relaxed ( -proximal point algorithm to the context of solving a class of nonlinear variational inclusion problems, based on the notion of maximal ( -monotonicity. Investigations highlighted in this communication are greatly influenced by the celebrated work of Rockafellar (1976, while others have played a significant part as well in generalizing the proximal point algorithm considered by Rockafellar (1976 to the case of the relaxed proximal point algorithm by Eckstein and Bertsekas (1992. Even for the linear convergence analysis for the overrelaxed (or super-relaxed ( -proximal point algorithm, the fundamental model for Rockafellar's case does the job. Furthermore, we attempt to explore possibilities of generalizing the Yosida regularization/approximation in light of maximal ( -monotonicity, and then applying to first-order evolution equations/inclusions.

Particle Swarm Optimization algorithms for geophysical inversion, practical hints

Science.gov (United States)

Garcia Gonzalo, E.; Fernandez Martinez, J.; Fernandez Alvarez, J.; Kuzma, H.; Menendez Perez, C.

2008-12-01

PSO is a stochastic optimization technique that has been successfully used in many different engineering fields. PSO algorithm can be physically interpreted as a stochastic damped mass-spring system (Fernandez Martinez and Garcia Gonzalo 2008). Based on this analogy we present a whole family of PSO algorithms and their respective first order and second order stability regions. Their performance is also checked using synthetic functions (Rosenbrock and Griewank) showing a degree of ill-posedness similar to that found in many geophysical inverse problems. Finally, we present the application of these algorithms to the analysis of a Vertical Electrical Sounding inverse problem associated to a seawater intrusion in a coastal aquifer in South Spain. We analyze the role of PSO parameters (inertia, local and global accelerations and discretization step), both in convergence curves and in the a posteriori sampling of the depth of an intrusion. Comparison is made with binary genetic algorithms and simulated annealing. As result of this analysis, practical hints are given to select the correct algorithm and to tune the corresponding PSO parameters. Fernandez Martinez, J.L., Garcia Gonzalo, E., 2008a. The generalized PSO: a new door to PSO evolution. Journal of Artificial Evolution and Applications. DOI:10.1155/2008/861275.

Genetic algorithms applied to nuclear reactor design optimization

International Nuclear Information System (INIS)

Pereira, C.M.N.A.; Schirru, R.; Martinez, A.S.

2000-01-01

A genetic algorithm is a powerful search technique that simulates natural evolution in order to fit a population of computational structures to the solution of an optimization problem. This technique presents several advantages over classical ones such as linear programming based techniques, often used in nuclear engineering optimization problems. However, genetic algorithms demand some extra computational cost. Nowadays, due to the fast computers available, the use of genetic algorithms has increased and its practical application has become a reality. In nuclear engineering there are many difficult optimization problems related to nuclear reactor design. Genetic algorithm is a suitable technique to face such kind of problems. This chapter presents applications of genetic algorithms for nuclear reactor core design optimization. A genetic algorithm has been designed to optimize the nuclear reactor cell parameters, such as array pitch, isotopic enrichment, dimensions and cells materials. Some advantages of this genetic algorithm implementation over a classical method based on linear programming are revealed through the application of both techniques to a simple optimization problem. In order to emphasize the suitability of genetic algorithms for design optimization, the technique was successfully applied to a more complex problem, where the classical method is not suitable. Results and comments about the applications are also presented. (orig.)

Analysis and improvement of a chaos-based image encryption algorithm

International Nuclear Information System (INIS)

Xiao Di; Liao Xiaofeng; Wei Pengcheng

2009-01-01

The security of digital image attracts much attention recently. In Guan et al. [Guan Z, Huang F, Guan W. Chaos-based image encryption algorithm. Phys Lett A 2005; 346: 153-7.], a chaos-based image encryption algorithm has been proposed. In this paper, the cause of potential flaws in the original algorithm is analyzed in detail, and then the corresponding enhancement measures are proposed. Both theoretical analysis and computer simulation indicate that the improved algorithm can overcome these flaws and maintain all the merits of the original one.

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

Directory of Open Access Journals (Sweden)

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.

Visual Perception Based Rate Control Algorithm for HEVC

Science.gov (United States)

Feng, Zeqi; Liu, PengYu; Jia, Kebin

2018-01-01

For HEVC, rate control is an indispensably important video coding technology to alleviate the contradiction between video quality and the limited encoding resources during video communication. However, the rate control benchmark algorithm of HEVC ignores subjective visual perception. For key focus regions, bit allocation of LCU is not ideal and subjective quality is unsatisfied. In this paper, a visual perception based rate control algorithm for HEVC is proposed. First bit allocation weight of LCU level is optimized based on the visual perception of luminance and motion to ameliorate video subjective quality. Then λ and QP are adjusted in combination with the bit allocation weight to improve rate distortion performance. Experimental results show that the proposed algorithm reduces average 0.5% BD-BR and maximum 1.09% BD-BR at no cost in bitrate accuracy compared with HEVC (HM15.0). The proposed algorithm devotes to improving video subjective quality under various video applications.

Cognitive radio resource allocation based on coupled chaotic genetic algorithm

International Nuclear Information System (INIS)

Zu Yun-Xiao; Zhou Jie; Zeng Chang-Chang

2010-01-01

A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed

Local Community Detection Algorithm Based on Minimal Cluster

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Yong Zhou

2016-01-01

Full Text Available In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation

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Suk-Ju Kang

2016-12-01

Full Text Available This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F1 score by up to 0.490, compared with benchmark algorithms.

Differential Evolution for Many-Particle Adaptive Quantum Metrology

NARCIS (Netherlands)

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

KM-FCM: A fuzzy clustering optimization algorithm based on Mahalanobis distance

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Zhiwen ZU

2018-04-01

Full Text Available The traditional fuzzy clustering algorithm uses Euclidean distance as the similarity criterion, which is disadvantageous to the multidimensional data processing. In order to solve this situation, Mahalanobis distance is used instead of the traditional Euclidean distance, and the optimization of fuzzy clustering algorithm based on Mahalanobis distance is studied to enhance the clustering effect and ability. With making the initialization means by Heuristic search algorithm combined with k-means algorithm, and in terms of the validity function which could automatically adjust the optimal clustering number, an optimization algorithm KM-FCM is proposed. The new algorithm is compared with FCM algorithm, FCM-M algorithm and M-FCM algorithm in three standard data sets. The experimental results show that the KM-FCM algorithm is effective. It has higher clustering accuracy than FCM, FCM-M and M-FCM, recognizing high-dimensional data clustering well. It has global optimization effect, and the clustering number has no need for setting in advance. The new algorithm provides a reference for the optimization of fuzzy clustering algorithm based on Mahalanobis distance.

Seismic active control by a heuristic-based algorithm

International Nuclear Information System (INIS)

Tang, Yu.

1996-01-01

A heuristic-based algorithm for seismic active control is generalized to permit consideration of the effects of control-structure interaction and actuator dynamics. Control force is computed at onetime step ahead before being applied to the structure. Therefore, the proposed control algorithm is free from the problem of time delay. A numerical example is presented to show the effectiveness of the proposed control algorithm. Also, two indices are introduced in the paper to assess the effectiveness and efficiency of control laws

Environmental/economic dispatch problem of power system by using an enhanced multi-objective differential evolution algorithm

International Nuclear Information System (INIS)

Lu Youlin; Zhou Jianzhong; Qin Hui; Wang Ying; Zhang Yongchuan

2011-01-01

An enhanced multi-objective differential evolution algorithm (EMODE) is proposed in this paper to solve environmental/economic dispatch (EED) problem by considering the minimal of fuel cost and emission effects synthetically. In the proposed algorithm, an elitist archive technique is adopted to retain the non-dominated solutions obtained during the evolutionary process, and the operators of DE are modified according to the characteristics of multi-objective optimization problems. Moreover, in order to avoid premature convergence, a local random search (LRS) operator is integrated with the proposed method to improve the convergence performance. In view of the difficulties of handling the complicated constraints of EED problem, a new heuristic constraints handling method without any penalty factor settings is presented. The feasibility and effectiveness of the proposed EMODE method is demonstrated for a test power system. Compared with other methods, EMODE can get higher quality solutions by reducing the fuel cost and the emission effects synthetically.

A New Improved Quantum Evolution Algorithm with Local Search Procedure for Capacitated Vehicle Routing Problem

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Ligang Cui

2013-01-01

Full Text Available The capacitated vehicle routing problem (CVRP is the most classical vehicle routing problem (VRP; many solution techniques are proposed to find its better answer. In this paper, a new improved quantum evolution algorithm (IQEA with a mixed local search procedure is proposed for solving CVRPs. First, an IQEA with a double chain quantum chromosome, new quantum rotation schemes, and self-adaptive quantum Not gate is constructed to initialize and generate feasible solutions. Then, to further strengthen IQEA's searching ability, three local search procedures 1-1 exchange, 1-0 exchange, and 2-OPT, are adopted. Experiments on a small case have been conducted to analyze the sensitivity of main parameters and compare the performances of the IQEA with different local search strategies. Together with results from the testing of CVRP benchmarks, the superiorities of the proposed algorithm over the PSO, SR-1, and SR-2 have been demonstrated. At last, a profound analysis of the experimental results is presented and some suggestions on future researches are given.

The concept of ageing in evolutionary algorithms: Discussion and inspirations for human ageing.

Science.gov (United States)

Dimopoulos, Christos; Papageorgis, Panagiotis; Boustras, George; Efstathiades, Christodoulos

2017-04-01

This paper discusses the concept of ageing as this applies to the operation of Evolutionary Algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. Evolutionary Algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of Evolutionary Algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of Evolutionary Algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

Darwinian evolution on a chip.

Directory of Open Access Journals (Sweden)

Brian M Paegel

2008-04-01

Full Text Available Computer control of Darwinian evolution has been demonstrated by propagating a population of RNA enzymes in a microfluidic device. The RNA population was challenged to catalyze the ligation of an oligonucleotide substrate under conditions of progressively lower substrate concentrations. A microchip-based serial dilution circuit automated an exponential growth phase followed by a 10-fold dilution, which was repeated for 500 log-growth iterations. Evolution was observed in real time as the population adapted and achieved progressively faster growth rates over time. The final evolved enzyme contained a set of 11 mutations that conferred a 90-fold improvement in substrate utilization, coinciding with the applied selective pressure. This system reduces evolution to a microfluidic algorithm, allowing the experimenter to observe and manipulate adaptation.

A Parallel Encryption Algorithm Based on Piecewise Linear Chaotic Map

Directory of Open Access Journals (Sweden)

Xizhong Wang

2013-01-01

Full Text Available We introduce a parallel chaos-based encryption algorithm for taking advantage of multicore processors. The chaotic cryptosystem is generated by the piecewise linear chaotic map (PWLCM. The parallel algorithm is designed with a master/slave communication model with the Message Passing Interface (MPI. The algorithm is suitable not only for multicore processors but also for the single-processor architecture. The experimental results show that the chaos-based cryptosystem possesses good statistical properties. The parallel algorithm provides much better performance than the serial ones and would be useful to apply in encryption/decryption file with large size or multimedia.

A Novel Clustering Algorithm Inspired by Membrane Computing

Directory of Open Access Journals (Sweden)

Hong Peng

2015-01-01

Full Text Available P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.

Evolution strategy based optimal chiller loading for saving energy

International Nuclear Information System (INIS)

Chang, Y.-C.; Lee, C.-Y.; Chen, C.-R.; Chou, C.-J.; Chen, W.-H.; Chen, W.-H.

2009-01-01

This study employs evolution strategy (ES) to solve optimal chiller loading (OCL) problem. ES overcomes the flaw that Lagrangian method is not adaptable for solving OCL as the power consumption models or the kW-PLR (partial load ratio) curves include convex functions and concave functions simultaneously. The complicated process of evolution by the genetic algorithm (GA) method for solving OCL can also be simplified by the ES method. This study uses the PLR of chiller as the variable to be solved for the decoupled air conditioning system. After analysis and comparison of the case study, it has been concluded that this method not only solves the problems of Lagrangian method and GA method, but also produces results with high accuracy within a rapid timeframe. It can be perfectly applied to the operation of air conditioning systems

Radioactivity nuclide identification based on BP and LM algorithm neural network

International Nuclear Information System (INIS)

Wang Jihong; Sun Jian; Wang Lianghou

2012-01-01

The paper provides the method which can identify radioactive nuclide based on the BP and LM algorithm neural network. Then, this paper compares the above-mentioned method with FR algorithm. Through the result of the Matlab simulation, the method of radioactivity nuclide identification based on the BP and LM algorithm neural network is superior to the FR algorithm. With the better effect and the higher accuracy, it will be the best choice. (authors)

MICRONEEDLE STRUCTURE DESIGN AND OPTIMIZATION USING GENETIC ALGORITHM

OpenAIRE

N. A. ISMAIL; S. C. NEOH; N. SABANI; B. N. TAIB

2015-01-01

This paper presents a Genetic Algorithm (GA) based microneedle design and analysis. GA is an evolutionary optimization technique that mimics the natural biological evolution. The design of microneedle structure considers the shape of microneedle, material used, size of the array, the base of microneedle, the lumen base, the height of microneedle, the height of the lumen, and the height of the drug container or reservoir. The GA is executed in conjunction with ANSYS simulation system to assess...

A new edge detection algorithm based on Canny idea

Science.gov (United States)

Feng, Yingke; Zhang, Jinmin; Wang, Siming

2017-10-01

The traditional Canny algorithm has poor self-adaptability threshold, and it is more sensitive to noise. In order to overcome these drawbacks, this paper proposed a new edge detection method based on Canny algorithm. Firstly, the media filtering and filtering based on the method of Euclidean distance are adopted to process it; secondly using the Frei-chen algorithm to calculate gradient amplitude; finally, using the Otsu algorithm to calculate partial gradient amplitude operation to get images of thresholds value, then find the average of all thresholds that had been calculated, half of the average is high threshold value, and the half of the high threshold value is low threshold value. Experiment results show that this new method can effectively suppress noise disturbance, keep the edge information, and also improve the edge detection accuracy.

A Coupled User Clustering Algorithm Based on Mixed Data for Web-Based Learning Systems

Directory of Open Access Journals (Sweden)

Ke Niu

2015-01-01

Full Text Available In traditional Web-based learning systems, due to insufficient learning behaviors analysis and personalized study guides, a few user clustering algorithms are introduced. While analyzing the behaviors with these algorithms, researchers generally focus on continuous data but easily neglect discrete data, each of which is generated from online learning actions. Moreover, there are implicit coupled interactions among the data but are frequently ignored in the introduced algorithms. Therefore, a mass of significant information which can positively affect clustering accuracy is neglected. To solve the above issues, we proposed a coupled user clustering algorithm for Wed-based learning systems by taking into account both discrete and continuous data, as well as intracoupled and intercoupled interactions of the data. The experiment result in this paper demonstrates the outperformance of the proposed algorithm.

Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

Directory of Open Access Journals (Sweden)

Lixiong Xu

2017-01-01

Full Text Available As one of the most effective function mining algorithms, Gene Expression Programming (GEP algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks.

An efficient algorithm for sorting by block-interchanges and its application to the evolution of vibrio species.

Science.gov (United States)

Lin, Ying Chih; Lu, Chin Lung; Chang, Hwan-You; Tang, Chuan Yi

2005-01-01

In the study of genome rearrangement, the block-interchanges have been proposed recently as a new kind of global rearrangement events affecting a genome by swapping two nonintersecting segments of any length. The so-called block-interchange distance problem, which is equivalent to the sorting-by-block-interchange problem, is to find a minimum series of block-interchanges for transforming one chromosome into another. In this paper, we study this problem by considering the circular chromosomes and propose a Omicron(deltan) time algorithm for solving it by making use of permutation groups in algebra, where n is the length of the circular chromosome and delta is the minimum number of block-interchanges required for the transformation, which can be calculated in Omicron(n) time in advance. Moreover, we obtain analogous results by extending our algorithm to linear chromosomes. Finally, we have implemented our algorithm and applied it to the circular genomic sequences of three human vibrio pathogens for predicting their evolutionary relationships. Consequently, our experimental results coincide with the previous ones obtained by others using a different comparative genomics approach, which implies that the block-interchange events seem to play a significant role in the evolution of vibrio species.

Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

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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.

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes

Science.gov (United States)

Lin, Shu

1998-01-01

A code trellis is a graphical representation of a code, block or convolutional, in which every path represents a codeword (or a code sequence for a convolutional code). This representation makes it possible to implement Maximum Likelihood Decoding (MLD) of a code with reduced decoding complexity. The most well known trellis-based MLD algorithm is the Viterbi algorithm. The trellis representation was first introduced and used for convolutional codes [23]. This representation, together with the Viterbi decoding algorithm, has resulted in a wide range of applications of convolutional codes for error control in digital communications over the last two decades. There are two major reasons for this inactive period of research in this area. First, most coding theorists at that time believed that block codes did not have simple trellis structure like convolutional codes and maximum likelihood decoding of linear block codes using the Viterbi algorithm was practically impossible, except for very short block codes. Second, since almost all of the linear block codes are constructed algebraically or based on finite geometries, it was the belief of many coding theorists that algebraic decoding was the only way to decode these codes. These two reasons seriously hindered the development of efficient soft-decision decoding methods for linear block codes and their applications to error control in digital communications. This led to a general belief that block codes are inferior to convolutional codes and hence, that they were not useful. Chapter 2 gives a brief review of linear block codes. The goal is to provide the essential background material for the development of trellis structure and trellis-based decoding algorithms for linear block codes in the later chapters. Chapters 3 through 6 present the fundamental concepts, finite-state machine model, state space formulation, basic structural properties, state labeling, construction procedures, complexity, minimality, and

Fast stochastic algorithm for simulating evolutionary population dynamics

Science.gov (United States)

Tsimring, Lev; Hasty, Jeff; Mather, William

2012-02-01

Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

Science.gov (United States)

Ge, Junwei; Cai, Yu; Fang, Yiqiu

2018-05-01

This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

Teaching learning based optimization algorithm and its engineering applications

CERN Document Server

Rao, R Venkata

2016-01-01

Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.

Relatively Inexact Proximal Point Algorithm and Linear Convergence Analysis

Directory of Open Access Journals (Sweden)

Ram U. Verma

2009-01-01

Full Text Available Based on a notion of relatively maximal (m-relaxed monotonicity, the approximation solvability of a general class of inclusion problems is discussed, while generalizing Rockafellar's theorem (1976 on linear convergence using the proximal point algorithm in a real Hilbert space setting. Convergence analysis, based on this new model, is simpler and compact than that of the celebrated technique of Rockafellar in which the Lipschitz continuity at 0 of the inverse of the set-valued mapping is applied. Furthermore, it can be used to generalize the Yosida approximation, which, in turn, can be applied to first-order evolution equations as well as evolution inclusions.

The Research and Application of SURF Algorithm Based on Feature Point Selection Algorithm

Directory of Open Access Journals (Sweden)

Zhang Fang Hu

2014-04-01

Full Text Available As the pixel information of depth image is derived from the distance information, when implementing SURF algorithm with KINECT sensor for static sign language recognition, there can be some mismatched pairs in palm area. This paper proposes a feature point selection algorithm, by filtering the SURF feature points step by step based on the number of feature points within adaptive radius r and the distance between the two points, it not only greatly improves the recognition rate, but also ensures the robustness under the environmental factors, such as skin color, illumination intensity, complex background, angle and scale changes. The experiment results show that the improved SURF algorithm can effectively improve the recognition rate, has a good robustness.

A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

Directory of Open Access Journals (Sweden)

Zhiming Song

2015-01-01

Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

A cross-disciplinary introduction to quantum annealing-based algorithms

Science.gov (United States)

Venegas-Andraca, Salvador E.; Cruz-Santos, William; McGeoch, Catherine; Lanzagorta, Marco

2018-04-01

A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science; hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model. We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.

A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm

Directory of Open Access Journals (Sweden)

Liping Liu

2018-01-01

Full Text Available Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO from two aspects: first, we introduce differential evolution (DE process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

New MPPT algorithm based on hybrid dynamical theory

KAUST Repository

Elmetennani, Shahrazed

2014-11-01

This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.

New MPPT algorithm based on hybrid dynamical theory

KAUST Repository

Elmetennani, Shahrazed; Laleg-Kirati, Taous-Meriem; Benmansour, K.; Boucherit, M. S.; Tadjine, M.

2014-01-01

This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.

Segment-based dose optimization using a genetic algorithm

International Nuclear Information System (INIS)

Cotrutz, Cristian; Xing Lei

2003-01-01

Intensity modulated radiation therapy (IMRT) inverse planning is conventionally done in two steps. Firstly, the intensity maps of the treatment beams are optimized using a dose optimization algorithm. Each of them is then decomposed into a number of segments using a leaf-sequencing algorithm for delivery. An alternative approach is to pre-assign a fixed number of field apertures and optimize directly the shapes and weights of the apertures. While the latter approach has the advantage of eliminating the leaf-sequencing step, the optimization of aperture shapes is less straightforward than that of beamlet-based optimization because of the complex dependence of the dose on the field shapes, and their weights. In this work we report a genetic algorithm for segment-based optimization. Different from a gradient iterative approach or simulated annealing, the algorithm finds the optimum solution from a population of candidate plans. In this technique, each solution is encoded using three chromosomes: one for the position of the left-bank leaves of each segment, the second for the position of the right-bank and the third for the weights of the segments defined by the first two chromosomes. The convergence towards the optimum is realized by crossover and mutation operators that ensure proper exchange of information between the three chromosomes of all the solutions in the population. The algorithm is applied to a phantom and a prostate case and the results are compared with those obtained using beamlet-based optimization. The main conclusion drawn from this study is that the genetic optimization of segment shapes and weights can produce highly conformal dose distribution. In addition, our study also confirms previous findings that fewer segments are generally needed to generate plans that are comparable with the plans obtained using beamlet-based optimization. Thus the technique may have useful applications in facilitating IMRT treatment planning

An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems

Directory of Open Access Journals (Sweden)

Vivek Patel

2012-08-01

Full Text Available Nature inspired population based algorithms is a research field which simulates different natural phenomena to solve a wide range of problems. Researchers have proposed several algorithms considering different natural phenomena. Teaching-Learning-based optimization (TLBO is one of the recently proposed population based algorithm which simulates the teaching-learning process of the class room. This algorithm does not require any algorithm-specific control parameters. In this paper, elitism concept is introduced in the TLBO algorithm and its effect on the performance of the algorithm is investigated. The effects of common controlling parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. The proposed algorithm is tested on 35 constrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. The proposed algorithm can be applied to various optimization problems of the industrial environment.

Stochastic quasi-gradient based optimization algorithms for dynamic reliability applications

International Nuclear Information System (INIS)

Bourgeois, F.; Labeau, P.E.

2001-01-01

On one hand, PSA results are increasingly used in decision making, system management and optimization of system design. On the other hand, when severe accidental transients are considered, dynamic reliability appears appropriate to account for the complex interaction between the transitions between hardware configurations, the operator behavior and the dynamic evolution of the system. This paper presents an exploratory work in which the estimation of the system unreliability in a dynamic context is coupled with an optimization algorithm to determine the 'best' safety policy. Because some reliability parameters are likely to be distributed, the cost function to be minimized turns out to be a random variable. Stochastic programming techniques are therefore envisioned to determine an optimal strategy. Monte Carlo simulation is used at all stages of the computations, from the estimation of the system unreliability to that of the stochastic quasi-gradient. The optimization algorithm is illustrated on a HNO 3 supply system

Electron dose map inversion based on several algorithms

International Nuclear Information System (INIS)

Li Gui; Zheng Huaqing; Wu Yican; Fds Team

2010-01-01

The reconstruction to the electron dose map in radiation therapy was investigated by constructing the inversion model of electron dose map with different algorithms. The inversion model of electron dose map based on nonlinear programming was used, and this model was applied the penetration dose map to invert the total space one. The realization of this inversion model was by several inversion algorithms. The test results with seven samples show that except the NMinimize algorithm, which worked for just one sample, with great error,though,all the inversion algorithms could be realized to our inversion model rapidly and accurately. The Levenberg-Marquardt algorithm, having the greatest accuracy and speed, could be considered as the first choice in electron dose map inversion.Further tests show that more error would be created when the data close to the electron range was used (tail error). The tail error might be caused by the approximation of mean energy spectra, and this should be considered to improve the method. The time-saving and accurate algorithms could be used to achieve real-time dose map inversion. By selecting the best inversion algorithm, the clinical need in real-time dose verification can be satisfied. (authors)

New calibration algorithms for dielectric-based microwave moisture sensors

Science.gov (United States)

New calibration algorithms for determining moisture content in granular and particulate materials from measurement of the dielectric properties at a single microwave frequency are proposed. The algorithms are based on identifying empirically correlations between the dielectric properties and the par...

A Data Forward Stepwise Fitting Algorithm Based on Orthogonal Function System

Directory of Open Access Journals (Sweden)

Li Han-Ju

2017-01-01

Full Text Available Data fitting is the main method of functional data analysis, and it is widely used in the fields of economy, social science, engineering technology and so on. Least square method is the main method of data fitting, but the least square method is not convergent, no memory property, big fitting error and it is easy to over fitting. Based on the orthogonal trigonometric function system, this paper presents a data forward stepwise fitting algorithm. This algorithm takes forward stepwise fitting strategy, each time using the nearest base function to fit the residual error generated by the previous base function fitting, which makes the residual mean square error minimum. In this paper, we theoretically prove the convergence, the memory property and the fitting error diminishing character for the algorithm. Experimental results show that the proposed algorithm is effective, and the fitting performance is better than that of the least square method and the forward stepwise fitting algorithm based on the non-orthogonal function system.

Newton algorithm for Hamiltonian characterization in quantum control

International Nuclear Information System (INIS)

Ndong, M; Sugny, D; Salomon, J

2014-01-01

We propose a Newton algorithm to characterize the Hamiltonian of a quantum system interacting with a given laser field. The algorithm is based on the assumption that the evolution operator of the system is perfectly known at a fixed time. The computational scheme uses the Crank–Nicholson approximation to explicitly determine the derivatives of the propagator with respect to the Hamiltonians of the system. In order to globalize this algorithm, we use a continuation method that improves its convergence properties. This technique is applied to a two-level quantum system and to a molecular one with a double-well potential. The numerical tests show that accurate estimates of the unknown parameters are obtained in some cases. We discuss the numerical limits of the algorithm in terms of the basin of convergence and the non-uniqueness of the solution. (paper)

Automated Vectorization of Decision-Based Algorithms

Science.gov (United States)

James, Mark

2006-01-01

Virtually all existing vectorization algorithms are designed to only analyze the numeric properties of an algorithm and distribute those elements across multiple processors. This advances the state of the practice because it is the only known system, at the time of this reporting, that takes high-level statements and analyzes them for their decision properties and converts them to a form that allows them to automatically be executed in parallel. The software takes a high-level source program that describes a complex decision- based condition and rewrites it as a disjunctive set of component Boolean relations that can then be executed in parallel. This is important because parallel architectures are becoming more commonplace in conventional systems and they have always been present in NASA flight systems. This technology allows one to take existing condition-based code and automatically vectorize it so it naturally decomposes across parallel architectures.

Convergence and Applications of a Gossip-Based Gauss-Newton Algorithm

Science.gov (United States)

Li, Xiao; Scaglione, Anna

2013-11-01

The Gauss-Newton algorithm is a popular and efficient centralized method for solving non-linear least squares problems. In this paper, we propose a multi-agent distributed version of this algorithm, named Gossip-based Gauss-Newton (GGN) algorithm, which can be applied in general problems with non-convex objectives. Furthermore, we analyze and present sufficient conditions for its convergence and show numerically that the GGN algorithm achieves performance comparable to the centralized algorithm, with graceful degradation in case of network failures. More importantly, the GGN algorithm provides significant performance gains compared to other distributed first order methods.

Soft gluon evolution and non-global logarithms

Science.gov (United States)

Martínez, René Ángeles; De Angelis, Matthew; Forshaw, Jeffrey R.; Plätzer, Simon; Seymour, Michael H.

2018-05-01

We consider soft-gluon evolution at the amplitude level. Our evolution algorithm applies to generic hard-scattering processes involving any number of coloured partons and we present a reformulation of the algorithm in such a way as to make the cancellation of infrared divergences explicit. We also emphasise the special role played by a Lorentz-invariant evolution variable, which coincides with the transverse momentum of the latest emission in a suitably defined dipole zero-momentum frame. Handling large colour matrices presents the most significant challenge to numerical implementations and we present a means to expand systematically about the leading colour approximation. Specifically, we present a systematic procedure to calculate the resulting colour traces, which is based on the colour flow basis. Identifying the leading contribution leads us to re-derive the Banfi-Marchesini-Smye equation. However, our formalism is more general and can systematically perform resummation of contributions enhanced by the t'Hooft coupling α s N ˜ 1, along with successive perturbations that are parametrically suppressed by powers of 1 /N . We also discuss how our approach relates to earlier work.

Automatic Knowledge Base Evolution by Learning Instances

OpenAIRE

Kim, Sundong

2016-01-01

Knowledge base is the way to store structured and unstructured data throughout the web. Since the size of the web is increasing rapidly, there are huge needs to structure the knowledge in a fully automated way. However fully-automated knowledge-base evolution on the Semantic Web is a major challenges, although there are many ontology evolution techniques available. Therefore learning ontology automatically can contribute to the semantic web society significantly. In this paper, we propose ful...

Coarse Alignment Technology on Moving base for SINS Based on the Improved Quaternion Filter Algorithm.

Science.gov (United States)

Zhang, Tao; Zhu, Yongyun; Zhou, Feng; Yan, Yaxiong; Tong, Jinwu

2017-06-17

Initial alignment of the strapdown inertial navigation system (SINS) is intended to determine the initial attitude matrix in a short time with certain accuracy. The alignment accuracy of the quaternion filter algorithm is remarkable, but the convergence rate is slow. To solve this problem, this paper proposes an improved quaternion filter algorithm for faster initial alignment based on the error model of the quaternion filter algorithm. The improved quaternion filter algorithm constructs the K matrix based on the principle of optimal quaternion algorithm, and rebuilds the measurement model by containing acceleration and velocity errors to make the convergence rate faster. A doppler velocity log (DVL) provides the reference velocity for the improved quaternion filter alignment algorithm. In order to demonstrate the performance of the improved quaternion filter algorithm in the field, a turntable experiment and a vehicle test are carried out. The results of the experiments show that the convergence rate of the proposed improved quaternion filter is faster than that of the tradition quaternion filter algorithm. In addition, the improved quaternion filter algorithm also demonstrates advantages in terms of correctness, effectiveness, and practicability.

Online evolution for multi-action adversarial games

DEFF Research Database (Denmark)

Justesen, Niels Orsleff; Mahlmann, Tobias; Togelius, Julian

2016-01-01

the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online...

Shaping asteroid models using genetic evolution (SAGE)

Science.gov (United States)

Bartczak, P.; Dudziński, G.

2018-02-01

In this work, we present SAGE (shaping asteroid models using genetic evolution), an asteroid modelling algorithm based solely on photometric lightcurve data. It produces non-convex shapes, orientations of the rotation axes and rotational periods of asteroids. The main concept behind a genetic evolution algorithm is to produce random populations of shapes and spin-axis orientations by mutating a seed shape and iterating the process until it converges to a stable global minimum. We tested SAGE on five artificial shapes. We also modelled asteroids 433 Eros and 9 Metis, since ground truth observations for them exist, allowing us to validate the models. We compared the derived shape of Eros with the NEAR Shoemaker model and that of Metis with adaptive optics and stellar occultation observations since other models from various inversion methods were available for Metis.

A chaos-based image encryption algorithm with variable control parameters

International Nuclear Information System (INIS)

Wang Yong; Wong, K.-W.; Liao Xiaofeng; Xiang Tao; Chen Guanrong

2009-01-01

In recent years, a number of image encryption algorithms based on the permutation-diffusion structure have been proposed. However, the control parameters used in the permutation stage are usually fixed in the whole encryption process, which favors attacks. In this paper, a chaos-based image encryption algorithm with variable control parameters is proposed. The control parameters used in the permutation stage and the keystream employed in the diffusion stage are generated from two chaotic maps related to the plain-image. As a result, the algorithm can effectively resist all known attacks against permutation-diffusion architectures. Theoretical analyses and computer simulations both confirm that the new algorithm possesses high security and fast encryption speed for practical image encryption.

Noise filtering algorithm for the MFTF-B computer based control system

International Nuclear Information System (INIS)

Minor, E.G.

1983-01-01

An algorithm to reduce the message traffic in the MFTF-B computer based control system is described. The algorithm filters analog inputs to the control system. Its purpose is to distinguish between changes in the inputs due to noise and changes due to significant variations in the quantity being monitored. Noise is rejected while significant changes are reported to the control system data base, thus keeping the data base updated with a minimum number of messages. The algorithm is memory efficient, requiring only four bytes of storage per analog channel, and computationally simple, requiring only subtraction and comparison. Quantitative analysis of the algorithm is presented for the case of additive Gaussian noise. It is shown that the algorithm is stable and tends toward the mean value of the monitored variable over a wide variety of additive noise distributions

Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm

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N. Sri Madhava Raja

2014-01-01

Full Text Available Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD guided firefly algorithm (FA. A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF guided FA and random operator guided FA. The performance assessment comparison between the proposed and existing firefly algorithms is carried using prevailing parameters such as objective function, standard deviation, peak-to-signal ratio (PSNR, structural similarity (SSIM index, and search time of CPU. The results show that BD guided FA provides better objective function, PSNR, and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.

A Cultural Algorithm for Optimal Design of Truss Structures

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Shahin Jalili

Full Text Available Abstract A cultural algorithm was utilized in this study to solve optimal design of truss structures problem achieving minimum weight objective under stress and deflection constraints. The algorithm is inspired by principles of human social evolution. It simulates the social interaction between the peoples and their beliefs in a belief space. Cultural Algorithm (CA utilizes the belief space and population space which affects each other based on acceptance and influence functions. The belief space of CA consists of different knowledge components. In this paper, only situational and normative knowledge components are used within the belief space. The performance of the method is demonstrated through four benchmark design examples. Comparison of the obtained results with those of some previous studies demonstrates the efficiency of this algorithm.

An algorithm to construct Groebner bases for solving integration by parts relations

International Nuclear Information System (INIS)

Smirnov, Alexander V.

2006-01-01

This paper is a detailed description of an algorithm based on a generalized Buchberger algorithm for constructing Groebner-type bases associated with polynomials of shift operators. The algorithm is used to calculate Feynman integrals and has proved to be efficient in several complicated cases

An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

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Kai Hu

2013-01-01

Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

An accurate projection algorithm for array processor based SPECT systems

International Nuclear Information System (INIS)

King, M.A.; Schwinger, R.B.; Cool, S.L.

1985-01-01

A data re-projection algorithm has been developed for use in single photon emission computed tomography (SPECT) on an array processor based computer system. The algorithm makes use of an accurate representation of pixel activity (uniform square pixel model of intensity distribution), and is rapidly performed due to the efficient handling of an array based algorithm and the Fast Fourier Transform (FFT) on parallel processing hardware. The algorithm consists of using a pixel driven nearest neighbour projection operation to an array of subdivided projection bins. This result is then convolved with the projected uniform square pixel distribution before being compressed to original bin size. This distribution varies with projection angle and is explicitly calculated. The FFT combined with a frequency space multiplication is used instead of a spatial convolution for more rapid execution. The new algorithm was tested against other commonly used projection algorithms by comparing the accuracy of projections of a simulated transverse section of the abdomen against analytically determined projections of that transverse section. The new algorithm was found to yield comparable or better standard error and yet result in easier and more efficient implementation on parallel hardware. Applications of the algorithm include iterative reconstruction and attenuation correction schemes and evaluation of regions of interest in dynamic and gated SPECT

Optimization algorithm based on densification and dynamic canonical descent

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Bousson, K.; Correia, S. D.

2006-07-01

Stochastic methods have gained some popularity in global optimization in that most of them do not assume the cost functions to be differentiable. They have capabilities to avoid being trapped by local optima, and may converge even faster than gradient-based optimization methods on some problems. The present paper proposes an optimization method, which reduces the search space by means of densification curves, coupled with the dynamic canonical descent algorithm. The performances of the new method are shown on several known problems classically used for testing optimization algorithms, and proved to outperform competitive algorithms such as simulated annealing and genetic algorithms.

Optimization of heliostat field layout in solar central receiver systems on annual basis using differential evolution algorithm

International Nuclear Information System (INIS)

Atif, Maimoon; Al-Sulaiman, Fahad A.

2015-01-01

Highlights: • Differential evolution optimization model was developed to optimize the heliostat field. • Five optical parameters were considered for the optimization of the optical efficiency. • Optimization using insolation weighted and un-weighted annual efficiency are developed. • The daily averaged annual optical efficiency was calculated to be 0.5023 while the monthly was 0.5025. • The insolation weighted daily averaged annual efficiency was 0.5634. - Abstract: Optimization of a heliostat field is an essential task to make a solar central receiver system effective because major optical losses are associated with the heliostat fields. In this study, a mathematical model was developed to effectively optimize the heliostat field on annual basis using differential evolution, which is an evolutionary algorithm. The heliostat field layout optimization is based on the calculation of five optical performance parameters: the mirror or the heliostat reflectivity, the cosine factor, the atmospheric attenuation factor, the shadowing and blocking factor, and the intercept factor. This model calculates all the aforementioned performance parameters at every stage of the optimization, until the best heliostat field layout based on annual performance is obtained. Two different approaches were undertaken to optimize the heliostat field layout: one with optimizing insolation weighted annual efficiency and the other with optimizing the un-weighted annual efficiency. Moreover, an alternate approach was also proposed to efficiently optimize the heliostat field in which the number of computational time steps was considerably reduced. It was observed that the daily averaged annual optical efficiency was calculated to be 0.5023 as compared to the monthly averaged annual optical efficiency, 0.5025. Moreover, the insolation weighted daily averaged annual efficiency of the heliostat field was 0.5634 for Dhahran, Saudi Arabia. The code developed can be used for any other

Mapping the evolution of scientific fields.

Science.gov (United States)

Herrera, Mark; Roberts, David C; Gulbahce, Natali

2010-05-04

Despite the apparent cross-disciplinary interactions among scientific fields, a formal description of their evolution is lacking. Here we describe a novel approach to study the dynamics and evolution of scientific fields using a network-based analysis. We build an idea network consisting of American Physical Society Physics and Astronomy Classification Scheme (PACS) numbers as nodes representing scientific concepts. Two PACS numbers are linked if there exist publications that reference them simultaneously. We locate scientific fields using a community finding algorithm, and describe the time evolution of these fields over the course of 1985-2006. The communities we identify map to known scientific fields, and their age depends on their size and activity. We expect our approach to quantifying the evolution of ideas to be relevant for making predictions about the future of science and thus help to guide its development.

A novel clustering algorithm based on quantum games

International Nuclear Information System (INIS)

Li Qiang; He Yan; Jiang Jingping

2009-01-01

Enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum game with the problem of data clustering, and then develop a quantum-game-based clustering algorithm, in which data points in a dataset are considered as players who can make decisions and implement quantum strategies in quantum games. After each round of a quantum game, each player's expected payoff is calculated. Later, he uses a link-removing-and-rewiring (LRR) function to change his neighbors and adjust the strength of links connecting to them in order to maximize his payoff. Further, algorithms are discussed and analyzed in two cases of strategies, two payoff matrixes and two LRR functions. Consequently, the simulation results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.

A Plagiarism Detection Algorithm based on Extended Winnowing

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Duan Xuliang

2017-01-01

Full Text Available Plagiarism is a common problem faced by academia and education. Mature commercial plagiarism detection system has the advantages of comprehensive and high accuracy, but the expensive detection costs make it unsuitable for real-time, lightweight application environment such as the student assignments plagiarism detection. This paper introduces the method of extending classic Winnowing plagiarism detection algorithm, expands the algorithm in functionality. The extended algorithm can retain the text location and length information in original document while extracting the fingerprints of a document, so that the locating and marking for plagiarism text fragment are much easier to achieve. The experimental results and several years of running practice show that the expansion of the algorithm has little effect on its performance, normal hardware configuration of PC will be able to meet small and medium-sized applications requirements. Based on the characteristics of lightweight, high efficiency, reliability and flexibility of Winnowing, the extended algorithm further enhances the adaptability and extends the application areas.

A similarity based agglomerative clustering algorithm in networks

Science.gov (United States)

Liu, Zhiyuan; Wang, Xiujuan; Ma, Yinghong

2018-04-01

The detection of clusters is benefit for understanding the organizations and functions of networks. Clusters, or communities, are usually groups of nodes densely interconnected but sparsely linked with any other clusters. To identify communities, an efficient and effective community agglomerative algorithm based on node similarity is proposed. The proposed method initially calculates similarities between each pair of nodes, and form pre-partitions according to the principle that each node is in the same community as its most similar neighbor. After that, check each partition whether it satisfies community criterion. For the pre-partitions who do not satisfy, incorporate them with others that having the biggest attraction until there are no changes. To measure the attraction ability of a partition, we propose an attraction index that based on the linked node's importance in networks. Therefore, our proposed method can better exploit the nodes' properties and network's structure. To test the performance of our algorithm, both synthetic and empirical networks ranging in different scales are tested. Simulation results show that the proposed algorithm can obtain superior clustering results compared with six other widely used community detection algorithms.

Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm

International Nuclear Information System (INIS)

Rao, R. Venkata; Rai, Dhiraj P.

2017-01-01

Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO).

Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm

Energy Technology Data Exchange (ETDEWEB)

Rao, R. Venkata; Rai, Dhiraj P. [Sardar Vallabhbhai National Institute of Technology, Gujarat (India)

2017-05-15

Submerged arc welding (SAW) is characterized as a multi-input process. Selection of optimum combination of process parameters of SAW process is a vital task in order to achieve high quality of weld and productivity. The objective of this work is to optimize the SAW process parameters using a simple optimization algorithm, which is fast, robust and convenient. Therefore, in this work a very recently proposed optimization algorithm named Jaya algorithm is applied to solve the optimization problems in SAW process. In addition, a modified version of Jaya algorithm with oppositional based learning, named “Quasi-oppositional based Jaya algorithm” (QO-Jaya) is proposed in order to improve the performance of the Jaya algorithm. Three optimization case studies are considered and the results obtained by Jaya algorithm and QO-Jaya algorithm are compared with the results obtained by well-known optimization algorithms such as Genetic algorithm (GA), Particle swarm optimization (PSO), Imperialist competitive algorithm (ICA) and Teaching learning based optimization (TLBO).

Evaluation of the performance of existing non-laboratory based cardiovascular risk assessment algorithms

Science.gov (United States)

2013-01-01

Background The high burden and rising incidence of cardiovascular disease (CVD) in resource constrained countries necessitates implementation of robust and pragmatic primary and secondary prevention strategies. Many current CVD management guidelines recommend absolute cardiovascular (CV) risk assessment as a clinically sound guide to preventive and treatment strategies. Development of non-laboratory based cardiovascular risk assessment algorithms enable absolute risk assessment in resource constrained countries. The objective of this review is to evaluate the performance of existing non-laboratory based CV risk assessment algorithms using the benchmarks for clinically useful CV risk assessment algorithms outlined by Cooney and colleagues. Methods A literature search to identify non-laboratory based risk prediction algorithms was performed in MEDLINE, CINAHL, Ovid Premier Nursing Journals Plus, and PubMed databases. The identified algorithms were evaluated using the benchmarks for clinically useful cardiovascular risk assessment algorithms outlined by Cooney and colleagues. Results Five non-laboratory based CV risk assessment algorithms were identified. The Gaziano and Framingham algorithms met the criteria for appropriateness of statistical methods used to derive the algorithms and endpoints. The Swedish Consultation, Framingham and Gaziano algorithms demonstrated good discrimination in derivation datasets. Only the Gaziano algorithm was externally validated where it had optimal discrimination. The Gaziano and WHO algorithms had chart formats which made them simple and user friendly for clinical application. Conclusion Both the Gaziano and Framingham non-laboratory based algorithms met most of the criteria outlined by Cooney and colleagues. External validation of the algorithms in diverse samples is needed to ascertain their performance and applicability to different populations and to enhance clinicians’ confidence in them. PMID:24373202

Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

Science.gov (United States)

Baraldi, P.; Bonfanti, G.; Zio, E.

2018-03-01

The identification of the current degradation state of an industrial component and the prediction of its future evolution is a fundamental step for the development of condition-based and predictive maintenance approaches. The objective of the present work is to propose a general method for extracting a health indicator to measure the amount of component degradation from a set of signals measured during operation. The proposed method is based on the combined use of feature extraction techniques, such as Empirical Mode Decomposition and Auto-Associative Kernel Regression, and a multi-objective Binary Differential Evolution (BDE) algorithm for selecting the subset of features optimal for the definition of the health indicator. The objectives of the optimization are desired characteristics of the health indicator, such as monotonicity, trendability and prognosability. A case study is considered, concerning the prediction of the remaining useful life of turbofan engines. The obtained results confirm that the method is capable of extracting health indicators suitable for accurate prognostics.

Teaching AI Search Algorithms in a Web-Based Educational System

Science.gov (United States)

Grivokostopoulou, Foteini; Hatzilygeroudis, Ioannis

2013-01-01

In this paper, we present a way of teaching AI search algorithms in a web-based adaptive educational system. Teaching is based on interactive examples and exercises. Interactive examples, which use visualized animations to present AI search algorithms in a step-by-step way with explanations, are used to make learning more attractive. Practice…

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

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Ye Jin

2018-04-01

Full Text Available The Artificial Bee Colony (ABC algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

A Greedy Algorithm for Neighborhood Overlap-Based Community Detection

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Natarajan Meghanathan

2016-01-01

Full Text Available The neighborhood overlap (NOVER of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or v. In this paper, we hypothesize that an edge u-v with a lower NOVER score bridges two or more sets of vertices, with very few edges (other than u-v connecting vertices from one set to another set. Accordingly, we propose a greedy algorithm of iteratively removing the edges of a network in the increasing order of their neighborhood overlap and calculating the modularity score of the resulting network component(s after the removal of each edge. The network component(s that have the largest cumulative modularity score are identified as the different communities of the network. We evaluate the performance of the proposed NOVER-based community detection algorithm on nine real-world network graphs and compare the performance against the multi-level aggregation-based Louvain algorithm, as well as the original and time-efficient versions of the edge betweenness-based Girvan-Newman (GN community detection algorithm.

Parallel Directionally Split Solver Based on Reformulation of Pipelined Thomas Algorithm

Science.gov (United States)

Povitsky, A.

1998-01-01

In this research an efficient parallel algorithm for 3-D directionally split problems is developed. The proposed algorithm is based on a reformulated version of the pipelined Thomas algorithm that starts the backward step computations immediately after the completion of the forward step computations for the first portion of lines This algorithm has data available for other computational tasks while processors are idle from the Thomas algorithm. The proposed 3-D directionally split solver is based on the static scheduling of processors where local and non-local, data-dependent and data-independent computations are scheduled while processors are idle. A theoretical model of parallelization efficiency is used to define optimal parameters of the algorithm, to show an asymptotic parallelization penalty and to obtain an optimal cover of a global domain with subdomains. It is shown by computational experiments and by the theoretical model that the proposed algorithm reduces the parallelization penalty about two times over the basic algorithm for the range of the number of processors (subdomains) considered and the number of grid nodes per subdomain.

A Hybrid Differential Evolution and Tree Search Algorithm for the Job Shop Scheduling Problem

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Rui Zhang

2011-01-01

Full Text Available The job shop scheduling problem (JSSP is a notoriously difficult problem in combinatorial optimization. In terms of the objective function, most existing research has been focused on the makespan criterion. However, in contemporary manufacturing systems, due-date-related performances are more important because they are essential for maintaining a high service reputation. Therefore, in this study we aim at minimizing the total weighted tardiness in JSSP. Considering the high complexity, a hybrid differential evolution (DE algorithm is proposed for the problem. To enhance the overall search efficiency, a neighborhood property of the problem is discovered, and then a tree search procedure is designed and embedded into the DE framework. According to the extensive computational experiments, the proposed approach is efficient in solving the job shop scheduling problem with total weighted tardiness objective.

Tolerance based algorithms for the ATSP

NARCIS (Netherlands)

Goldengorin, B; Sierksma, G; Turkensteen, M; Hromkovic, J; Nagl, M; Westfechtel, B

2004-01-01

In this paper we use arc tolerances, instead of arc costs, to improve Branch-and-Bound type algorithms for the Asymmetric Traveling Salesman Problem (ATSP). We derive new tighter lower bounds based on exact and approximate bottleneck upper tolerance values of the Assignment Problem (AP). It is shown

Analysis of energy-based algorithms for RNA secondary structure prediction

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Hajiaghayi Monir

2012-02-01

Full Text Available Abstract Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA or pseudo-expected accuracy (pseudo-MEA methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-MEA-based methods, with respect to the latest datasets and energy parameters. Results We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms

Key Distribution and Changing Key Cryptosystem Based on Phase Retrieval Algorithm and RSA Public-Key Algorithm

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Tieyu Zhao

2015-01-01

Full Text Available The optical image encryption has attracted more and more researchers’ attention, and the various encryption schemes have been proposed. In existing optical cryptosystem, the phase functions or images are usually used as the encryption keys, and it is difficult that the traditional public-key algorithm (such as RSA, ECC, etc. is used to complete large numerical key transfer. In this paper, we propose a key distribution scheme based on the phase retrieval algorithm and the RSA public-key algorithm, which solves the problem for the key distribution in optical image encryption system. Furthermore, we also propose a novel image encryption system based on the key distribution principle. In the system, the different keys can be used in every encryption process, which greatly improves the security of the system.

Minimum Probability of Error-Based Equalization Algorithms for Fading Channels

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Janos Levendovszky

2007-06-01

Full Text Available Novel channel equalizer algorithms are introduced for wireless communication systems to combat channel distortions resulting from multipath propagation. The novel algorithms are based on newly derived bounds on the probability of error (PE and guarantee better performance than the traditional zero forcing (ZF or minimum mean square error (MMSE algorithms. The new equalization methods require channel state information which is obtained by a fast adaptive channel identification algorithm. As a result, the combined convergence time needed for channel identification and PE minimization still remains smaller than the convergence time of traditional adaptive algorithms, yielding real-time equalization. The performance of the new algorithms is tested by extensive simulations on standard mobile channels.

An improved recommended algorithm for network structure based on two partial graphs

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Deng Song

2017-08-01

Full Text Available In this thesis,we introduce an improved algorithm based on network structure.Based on the standard material diffusion algorithm,considering the influence of the user's score on the recommendation,the adjustment factor of the initial resource allocation vector and the resource transfer matrix in the recommendation algorithm is improved.Using the practical data set from GroupLens webite to evaluate the performance of the proposed algorithm,we performed a series of experiments.The experimental results reveal that it can yield better recommendation accuracy and has higher hitting rate than collaborative filtering,network-based inference.It can solve the problem of cold start and scalability in the standard material diffusion algorithm.And it also can make the recommendation results diversified.

Multirate-based fast parallel algorithms for 2-D DHT-based real-valued discrete Gabor transform.

Science.gov (United States)

Tao, Liang; Kwan, Hon Keung

2012-07-01

Novel algorithms for the multirate and fast parallel implementation of the 2-D discrete Hartley transform (DHT)-based real-valued discrete Gabor transform (RDGT) and its inverse transform are presented in this paper. A 2-D multirate-based analysis convolver bank is designed for the 2-D RDGT, and a 2-D multirate-based synthesis convolver bank is designed for the 2-D inverse RDGT. The parallel channels in each of the two convolver banks have a unified structure and can apply the 2-D fast DHT algorithm to speed up their computations. The computational complexity of each parallel channel is low and is independent of the Gabor oversampling rate. All the 2-D RDGT coefficients of an image are computed in parallel during the analysis process and can be reconstructed in parallel during the synthesis process. The computational complexity and time of the proposed parallel algorithms are analyzed and compared with those of the existing fastest algorithms for 2-D discrete Gabor transforms. The results indicate that the proposed algorithms are the fastest, which make them attractive for real-time image processing.

SINGLE VERSUS MULTIPLE TRIAL VECTORS IN CLASSICAL DIFFERENTIAL EVOLUTION FOR OPTIMIZING THE QUANTIZATION TABLE IN JPEG BASELINE ALGORITHM

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B Vinoth Kumar

2017-07-01

Full Text Available Quantization Table is responsible for compression / quality trade-off in baseline Joint Photographic Experts Group (JPEG algorithm and therefore it is viewed as an optimization problem. In the literature, it has been found that Classical Differential Evolution (CDE is a promising algorithm to generate the optimal quantization table. However, the searching capability of CDE could be limited due to generation of single trial vector in an iteration which in turn reduces the convergence speed. This paper studies the performance of CDE by employing multiple trial vectors in a single iteration. An extensive performance analysis has been made between CDE and CDE with multiple trial vectors in terms of Optimization process, accuracy, convergence speed and reliability. The analysis report reveals that CDE with multiple trial vectors improves the convergence speed of CDE and the same is confirmed using a statistical hypothesis test (t-test.

Collaborative filtering recommendation model based on fuzzy clustering algorithm

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Yang, Ye; Zhang, Yunhua

2018-05-01

As one of the most widely used algorithms in recommender systems, collaborative filtering algorithm faces two serious problems, which are the sparsity of data and poor recommendation effect in big data environment. In traditional clustering analysis, the object is strictly divided into several classes and the boundary of this division is very clear. However, for most objects in real life, there is no strict definition of their forms and attributes of their class. Concerning the problems above, this paper proposes to improve the traditional collaborative filtering model through the hybrid optimization of implicit semantic algorithm and fuzzy clustering algorithm, meanwhile, cooperating with collaborative filtering algorithm. In this paper, the fuzzy clustering algorithm is introduced to fuzzy clustering the information of project attribute, which makes the project belong to different project categories with different membership degrees, and increases the density of data, effectively reduces the sparsity of data, and solves the problem of low accuracy which is resulted from the inaccuracy of similarity calculation. Finally, this paper carries out empirical analysis on the MovieLens dataset, and compares it with the traditional user-based collaborative filtering algorithm. The proposed algorithm has greatly improved the recommendation accuracy.

Relationship research between meteorological disasters and stock markets based on a multifractal detrending moving average algorithm

Science.gov (United States)

Li, Qingchen; Cao, Guangxi; Xu, Wei

2018-01-01

Based on a multifractal detrending moving average algorithm (MFDMA), this study uses the fractionally autoregressive integrated moving average process (ARFIMA) to demonstrate the effectiveness of MFDMA in the detection of auto-correlation at different sample lengths and to simulate some artificial time series with the same length as the actual sample interval. We analyze the effect of predictable and unpredictable meteorological disasters on the US and Chinese stock markets and the degree of long memory in different sectors. Furthermore, we conduct a preliminary investigation to determine whether the fluctuations of financial markets caused by meteorological disasters are derived from the normal evolution of the financial system itself or not. We also propose several reasonable recommendations.

Evolution of semilocal string networks. II. Velocity estimators

Science.gov (United States)

Lopez-Eiguren, A.; Urrestilla, J.; Achúcarro, A.; Avgoustidis, A.; Martins, C. J. A. P.

2017-07-01

We continue a comprehensive numerical study of semilocal string networks and their cosmological evolution. These can be thought of as hybrid networks comprised of (nontopological) string segments, whose core structure is similar to that of Abelian Higgs vortices, and whose ends have long-range interactions and behavior similar to that of global monopoles. Our study provides further evidence of a linear scaling regime, already reported in previous studies, for the typical length scale and velocity of the network. We introduce a new algorithm to identify the position of the segment cores. This allows us to determine the length and velocity of each individual segment and follow their evolution in time. We study the statistical distribution of segment lengths and velocities for radiation- and matter-dominated evolution in the regime where the strings are stable. Our segment detection algorithm gives higher length values than previous studies based on indirect detection methods. The statistical distribution shows no evidence of (anti)correlation between the speed and the length of the segments.

Learning-based meta-algorithm for MRI brain extraction.

Science.gov (United States)

Shi, Feng; Wang, Li; Gilmore, John H; Lin, Weili; Shen, Dinggang

2011-01-01

Multiple-segmentation-and-fusion method has been widely used for brain extraction, tissue segmentation, and region of interest (ROI) localization. However, such studies are hindered in practice by their computational complexity, mainly coming from the steps of template selection and template-to-subject nonlinear registration. In this study, we address these two issues and propose a novel learning-based meta-algorithm for MRI brain extraction. Specifically, we first use exemplars to represent the entire template library, and assign the most similar exemplar to the test subject. Second, a meta-algorithm combining two existing brain extraction algorithms (BET and BSE) is proposed to conduct multiple extractions directly on test subject. Effective parameter settings for the meta-algorithm are learned from the training data and propagated to subject through exemplars. We further develop a level-set based fusion method to combine multiple candidate extractions together with a closed smooth surface, for obtaining the final result. Experimental results show that, with only a small portion of subjects for training, the proposed method is able to produce more accurate and robust brain extraction results, at Jaccard Index of 0.956 +/- 0.010 on total 340 subjects under 6-fold cross validation, compared to those by the BET and BSE even using their best parameter combinations.

a Voxel-Based Filtering Algorithm for Mobile LIDAR Data

Science.gov (United States)

Qin, H.; Guan, G.; Yu, Y.; Zhong, L.

2018-04-01

This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

Science.gov (United States)

Liao, Qin; Guo, Ying; Tu, Yifeng; Zhang, Hang

2018-03-01

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Implementation of software-based sensor linearization algorithms on low-cost microcontrollers.

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Erdem, Hamit

2010-10-01

Nonlinear sensors and microcontrollers are used in many embedded system designs. As the input-output characteristic of most sensors is nonlinear in nature, obtaining data from a nonlinear sensor by using an integer microcontroller has always been a design challenge. This paper discusses the implementation of six software-based sensor linearization algorithms for low-cost microcontrollers. The comparative study of the linearization algorithms is performed by using a nonlinear optical distance-measuring sensor. The performance of the algorithms is examined with respect to memory space usage, linearization accuracy and algorithm execution time. The implementation and comparison results can be used for selection of a linearization algorithm based on the sensor transfer function, expected linearization accuracy and microcontroller capacity. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Unsupervised classification of multivariate geostatistical data: Two algorithms

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Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

Development of Base Transceiver Station Selection Algorithm for ...

African Journals Online (AJOL)

TEMS) equipment was carried out on the existing BTSs, and a linear algorithm optimization program based on the spectral link efficiency of each BTS was developed, the output of this site optimization gives the selected number of base station sites ...

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

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Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

2017-01-01

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation

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Guomin Li

2016-12-01

Full Text Available An improved pilot pattern algorithm for facilitating the channel estimation in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM systems is proposed in this paper. The presented algorithm reconfigures the parameter in the least square (LS algorithm, which belongs to the space-time block-coded (STBC category for channel estimation in pilot-based MIMO-OFDM system. Simulation results show that the algorithm has better performance in contrast to the classical single symbol scheme. In contrast to the double symbols scheme, the proposed algorithm can achieve nearly the same performance with only half of the complexity of the double symbols scheme.

Statistics-based optimization of the polarimetric radar hydrometeor classification algorithm and its application for a squall line in South China

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Wu, Chong; Liu, Liping; Wei, Ming; Xi, Baozhu; Yu, Minghui

2018-03-01

A modified hydrometeor classification algorithm (HCA) is developed in this study for Chinese polarimetric radars. This algorithm is based on the U.S. operational HCA. Meanwhile, the methodology of statistics-based optimization is proposed including calibration checking, datasets selection, membership functions modification, computation thresholds modification, and effect verification. Zhuhai radar, the first operational polarimetric radar in South China, applies these procedures. The systematic bias of calibration is corrected, the reliability of radar measurements deteriorates when the signal-to-noise ratio is low, and correlation coefficient within the melting layer is usually lower than that of the U.S. WSR-88D radar. Through modification based on statistical analysis of polarimetric variables, the localized HCA especially for Zhuhai is obtained, and it performs well over a one-month test through comparison with sounding and surface observations. The algorithm is then utilized for analysis of a squall line process on 11 May 2014 and is found to provide reasonable details with respect to horizontal and vertical structures, and the HCA results—especially in the mixed rain-hail region—can reflect the life cycle of the squall line. In addition, the kinematic and microphysical processes of cloud evolution and the differences between radar-detected hail and surface observations are also analyzed. The results of this study provide evidence for the improvement of this HCA developed specifically for China.

Koordinasi Optimal Capacitive Energy Storage (CES dan Kontroler PID Menggunakan Differential Evolution Algorithm (DEA pada Sistem Tenaga Listrik

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Akbar Swandaru

2012-09-01

Full Text Available Peningkatan suplai daya listrik diperlukan untuk memenuhi kebutuhan daya listrik. Generator cenderung beroperasi dalam beban penuh.Hal ini berpengaruh pada keamanan generator dalam operasi sistem tenaga listrik.Salah satu masalah adalah osilasi frekuensi.Bila perubahan beban terjadi, kontroler diperlukan untuk meredam osilasi frekuensi ini.Pada tugas akhir ini diusulkan sebuah koordinasi antara Kontroler Capacitive Energy Storage (CES dan Kontroler PID. CES disini berfungsi untuk membantu kinerja Governor agar meredam osilasi frekuensi dengan cepat. Kontroler CES ini digunakan bersama dengan PID controller yang dioptimalkan dengan  Differential Evolution Algorithm (DEA.

Algorithm for Wireless Sensor Networks Based on Grid Management

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Geng Zhang

2014-05-01

Full Text Available This paper analyzes the key issues for wireless sensor network trust model and describes a method to build a wireless sensor network, such as the definition of trust for wireless sensor networks, computing and credibility of trust model application. And for the problem that nodes are vulnerable to attack, this paper proposed a grid-based trust algorithm by deep exploration trust model within the framework of credit management. Algorithm for node reliability screening and rotation schedule to cover parallel manner based on the implementation of the nodes within the area covered by trust. And analyze the results of the size of trust threshold has great influence on the safety and quality of coverage throughout the coverage area. The simulation tests the validity and correctness of the algorithm.

Multi-robot task allocation based on two dimensional artificial fish swarm algorithm

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Zheng, Taixiong; Li, Xueqin; Yang, Liangyi

2007-12-01

The problem of task allocation for multiple robots is to allocate more relative-tasks to less relative-robots so as to minimize the processing time of these tasks. In order to get optimal multi-robot task allocation scheme, a twodimensional artificial swarm algorithm based approach is proposed in this paper. In this approach, the normal artificial fish is extended to be two dimension artificial fish. In the two dimension artificial fish, each vector of primary artificial fish is extended to be an m-dimensional vector. Thus, each vector can express a group of tasks. By redefining the distance between artificial fish and the center of artificial fish, the behavior of two dimension fish is designed and the task allocation algorithm based on two dimension artificial swarm algorithm is put forward. At last, the proposed algorithm is applied to the problem of multi-robot task allocation and comparer with GA and SA based algorithm is done. Simulation and compare result shows the proposed algorithm is effective.

A novel image encryption algorithm based on a 3D chaotic map

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Kanso, A.; Ghebleh, M.

2012-07-01

Recently [Solak E, Çokal C, Yildiz OT Biyikoǧlu T. Cryptanalysis of Fridrich's chaotic image encryption. Int J Bifur Chaos 2010;20:1405-1413] cryptanalyzed the chaotic image encryption algorithm of [Fridrich J. Symmetric ciphers based on two-dimensional chaotic maps. Int J Bifur Chaos 1998;8(6):1259-1284], which was considered a benchmark for measuring security of many image encryption algorithms. This attack can also be applied to other encryption algorithms that have a structure similar to Fridrich's algorithm, such as that of [Chen G, Mao Y, Chui, C. A symmetric image encryption scheme based on 3D chaotic cat maps. Chaos Soliton Fract 2004;21:749-761]. In this paper, we suggest a novel image encryption algorithm based on a three dimensional (3D) chaotic map that can defeat the aforementioned attack among other existing attacks. The design of the proposed algorithm is simple and efficient, and based on three phases which provide the necessary properties for a secure image encryption algorithm including the confusion and diffusion properties. In phase I, the image pixels are shuffled according to a search rule based on the 3D chaotic map. In phases II and III, 3D chaotic maps are used to scramble shuffled pixels through mixing and masking rules, respectively. Simulation results show that the suggested algorithm satisfies the required performance tests such as high level security, large key space and acceptable encryption speed. These characteristics make it a suitable candidate for use in cryptographic applications.

Temporal high-pass non-uniformity correction algorithm based on grayscale mapping and hardware implementation

Science.gov (United States)

Jin, Minglei; Jin, Weiqi; Li, Yiyang; Li, Shuo

2015-08-01

In this paper, we propose a novel scene-based non-uniformity correction algorithm for infrared image processing-temporal high-pass non-uniformity correction algorithm based on grayscale mapping (THP and GM). The main sources of non-uniformity are: (1) detector fabrication inaccuracies; (2) non-linearity and variations in the read-out electronics and (3) optical path effects. The non-uniformity will be reduced by non-uniformity correction (NUC) algorithms. The NUC algorithms are often divided into calibration-based non-uniformity correction (CBNUC) algorithms and scene-based non-uniformity correction (SBNUC) algorithms. As non-uniformity drifts temporally, CBNUC algorithms must be repeated by inserting a uniform radiation source which SBNUC algorithms do not need into the view, so the SBNUC algorithm becomes an essential part of infrared imaging system. The SBNUC algorithms' poor robustness often leads two defects: artifacts and over-correction, meanwhile due to complicated calculation process and large storage consumption, hardware implementation of the SBNUC algorithms is difficult, especially in Field Programmable Gate Array (FPGA) platform. The THP and GM algorithm proposed in this paper can eliminate the non-uniformity without causing defects. The hardware implementation of the algorithm only based on FPGA has two advantages: (1) low resources consumption, and (2) small hardware delay: less than 20 lines, it can be transplanted to a variety of infrared detectors equipped with FPGA image processing module, it can reduce the stripe non-uniformity and the ripple non-uniformity.

Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT

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Cunsuo Pang

2016-09-01

Full Text Available This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT’s performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated pulse radar, SAR (Synthetic aperture radar, or ISAR (Inverse synthetic aperture radar, for improving the probability of target recognition.

Micro-Doppler Signal Time-Frequency Algorithm Based on STFRFT.

Science.gov (United States)

Pang, Cunsuo; Han, Yan; Hou, Huiling; Liu, Shengheng; Zhang, Nan

2016-09-24

This paper proposes a time-frequency algorithm based on short-time fractional order Fourier transformation (STFRFT) for identification of a complicated movement targets. This algorithm, consisting of a STFRFT order-changing and quick selection method, is effective in reducing the computation load. A multi-order STFRFT time-frequency algorithm is also developed that makes use of the time-frequency feature of each micro-Doppler component signal. This algorithm improves the estimation accuracy of time-frequency curve fitting through multi-order matching. Finally, experiment data were used to demonstrate STFRFT's performance in micro-Doppler time-frequency analysis. The results validated the higher estimate accuracy of the proposed algorithm. It may be applied to an LFM (Linear frequency modulated) pulse radar, SAR (Synthetic aperture radar), or ISAR (Inverse synthetic aperture radar), for improving the probability of target recognition.

Balancing Inverted Pendulum by Angle Sensing Using Fuzzy Logic Supervised PID Controller Optimized by Genetic Algorithm

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Ashutosh K. AGARWAL

2011-10-01

Full Text Available Genetic algorithms are robust search techniques based on the principles of evolution. A genetic algorithm maintains a population of encoded solutions and guides the population towards the optimum solution. This important property of genetic algorithm is used in this paper to stabilize the Inverted pendulum system. This paper highlights the application and stability of inverted pendulum using PID controller with fuzzy logic genetic algorithm supervisor . There are a large number of well established search techniques in use within the information technology industry. We propose a method to control inverted pendulum steady state error and overshoot using genetic algorithm technique.

A Superresolution Image Reconstruction Algorithm Based on Landweber in Electrical Capacitance Tomography

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Chen Deyun

2013-01-01

Full Text Available According to the image reconstruction accuracy influenced by the “soft field” nature and ill-conditioned problems in electrical capacitance tomography, a superresolution image reconstruction algorithm based on Landweber is proposed in the paper, which is based on the working principle of the electrical capacitance tomography system. The method uses the algorithm which is derived by regularization of solutions derived and derives closed solution by fast Fourier transform of the convolution kernel. So, it ensures the certainty of the solution and improves the stability and quality of image reconstruction results. Simulation results show that the imaging precision and real-time imaging of the algorithm are better than Landweber algorithm, and this algorithm proposes a new method for the electrical capacitance tomography image reconstruction algorithm.

K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

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Cheng Lu

2015-01-01

Full Text Available The Affinity Propagation (AP algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based on K-nearest neighbor intervals (KNNI for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm

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Lei Chen

2014-01-01

Full Text Available The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

A Cancer Gene Selection Algorithm Based on the K-S Test and CFS

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Qiang Su

2017-01-01

Full Text Available Background. To address the challenging problem of selecting distinguished genes from cancer gene expression datasets, this paper presents a gene subset selection algorithm based on the Kolmogorov-Smirnov (K-S test and correlation-based feature selection (CFS principles. The algorithm selects distinguished genes first using the K-S test, and then, it uses CFS to select genes from those selected by the K-S test. Results. We adopted support vector machines (SVM as the classification tool and used the criteria of accuracy to evaluate the performance of the classifiers on the selected gene subsets. This approach compared the proposed gene subset selection algorithm with the K-S test, CFS, minimum-redundancy maximum-relevancy (mRMR, and ReliefF algorithms. The average experimental results of the aforementioned gene selection algorithms for 5 gene expression datasets demonstrate that, based on accuracy, the performance of the new K-S and CFS-based algorithm is better than those of the K-S test, CFS, mRMR, and ReliefF algorithms. Conclusions. The experimental results show that the K-S test-CFS gene selection algorithm is a very effective and promising approach compared to the K-S test, CFS, mRMR, and ReliefF algorithms.

Genetic algorithm-based improved DOA estimation using fourth-order cumulants

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Ahmed, Ammar; Tufail, Muhammad

2017-05-01

Genetic algorithm (GA)-based direction of arrival (DOA) estimation is proposed using fourth-order cumulants (FOC) and ESPRIT principle which results in Multiple Invariance Cumulant ESPRIT algorithm. In the existing FOC ESPRIT formulations, only one invariance is utilised to estimate DOAs. The unused multiple invariances (MIs) must be exploited simultaneously in order to improve the estimation accuracy. In this paper, a fitness function based on a carefully designed cumulant matrix is developed which incorporates MIs present in the sensor array. Better DOA estimation can be achieved by minimising this fitness function. Moreover, the effectiveness of Newton's method as well as GA for this optimisation problem has been illustrated. Simulation results show that the proposed algorithm provides improved estimation accuracy compared to existing algorithms, especially in the case of low SNR, less number of snapshots, closely spaced sources and high signal and noise correlation. Moreover, it is observed that the optimisation using Newton's method is more likely to converge to false local optima resulting in erroneous results. However, GA-based optimisation has been found attractive due to its global optimisation capability.

A fast, parallel algorithm to solve the basic fluvial erosion/transport equations

Science.gov (United States)

Braun, J.

2012-04-01

Quantitative models of landform evolution are commonly based on the solution of a set of equations representing the processes of fluvial erosion, transport and deposition, which leads to predict the geometry of a river channel network and its evolution through time. The river network is often regarded as the backbone of any surface processes model (SPM) that might include other physical processes acting at a range of spatial and temporal scales along hill slopes. The basic laws of fluvial erosion requires the computation of local (slope) and non-local (drainage area) quantities at every point of a given landscape, a computationally expensive operation which limits the resolution of most SPMs. I present here an algorithm to compute the various components required in the parameterization of fluvial erosion (and transport) and thus solve the basic fluvial geomorphic equation, that is very efficient because it is O(n) (the number of required arithmetic operations is linearly proportional to the number of nodes defining the landscape), and is fully parallelizable (the computation cost decreases in a direct inverse proportion to the number of processors used to solve the problem). The algorithm is ideally suited for use on latest multi-core processors. Using this new technique, geomorphic problems can be solved at an unprecedented resolution (typically of the order of 10,000 X 10,000 nodes) while keeping the computational cost reasonable (order 1 sec per time step). Furthermore, I will show that the algorithm is applicable to any regular or irregular representation of the landform, and is such that the temporal evolution of the landform can be discretized by a fully implicit time-marching algorithm, making it unconditionally stable. I will demonstrate that such an efficient algorithm is ideally suited to produce a fully predictive SPM that links observationally based parameterizations of small-scale processes to the evolution of large-scale features of the landscapes on

A grammar-based semantic similarity algorithm for natural language sentences.

Science.gov (United States)

Lee, Ming Che; Chang, Jia Wei; Hsieh, Tung Cheng

2014-01-01

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to "artificial language", such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.

A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning

Science.gov (United States)

Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei

2013-03-01

In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.

High-order hydrodynamic algorithms for exascale computing

Energy Technology Data Exchange (ETDEWEB)

Morgan, Nathaniel Ray [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2016-02-05

Hydrodynamic algorithms are at the core of many laboratory missions ranging from simulating ICF implosions to climate modeling. The hydrodynamic algorithms commonly employed at the laboratory and in industry (1) typically lack requisite accuracy for complex multi- material vortical flows and (2) are not well suited for exascale computing due to poor data locality and poor FLOP/memory ratios. Exascale computing requires advances in both computer science and numerical algorithms. We propose to research the second requirement and create a new high-order hydrodynamic algorithm that has superior accuracy, excellent data locality, and excellent FLOP/memory ratios. This proposal will impact a broad range of research areas including numerical theory, discrete mathematics, vorticity evolution, gas dynamics, interface instability evolution, turbulent flows, fluid dynamics and shock driven flows. If successful, the proposed research has the potential to radically transform simulation capabilities and help position the laboratory for computing at the exascale.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems.

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-22

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K -center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

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Shuqiang Huang

2017-01-01

Full Text Available Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest and the population optimum (gbest; thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms.

Competitive Swarm Optimizer Based Gateway Deployment Algorithm in Cyber-Physical Systems

Science.gov (United States)

Huang, Shuqiang; Tao, Ming

2017-01-01

Wireless sensor network topology optimization is a highly important issue, and topology control through node selection can improve the efficiency of data forwarding, while saving energy and prolonging lifetime of the network. To address the problem of connecting a wireless sensor network to the Internet in cyber-physical systems, here we propose a geometric gateway deployment based on a competitive swarm optimizer algorithm. The particle swarm optimization (PSO) algorithm has a continuous search feature in the solution space, which makes it suitable for finding the geometric center of gateway deployment; however, its search mechanism is limited to the individual optimum (pbest) and the population optimum (gbest); thus, it easily falls into local optima. In order to improve the particle search mechanism and enhance the search efficiency of the algorithm, we introduce a new competitive swarm optimizer (CSO) algorithm. The CSO search algorithm is based on an inter-particle competition mechanism and can effectively avoid trapping of the population falling into a local optimum. With the improvement of an adaptive opposition-based search and its ability to dynamically parameter adjustments, this algorithm can maintain the diversity of the entire swarm to solve geometric K-center gateway deployment problems. The simulation results show that this CSO algorithm has a good global explorative ability as well as convergence speed and can improve the network quality of service (QoS) level of cyber-physical systems by obtaining a minimum network coverage radius. We also find that the CSO algorithm is more stable, robust and effective in solving the problem of geometric gateway deployment as compared to the PSO or Kmedoids algorithms. PMID:28117735

Shockwave-Based Automated Vehicle Longitudinal Control Algorithm for Nonrecurrent Congestion Mitigation

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Liuhui Zhao

2017-01-01

Full Text Available A shockwave-based speed harmonization algorithm for the longitudinal movement of automated vehicles is presented in this paper. In the advent of Connected/Automated Vehicle (C/AV environment, the proposed algorithm can be applied to capture instantaneous shockwaves constructed from vehicular speed profiles shared by individual equipped vehicles. With a continuous wavelet transform (CWT method, the algorithm detects abnormal speed drops in real-time and optimizes speed to prevent the shockwave propagating to the upstream traffic. A traffic simulation model is calibrated to evaluate the applicability and efficiency of the proposed algorithm. Based on 100% C/AV market penetration, the simulation results show that the CWT-based algorithm accurately detects abnormal speed drops. With the improved accuracy of abnormal speed drop detection, the simulation results also demonstrate that the congestion can be mitigated by reducing travel time and delay up to approximately 9% and 18%, respectively. It is also found that the shockwave caused by nonrecurrent congestion is quickly dissipated even with low market penetration.

Quantum walks and search algorithms

CERN Document Server

Portugal, Renato

2013-01-01

This book addresses an interesting area of quantum computation called quantum walks, which play an important role in building quantum algorithms, in particular search algorithms. Quantum walks are the quantum analogue of classical random walks. It is known that quantum computers have great power for searching unsorted databases. This power extends to many kinds of searches, particularly to the problem of finding a specific location in a spatial layout, which can be modeled by a graph. The goal is to find a specific node knowing that the particle uses the edges to jump from one node to the next. This book is self-contained with main topics that include: Grover's algorithm, describing its geometrical interpretation and evolution by means of the spectral decomposition of the evolution operater Analytical solutions of quantum walks on important graphs like line, cycles, two-dimensional lattices, and hypercubes using Fourier transforms Quantum walks on generic graphs, describing methods to calculate the limiting d...

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

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Zhehuang Huang

2015-01-01

Full Text Available Artificial fish swarm algorithm (AFSA is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Log-linear model based behavior selection method for artificial fish swarm algorithm.

Science.gov (United States)

Huang, Zhehuang; Chen, Yidong

2015-01-01

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

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Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Portfolio optimization by using linear programing models based on genetic algorithm

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Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

DNA-based watermarks using the DNA-Crypt algorithm

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Barnekow Angelika

2007-05-01

Full Text Available Abstract Background The aim of this paper is to demonstrate the application of watermarks based on DNA sequences to identify the unauthorized use of genetically modified organisms (GMOs protected by patents. Predicted mutations in the genome can be corrected by the DNA-Crypt program leaving the encrypted information intact. Existing DNA cryptographic and steganographic algorithms use synthetic DNA sequences to store binary information however, although these sequences can be used for authentication, they may change the target DNA sequence when introduced into living organisms. Results The DNA-Crypt algorithm and image steganography are based on the same watermark-hiding principle, namely using the least significant base in case of DNA-Crypt and the least significant bit in case of the image steganography. It can be combined with binary encryption algorithms like AES, RSA or Blowfish. DNA-Crypt is able to correct mutations in the target DNA with several mutation correction codes such as the Hamming-code or the WDH-code. Mutations which can occur infrequently may destroy the encrypted information, however an integrated fuzzy controller decides on a set of heuristics based on three input dimensions, and recommends whether or not to use a correction code. These three input dimensions are the length of the sequence, the individual mutation rate and the stability over time, which is represented by the number of generations. In silico experiments using the Ypt7 in Saccharomyces cerevisiae shows that the DNA watermarks produced by DNA-Crypt do not alter the translation of mRNA into protein. Conclusion The program is able to store watermarks in living organisms and can maintain the original information by correcting mutations itself. Pairwise or multiple sequence alignments show that DNA-Crypt produces few mismatches between the sequences similar to all steganographic algorithms.

DNA-based watermarks using the DNA-Crypt algorithm.

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Heider, Dominik; Barnekow, Angelika

2007-05-29

The aim of this paper is to demonstrate the application of watermarks based on DNA sequences to identify the unauthorized use of genetically modified organisms (GMOs) protected by patents. Predicted mutations in the genome can be corrected by the DNA-Crypt program leaving the encrypted information intact. Existing DNA cryptographic and steganographic algorithms use synthetic DNA sequences to store binary information however, although these sequences can be used for authentication, they may change the target DNA sequence when introduced into living organisms. The DNA-Crypt algorithm and image steganography are based on the same watermark-hiding principle, namely using the least significant base in case of DNA-Crypt and the least significant bit in case of the image steganography. It can be combined with binary encryption algorithms like AES, RSA or Blowfish. DNA-Crypt is able to correct mutations in the target DNA with several mutation correction codes such as the Hamming-code or the WDH-code. Mutations which can occur infrequently may destroy the encrypted information, however an integrated fuzzy controller decides on a set of heuristics based on three input dimensions, and recommends whether or not to use a correction code. These three input dimensions are the length of the sequence, the individual mutation rate and the stability over time, which is represented by the number of generations. In silico experiments using the Ypt7 in Saccharomyces cerevisiae shows that the DNA watermarks produced by DNA-Crypt do not alter the translation of mRNA into protein. The program is able to store watermarks in living organisms and can maintain the original information by correcting mutations itself. Pairwise or multiple sequence alignments show that DNA-Crypt produces few mismatches between the sequences similar to all steganographic algorithms.

DNA-based watermarks using the DNA-Crypt algorithm

Science.gov (United States)

Heider, Dominik; Barnekow, Angelika

2007-01-01

Background The aim of this paper is to demonstrate the application of watermarks based on DNA sequences to identify the unauthorized use of genetically modified organisms (GMOs) protected by patents. Predicted mutations in the genome can be corrected by the DNA-Crypt program leaving the encrypted information intact. Existing DNA cryptographic and steganographic algorithms use synthetic DNA sequences to store binary information however, although these sequences can be used for authentication, they may change the target DNA sequence when introduced into living organisms. Results The DNA-Crypt algorithm and image steganography are based on the same watermark-hiding principle, namely using the least significant base in case of DNA-Crypt and the least significant bit in case of the image steganography. It can be combined with binary encryption algorithms like AES, RSA or Blowfish. DNA-Crypt is able to correct mutations in the target DNA with several mutation correction codes such as the Hamming-code or the WDH-code. Mutations which can occur infrequently may destroy the encrypted information, however an integrated fuzzy controller decides on a set of heuristics based on three input dimensions, and recommends whether or not to use a correction code. These three input dimensions are the length of the sequence, the individual mutation rate and the stability over time, which is represented by the number of generations. In silico experiments using the Ypt7 in Saccharomyces cerevisiae shows that the DNA watermarks produced by DNA-Crypt do not alter the translation of mRNA into protein. Conclusion The program is able to store watermarks in living organisms and can maintain the original information by correcting mutations itself. Pairwise or multiple sequence alignments show that DNA-Crypt produces few mismatches between the sequences similar to all steganographic algorithms. PMID:17535434

Reactive power and voltage control based on general quantum genetic algorithms

DEFF Research Database (Denmark)

Vlachogiannis, Ioannis (John); Østergaard, Jacob

2009-01-01

This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determines...... techniques such as enhanced GA, multi-objective evolutionary algorithm and particle swarm optimization algorithms, as well as the classical primal-dual interior-point optimal power flow algorithm. The comparison demonstrates the ability of the GQ-GA in reaching more optimal solutions....

CAS algorithm-based optimum design of PID controller in AVR system

International Nuclear Information System (INIS)

Zhu Hui; Li Lixiang; Zhao Ying; Guo Yu; Yang Yixian

2009-01-01

This paper presents a novel design method for determining the optimal PID controller parameters of an automatic voltage regulator (AVR) system using the chaotic ant swarm (CAS) algorithm. In the tuning process of parameters, the CAS algorithm is iterated to give the optimal parameters of the PID controller based on the fitness theory, where the position vector of each ant in the CAS algorithm corresponds to the parameter vector of the PID controller. The proposed CAS-PID controllers can ensure better control system performance with respect to the reference input in comparison with GA-PID controllers. Numerical simulations are provided to verify the effectiveness and feasibility of PID controller based on CAS algorithm.

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

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Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

A Novel Entropy-Based Decoding Algorithm for a Generalized High-Order Discrete Hidden Markov Model

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Jason Chin-Tiong Chan

2018-01-01

Full Text Available The optimal state sequence of a generalized High-Order Hidden Markov Model (HHMM is tracked from a given observational sequence using the classical Viterbi algorithm. This classical algorithm is based on maximum likelihood criterion. We introduce an entropy-based Viterbi algorithm for tracking the optimal state sequence of a HHMM. The entropy of a state sequence is a useful quantity, providing a measure of the uncertainty of a HHMM. There will be no uncertainty if there is only one possible optimal state sequence for HHMM. This entropy-based decoding algorithm can be formulated in an extended or a reduction approach. We extend the entropy-based algorithm for computing the optimal state sequence that was developed from a first-order to a generalized HHMM with a single observational sequence. This extended algorithm performs the computation exponentially with respect to the order of HMM. The computational complexity of this extended algorithm is due to the growth of the model parameters. We introduce an efficient entropy-based decoding algorithm that used reduction approach, namely, entropy-based order-transformation forward algorithm (EOTFA to compute the optimal state sequence of any generalized HHMM. This EOTFA algorithm involves a transformation of a generalized high-order HMM into an equivalent first-order HMM and an entropy-based decoding algorithm is developed based on the equivalent first-order HMM. This algorithm performs the computation based on the observational sequence and it requires OTN~2 calculations, where N~ is the number of states in an equivalent first-order model and T is the length of observational sequence.

Fuzzy Sets-based Control Rules for Terminating Algorithms

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Jose L. VERDEGAY

2002-01-01

Full Text Available In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the questions on the management of the fuzziness in some optimisation models, and then of using fuzzy rules for terminating conventional algorithms are presented, discussed and analyzed. Finally, for the concrete case of the Travelling Salesman Problem, and as an illustration of determination, management and using the fuzzy rules, a new algorithm easy to implement in the Model-Base Management System of any oriented Decision Support System is shown.

The research of automatic speed control algorithm based on Green CBTC

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Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

2017-06-01

Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

Low-dose multiple-information retrieval algorithm for X-ray grating-based imaging

International Nuclear Information System (INIS)