For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.
Xuan, Xin; Chen, Jian; Zhen, Shanshan; Kuo, Yonghong
This paper proposes an optimal hops-based adaptive clustering algorithm (OHACA). The algorithm sets an energy selection threshold before the cluster forms so that the nodes with less energy are more likely to go to sleep immediately. In setup phase, OHACA introduces an adaptive mechanism to adjust cluster head and load balance. And the optimal distance theory is applied to discover the practical optimal routing path to minimize the total energy for transmission. Simulation results show that OHACA prolongs the life of network, improves utilizing rate and transmits more data because of energy balance.
ZHU Guangyu; ZHANG Weibo; DU Yuexiang
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
In this article, some methods are proposed for enhancing the converging velocity of the COA (chaos optimization algorithm) based on using carrier wave two times, which can greatly increase the speed and efficiency of the first carrier wave's search for the optimal point in implementing the sophisticated searching during the second carrier wave is faster and more accurate.In addition, the concept of using the carrier wave three times is proposed and put into practice to tackle the multi-variables optimization problems, where the searching for the optimal point of the last several variables is frequently worse than the first several ones.
Full Text Available Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA and Genetic Quantum Algorithm (GQA. The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.
Ying Sun; Hegen Xiong
Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded c...
吕宗伟; 林争辉; 张镭
DAG-MAP is an FPGA technology mapping algorithm for delay optimization and the labeling phase is the algorithm's kernel. This paper studied the labeling phase and presented an improved labeling method. It is shown through the experimental results on MCNC benchmarks that the improved method is more effective than the original method while the computation time is almost the same.
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
Full Text Available In order to enhance the convergence capability of the central force optimization (CFO algorithm, an adaptive central force optimization (ACFO algorithm is presented by introducing an adaptive weight and defining an adaptive gravitational constant. The adaptive weight and gravitational constant are selected based on the stability theory of discrete time-varying dynamic systems. The convergence capability of ACFO algorithm is compared with the other improved CFO algorithm and evolutionary-based algorithm using 23 unimodal and multimodal benchmark functions. Experiments results show that ACFO substantially enhances the performance of CFO in terms of global optimality and solution accuracy.
Full Text Available Paradigms based on competition have shown to be useful for solving difficult problems. In this paper we present a new approach for solving hard problems using a collaborative philosophy. A collaborative philosophy can produce paradigms as interesting as the ones found in algorithms based on a competitive philosophy. Furthermore, we show that the performance - in problems associated to explosive combinatorial - is comparable to the performance obtained using a classic evolutive approach.
Yue CHEN; Kai HU
As for the optimal design of steel portal frames, due to both the complexity of cross selections of beams and columns and the discreteness of design variables, it is difficult to obtain satisfactory results by traditional optimization. Based on a set of constraints of the Technical Specification for Light-weighted Steel Portal Frames of China, a genetic algorithm (GA) optimization program for portal frames, written in MATLAB code, was proposed in this paper. The graph user interface (GUI) is also developed for this optimal program, so that it can be used much more conveniently. Finally, some examples illustrate the effectiveness and efficiency of the genetic-algorithm-based optimal program.
Rao, R Venkata
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.
Ruan Youlin; Liu Gan; Zhu Guangxi; Lu Xiaofeng
When the communication time is relatively shorter than the computation time for every task, the task duplication based scheduling (TDS) algorithm proposed by Darbha and Agrawal generates an optimal schedule. Park and Choe also proposed an extended TDS algorithm whose optimality condition is less restricted than that of TDS algorithm, but the condition is very complex and is difficult to satisfy when the number of tasks is large. An efficient algorithm is proposed whose optimality condition is less restricted and simpler than both of the algorithms, and the schedule length is also shorter than both of the algorithms. The time complexity of the proposed algorithm is O ( v2 ), where v represents the number of tasks.
Full Text Available A decomposition-based optimization algorithm was proposed for solving Earth Observation Satellites scheduling problem. The problem was decomposed into task assignment main problem and single satellite scheduling sub-problem. In task assignment phase, the tasks were allocated to the satellites, and each satellite would schedule the task respectively in single satellite scheduling phase. We adopted an adaptive ant colony optimization algorithm to search the optimal task assignment scheme. Adaptive parameter adjusting strategy and pheromone trail smoothing strategy were introduced to balance the exploration and the exploitation of search process. A heuristic algorithm and a very fast simulated annealing algorithm were proposed to solve the single satellite scheduling problem. The task assignment scheme was valued by integrating the observation scheduling result of multiple satellites. The result was responded to the ant colony optimization algorithm, which can guide the search process of ant colony optimization. Computation results showed that the approach was effective to the satellites observation scheduling problem.
SHI Yan; HUANG Cong-ming
An improved particle swarm optimization (PSO) algorithm based on ensemble technique is presented. The algorithm combines some previous best positions (pbest) of the particles to get an ensemble position (Epbest), which is used to replace the global best position (gbest). It is compared with the standard PSO algorithm invented by Kennedy and Eberhart and some improved PSO algorithms based on three different benchmark functions. The simulation results show that the improved PSO based on ensemble technique can get better solutions than the standard PSO and some other improved algorithms under all test cases.
Ying Sun; Yuesheng Gu; Hegen Xiong
Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on.It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed ,which is called variable-boundary-coded quantum genetic algorithm (vbQGA) in which qubit chromosomes are collapsed into variableboundary- coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained.The m...
Most of the multimedia applications require strict Quality-of-Service (QoS) guarantee during the communication between a single source and multiple destinations. The paper mainly presents a QoS Multicast Routing algorithms based on Genetic Algorithm (QMRGA). Simulation results demonstrate that the algorithm is capable of discovering a set of QoS-based near optimized, non-dominated multicast routes within a few iterations, even for the networks environment with uncertain parameters.
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.
Zhang, Ruirui; Li, Tao; Xiao, Xin; Shi, Yuanquan
Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas around danger signals are called danger zones. By defining the danger zone to calculate danger signals for each antibody, the algorithm adjusts antibodies' concentrations through its own danger signals and then triggers immune responses of self-regulation. So the population diversity can be maintained. Experimental results show that the algorithm has more advantages in the solution quality and diversity of the population. Compared with influential optimization algorithms, CLONALG, opt-aiNet, and dopt-aiNet, the algorithm has smaller error values and higher success rates and can find solutions to meet the accuracies within the specified function evaluation times.
Ma, He; Xu, Hongming; Wang, Jihong; Schnier, Thorsten; Neaves, Ben; Tan, Cheng; Wang, Zhi
Modern engines feature a considerable number of adjustable control parameters. With this increasing number of Degrees of Freedom (DoF) for engines, and the consequent considerable calibration effort required to optimize engine performance, traditional manual engine calibration or optimization methods are reaching their limits. An automated engine optimization approach is desired. In this paper, a self-learning evolutionary algorithm based multi-objective globally optimization approach for a H...
Deng, Naiyang; Zhang, Chunhua
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions presents an accessible treatment of the two main components of support vector machines (SVMs)-classification problems and regression problems. The book emphasizes the close connection between optimization theory and SVMs since optimization is one of the pillars on which SVMs are built.The authors share insight on many of their research achievements. They give a precise interpretation of statistical leaning theory for C-support vector classification. They also discuss regularized twi
An improved real-coded genetic algorithm is pro-posed for global optimization of functionsl.The new algo-rithm is based om the judgement of the searching perfor-mance of basic real-coded genetic algorithm.The opera-tions of basic real-coded genetic algorithm are briefly dis-cussed and selected.A kind of chaos sequence is described in detail and added in the new algorithm ad a disturbance factor.The strategy of field partition is also used to im-prove the strcture of the new algorithm.Numerical ex-periment shows that the mew genetic algorithm can find the global optimum of complex funtions with satistaiting precision.
LAI Yifei; ZHANG Qianhua; JIA Junping
GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms, such as natural selection, genetic recombination and survival of the fittest. By use of coding betterment, the dynamic changes of the mutation rate and the crossover probability, the dynamic choice of subsistence, the reservation of the optimal fitness value, a modified genetic algorithm for optimizing combination of units in thermal power plants is proposed.And through taking examples, test result are analyzed and compared with results of some different algorithms. Numerical results show available value for the unit commitment problem with examples.
Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian
Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.
Full Text Available This article establish an efficient electronic commerce logistics operation system to reduce distribution costs and build a logistics network operation model based on around the B2C electronic commerce enterprise logistics network operation system. B2C electronic commerce transactions features in the enterprise network platform. To solve the NP-hard problem this article use hybrid ant colony algorithm, particle swarm algorithm and group swarm intelligence algorithm to get a best solution. According to the intelligent algorithm, design of electronic commerce logistics network optimization system, enter the national 22 electronic commerce logistics network for validation. Through the experiment to verify the optimized logistics cost greatly decreased. This research can help B2C electronic commerce enterprise logistics network to optimize decision-making under the premise of ensuring the interests of consumers and service levels also can be an effective way for enterprises to improve the efficiency of logistics services and reduce operation costs
Full Text Available The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
Full Text Available Along with the widespread application of the electronic commerce in the modern business, the logistic distribution has become increasingly important. More and more enterprises recognize that the logistic distribution plays an important role in the process of production and sales. A good routing for logistic distribution can cut down transport cost and improve efficiency. In order to cut down transport cost and improve efficiency, a routing optimization based on taboo search for logistic distribution is proposed in this paper. Taboo search is a metaheuristic search method to perform local search used for logistic optimization. The taboo search is employed to accelerate convergence and the aspiration criterion is combined with the heuristics algorithm to solve routing optimization. Simulation experimental results demonstrate that the optimal routing in the logistic distribution can be quickly obtained by the taboo search algorithm
Full Text Available Transmission system is a crucial precision mechanism for twin-screw chemi-mechanical pulping equipment. The structure of the system designed by traditional method is not optimal because the structure designed by the traditional methods is easy to fall into the local optimum. To achieve the global optimum, this article applies the genetic algorithm which has grown in recent years in the field of structure optimization. The article uses the volume of transmission system as the objective function to optimize the structure designed by traditional method. Compared to the simulation results, the original structure is not optimal, and the optimized structure is tighter and more reasonable. Based on the optimized results, the transmission shafts in the transmission system are designed and checked, and the parameters of the twin screw are selected and calculated. The article provided an effective method to design the structure of transmission system.
Ma, Jianhong; Zhang, Han; He, Baofeng
The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.
LI Jimin; SHANG Chaoxuan; ZOU Minghu
The selection of weighting matrix in design of the linear quadratic optimal controller is an important topic in the control theory. In this paper, an approach based on genetic algorithm is presented for selecting the weighting matrix for the optimal controller. Genetic algorithm is adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this algorithm, the fitness function is used to evaluate individuals and reproductive success varies with fitness. In the design of the linear quadratic optimal controller, the fitness function has relation to the anticipated step response of the system. Not only can the controller designed by this approach meet the demand of the performance indexes of linear quadratic controller, but also satisfy the anticipated step response of close-loop system. The method possesses a higher calculating efficiency and provides technical support for the optimal controller in engineering application. The simulation of a three-order single-input single-output (SISO) system has demonstrated the feasibility and validity of the approach.
Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.
During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically opti...
Dong Yumin; Zhao Li
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...
Full Text Available The problem of QoS multicast routing is to find a multicast tree with the least expense/cost which would meet the limitations such as band width, delay and loss rate. This is a NP-Complete problem. To solve the problem of multicast routing, the entire routes from the source node to every destination node are often recognized. Then the routes are integrated and changed into a single multicast tree. But they are slow and complicated methods. The present paper introduces a new tree-based optimization method to overcome such weaknesses. The recommended method directly optimizes the multicast tree. Therefore a tree-based typology including several spanning trees is created which combines the trees two by two. For this purpose, the Cuckoo Algorithm is used which is proved to be well converged and makes quick calculations. The simulation conducted on different types of network typologies proved that it is a practical and influential algorithm.
C Rajan; N Shanthi
Mobile Ad hoc Network (MANET) is established for a limited period, for special extemporaneous services related to mobile applications. This ad hoc network is set up for a limited period, in environments that change with the application. While in Internet the TCP/IP protocol suite supports a wide range of application, in MANETs protocols are tuned to specific customer/application. Multicasting is emerging as a popular communication format where the same packet is sent to multiple nodes in a network. Routing in multicasting involves maintaining routes and finding new node locations in a group and is NP-complete due to the dynamic nature of the network. In this paper, a Hybrid Genetic Based Optimization for Multicast Routing algorithm is proposed. The proposed algorithm uses the best features of Genetic Algorithm (GA) and particle swarm optimization (PSO) to improve the solution. Simulations were conducted by varying number of mobile nodes and results compared with Multicast AODV (MAODV) protocol, PSO based and GA based solution. The proposed optimization improves jitter, end to end delay and Packet Delivery Ratio (PDR) with faster convergence.
R. Venkata Rao
Full Text Available The performance of rapid prototyping (RP processes is often measured in terms of build time, product quality, dimensional accuracy, cost of production, mechanical and tribological properties of the models and energy consumed in the process. The success of any RP process in terms of these performance measures entails selection of the optimum combination of the influential process parameters. Thus, in this work the single-objective and multi-objective optimization problems of a widely used RP process, namely, fused deposition modeling (FDM, are formulated, and the same are solved using the teaching-learning-based optimization (TLBO algorithm and non-dominated Sorting TLBO (NSTLBO algorithm, respectively. The results of the TLBO algorithm are compared with those obtained using genetic algorithm (GA, and quantum behaved particle swarm optimization (QPSO algorithm. The TLBO algorithm showed better performance as compared to GA and QPSO algorithms. The NSTLBO algorithm proposed to solve the multi-objective optimization problems of the FDM process in this work is a posteriori version of the TLBO algorithm. The NSTLBO algorithm is incorporated with non-dominated sorting concept and crowding distance assignment mechanism to obtain a dense set of Pareto optimal solutions in a single simulation run. The results of the NSTLBO algorithm are compared with those obtained using non-dominated sorting genetic algorithm (NSGA-II and the desirability function approach. The Pareto-optimal set of solutions for each problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for the FDM process.
Shi Jing; Meng Weixiao; Zhang Naitong; Wang Zheng
All thc parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms (GAs).Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.
Full Text Available This paper puts forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction (τ,m and least squares support vector machine (LS-SVM (γ,σ by using membrane computing optimization algorithm. It is an important basis for spectrum management to predict accurately the change trend of parameters in the electromagnetic environment, which can help decision makers to adopt an optimal action. Then, the model presented in this paper is used to forecast band occupancy rate of frequency modulation (FM broadcasting band and interphone band. To show the applicability and superiority of the proposed model, this paper will compare the forecast model presented in it with conventional similar models. The experimental results show that whether single-step prediction or multistep prediction, the proposed model performs best based on three error measures, namely, normalized mean square error (NMSE, root mean square error (RMSE, and mean absolute percentage error (MAPE.
Full Text Available We present CFSO3, an optimization heuristic within the class of the swarm intelligence, based on a synergy among three different features of the Continuous Flock-of-Starlings Optimization. One of the main novelties is that this optimizer is no more a classical numerical algorithm since it now can be seen as a continuous dynamic system, which can be treated by using all the mathematical instruments available for managing state equations. In addition, CFSO3 allows passing from stochastic approaches to supervised deterministic ones since the random updating of parameters, a typical feature for numerical swam-based optimization algorithms, is now fully substituted by a supervised strategy: in CFSO3 the tuning of parameters is a priori designed for obtaining both exploration and exploitation. Indeed the exploration, that is, the escaping from a local minimum, as well as the convergence and the refinement to a solution can be designed simply by managing the eigenvalues of the CFSO state equations. Virtually in CFSO3, just the initial values of positions and velocities of the swarm members have to be randomly assigned. Both standard and parallel versions of CFSO3 together with validations on classical benchmarks are presented.
Many factors influencing range of extended range guided munition (ERGM) are analyzed. The definition domain of the most important three parameters are ascertained by preparatory mathematical simulation, the optimized mathematical model of ERGM maximum range with boundary conditions is created, and parameter optimization based on genetic algorithm (GA) is adopted. In the GA design, three-point crossover is used and the best chromosome is kept so that the convergence speed becomes rapid. Simulation result shows that GA is feasible, the result is good and it can be easy to attain global optimization solution, especially when the objective function is not the convex one for independent variables and it is a multi-parameter problem.
Full Text Available We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network.
Tzyy-Chyang Lu; Gwo-Ruey Yu; Jyh-Ching Juang
This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.
Mohanty, Prases K.; Parhi, Dayal R.
Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
M. Mohamed Surputheen
Full Text Available Genetic Algorithms (GAs has innumerable applications through the optimization techniques and network optimization is one of them. SNMP (Simple Network Management Protocol is used as the basic network protocol for monitoring the network activities health of the systems. This paper deals with adding Intelligence to the various aspects of SNMP by adding optimization techniques derived out of genetic algorithms, which enhances the performance of SNMP processes like routing.
Full Text Available To accurately evaluate the applicability of optimal coning algorithms, the direct influence of their periodic components on attitude accuracy is investigated. The true value of the change of the rotation vector is derived from the classical coning motion for analytic comparison. The analytic results show that the influence of periodic components is mostly dominant in two types of optimal coning algorithms. Considering that the errors of periodic components cannot be simply neglected, these algorithms are categorized with simplified forms. A variety of simulations are done under the classical coning motion. The numerical results are in good agreement with the analytic deductions. Considering their attitude accuracy, optimal coning algorithms of the 4-subinterval and 5-subinterval algorithms optimized with angular increments are not recommended for use for real application.
Full Text Available To accurately evaluate the applicability of optimal coning algorithms, the direct influence of their periodic components on attitude accuracy is investigated. The true value of the change of the rotation vector is derived from the classical coning motion for analytic comparison. The analytic results show that the influence of periodic components is mostly dominant in two types of optimal coning algorithms. Considering that the errors of periodic components cannot be simply neglected, these algorithms are categorized with simplified forms. A variety of simulations are done under the classical coning motion. The numerical results are in good agreement with the analytic deductions. Considering their attitude accuracy, optimal coning algorithms of the 4-subinterval and 5-subinterval algorithms optimized with angular increments are not recommended for use for real application.Defence Science Journal, 2012, 62(6, pp.361-368, DOI:http://dx.doi.org/10.14429/dsj.62.1430
Full Text Available Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.
Full Text Available A hybrid trajectory optimization method consisting of Gauss pseudospectral method (GPM and natural computation algorithm has been developed and utilized to solve multiphase return trajectory optimization problem, where a phase is defined as a subinterval in which the right-hand side of the differential equation is continuous. GPM converts the optimal control problem to a nonlinear programming problem (NLP, which helps to improve calculation accuracy and speed of natural computation algorithm. Through numerical simulations, it is found that the multiphase optimal control problem could be solved perfectly.
FENG Zhong-ren; WANG Xiong-jiang
The genetic algorithm was used in optimal design of deep jet method pile. The cost of deep jetmethod pile in one unit area of foundation was taken as the objective function. All the restrains were listed followingthe corresponding specification. Suggestions were proposed and the modified. The real-coded Genetic Algorithm wasgiven to deal with the problems of excessive computational cost and premature convergence. Software system of opti-mal design of deep jet method pile was developed.
Changqing YAO; Zhifeng YANG
Due to the multiplicity of factors including weather, the underlying surface and human activities, the complexity of parameter optimization for a distributed hydrological model of a watershed land surface goes far beyond the capability of traditional optimization methods. The genetic algorithm is a new attempt to find a solution to this problem. A genetic algorithm design on the Distributed-Hydrology-Soil-Vegetation model (DHSVM) parameter optimization is illustrated in this paper by defining the encoding method, designing the fitness value function, devising the genetic operators, selecting the arithmetic parameters and identifying the arithmetic termination conditions. Finally, a case study of the optimization method is implemented on the Lushi Watershed of the Yellow River Basin and achieves satisfactory results of parameter estimation. The result shows that the genetic algorithm is feasible in optimizing parameters of the DHSVM model.
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors.
Wang Pengjun; Lu Jingang; Xu Jian; Dai Jing
Based on the investigation of the XNOR/OR logical expression and the propagation algorithm of signal probability, a low power synthesis algorithm based on the XNOR/OR logic is proposed in this paper. The proposed algorithm has been implemented with C language. Fourteen Microelectronics Center North Carolina (MCNC) benchmarks are tested, and the results show that the proposed algorithm not only significantly reduces the average power consumption up to 27% without area and delay compensations, but also makes the runtime shorter.
YU Yan-li; WANG Ying-min; LI Lei
As conventional methods for beam pattern synthesis can not always obtain the desired optimum pattern for the arbitrary underwater acoustic sensor arrays, a hybrid numerical synthesis method based on adaptive principle and genetic algorithm was presented in this paper. First, based on the adaptive theory, a given array was supposed as an adaptive array and its sidelobes were reduced by assigning a number of interference signals in the sidelobe region. An initial beam pattern was obtained after several iterations and adjustments of the interference intensity, and based on its parameters, a desired pattern was created. Then, an objective function based on the difference between the designed and desired patterns can be constructed. The pattern can be optimized by using the genetic algorithm to minimize the objective function. A design example for a double-circular array demonstrates the effectiveness of this method. Compared with the approaches existing before, the proposed method can reduce the sidelobe effectively and achieve less synthesis magnitude error in the mainlobe.The method can search for optimum attainable pattern for the specific elements if the desired pattern can not be found.
WANG; Fu-qiang; RONG; Fei
[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.
HU Kun; XU Yi-fan
Aiming at the demand for optimization of hydrodynamic coefficients in submarine's motion equations, an adaptive weight immune genetic algorithm was proposed to optimize hydrodynamic coefficients in motion equations. Some hydrody-namic coefficients of high sensitivity to control and maneuver were chosen as the optimization objects in the algorithm. By using adaptive weight method to determine the weight and target function, the multi-objective optimization could be transla-ted into single-objective optimization. For a certain kind of submarine, three typical maneuvers were chosen to be the objects of study: overshoot maneuver in horizontal plane, overshoot maneuver in vertical plane and turning circle maneuver in horizontal plane. From the results of computer simulations using primal hydrodynamic coefficient and optimized hydrody-namic coefficient, the efficiency of proposed method is proved.
The aim of this paper is to provide a review of structural decomposition methods in discrete optimization and to give a unified framework in the form of local elimination algorithms (LEA). This paper is organized as follows. Local elimination algorithms for discrete optimization (DO) problems (DOPs) with constraints are considered; a classification of dynamic programming computational procedures is given. We introduce Elimination Game and Elimination tree. Application of bucket elimination algorithm from constraint satisfaction (CS) to solving DOPs is done. We consider different local elimination schemes and related notions. Clustering that merges several variables into single meta-variable defines a promising approach to solve DOPs. This allows to create a quotient (condensed) graph and apply a local block elimination algorithm. In order to describe a block elimination process, we introduce Block Elimination Game. We discuss the connection of aforementioned local elimination algorithmic schemes and a way of ...
Full Text Available Under the global voice of “energy saving” and the current boom in the development of energy storage technology at home and abroad, energy optimal control of the whole hybrid electric vehicle power system, as one of the core technologies of electric vehicles, is bound to become a hot target of “clean energy” vehicle development and research. This paper considers the constraints to the performance of energy storage system in Parallel Hybrid Electric Vehicle (PHEV, from which lithium-ion battery frequently charges/discharges, PHEV largely consumes energy of fuel, and their are difficulty in energy recovery and other issues in a single cycle; the research uses lithium-ion battery combined with super-capacitor (SC, which is hybrid energy storage system (Li-SC HESS, working together with internal combustion engine (ICE to drive PHEV. Combined with PSO-PI controller and Li-SC HESS internal power limited management approach, the research proposes the PHEV energy optimal control strategy. It is based on revised Pontryagin’s minimum principle (PMP algorithm, which establishes the PHEV vehicle simulation model through ADVISOR software and verifies the effectiveness and feasibility. Finally, the results show that the energy optimization control strategy can improve the instantaneity of tracking PHEV minimum fuel consumption track, implement energy saving, and prolong the life of lithium-ion batteries and thereby can improve hybrid energy storage system performance.
WANG Chun; CHENG Hao-zhong; HU Ze-chun; WANG Yi
An approach for the integrated optimization of the construction/expansion capacity of high-voltage/medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.
Full Text Available Expert system Nephrology is a computer program that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. The knowledge base consistsof information about a particular problem area. This information is collected from domain experts (doctors. This system mainly contains two modules one is Information System and the other is Expert Advisory system. The Information System contains the static information about different diseases and drugs in the field of Nephrology. This information system helps the patients /users to know about the problems related to kidneys. The Nephrology Advisory system helps the Patients /users to get the required and suitable advice depending on their queries. This medical expert system is developedusing Java Server Pages (JSP as front-end and MYSQL database as Backend in such a way that all the activities are carried out in a user-friendly manner. Improved Ant Colony Optimization Algorithm (ACO along with RETE algorithm is also used for better results.
Singh, R.; Verma, H. K.
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
Budiman, Gelar; Novamizanti, Ledya; Iwut, Iwan
Data hiding in an image content is mandatory for setting the ownership of the image. Two dimensions discrete wavelet transform (DWT) and discrete cosine transform (DCT) are proposed as transform method in this paper. First, the host image in RGB color space is converted to selected color space. We also can select the layer where the watermark is embedded. Next, 2D-DWT transforms the selected layer obtaining 4 subband. We select only one subband. And then block-based 2D-DCT transforms the selected subband. Binary-based watermark is embedded on the AC coefficients of each block after zigzag movement and range based pixel selection. Delta parameter replacing pixels in each range represents embedded bit. +Delta represents bit “1” and –delta represents bit “0”. Several parameters to be optimized by Genetics Algorithm (GA) are selected color space, layer, selected subband of DWT decomposition, block size, embedding range, and delta. The result of simulation performs that GA is able to determine the exact parameters obtaining optimum imperceptibility and robustness, in any watermarked image condition, either it is not attacked or attacked. DWT process in DCT based image watermarking optimized by GA has improved the performance of image watermarking. By five attacks: JPEG 50%, resize 50%, histogram equalization, salt-pepper and additive noise with variance 0.01, robustness in the proposed method has reached perfect watermark quality with BER=0. And the watermarked image quality by PSNR parameter is also increased about 5 dB than the watermarked image quality from previous method.
Full Text Available Principal Component Analysis (PCA is an algorithm involving heavy mathematical operations with matrices. The data extracted from the face images are usually very large and to process this data is time consuming. To reduce the execution time of these operations, parallel programming techniques are used. CUDA is a multipurpose parallel programming architecture supported by graphics cards. In this study we have implemented the PCA algorithm using both the classical programming approach and CUDA based implementation using different configurations. The algorithm is subdivided into its constituent calculation steps and evaluated for the positive effects of parallelization on each step. Therefore, the parts of the algorithm that cannot be improved by parallelization are identified. On the other hand, it is also shown that, with CUDA based approach dramatic improvements in the overall performance of the algorithm arepossible.
Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.
Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.
Hyo Seon Park
Full Text Available Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.
Full Text Available Actuators formed from piezoelectric ceramics were embedded in truss rods to make up active rods. The paper used mechanical knowledge, static stiffness method and the finite element method to analyze the active rod and the smart truss structure and then model them. In order to solve the difficult problem of number optimization, the paper put forward the actuator existence variable and optimized number and locations of actuators at the same time, made the structure have the best output effect, so it can reduce the displacement at the designated location of the truss structure and the structure vibration. It also can improve the truss structure accuracy. Then find the optimal solution by genetic algorithms（GA） and MATLAB programming. The results of the example show that the model this paper builds is correct and genetic algorithms are effective in solving the optimization question.
Full Text Available This study takes the concept of food logistics distribution as the breakthrough point, by means of the aim of optimization of food logistics distribution routes and analysis of the optimization model of food logistics route, as well as the interpretation of the genetic algorithm, it discusses the optimization of food logistics distribution route based on genetic and cluster scheme algorithm.
Enevoldsen, I.; Sørensen, John Dalsgaard
The common problem of an extensive number of limit state function calculations in the various formulations and applications of reliability-based optimization is treated. It is suggested to use a formulation based on decomposition techniques so the nested two-level optimization problem can be solved...
Aslantas, Veysel; Ozer, Saban; Ozturk, Serkan
The performance of a fragile watermarking method based on discrete cosine transform (DCT) has been improved in this paper by using intelligent optimization algorithms (IOA), namely genetic algorithm, differential evolution algorithm, clonal selection algorithm and particle swarm optimization algorithm. In DCT based fragile watermarking techniques, watermark embedding can usually be achieved by modifying the least significant bits of the transformation coefficients. After the embedding process is completed, transforming the modified coefficients from the frequency domain to the spatial domain produces some rounding errors due to the conversion of real numbers to integers. The rounding errors caused by this transformation process were corrected by the use of intelligent optimization algorithms mentioned above. This paper gives experimental results which show the feasibility of using these optimization algorithms for the fragile watermarking and demonstrate the accuracy of these methods. The performance comparison of the algorithms was also realized.
Wang Changjun; Xi Yugeng
Focusing on the single machine scheduling problem which minimizes the total completion time in the presence of dynamic job arrivals, a rolling optimization scheduling algorithm is proposed based on the analysis of the character and structure of scheduling. An optimal scheduling strategy in collision window is presented. Performance evaluation of this algorithm is given. Simulation indicates that the proposed algorithm is better than other common heuristic algorithms on both the total performance and stability.
The pheromone based positive feedback approach of ant algorithmis int r oduced in evolutionary computation of discrete problem, so as to accomplish the optimization of each allele. It ensures stable converge of the algorithm into global optimum. The optimal cutting problem is studied as an example to analyze the performance of the algorithm. The experimental results show the novel performa nce of the algorithm in the optimization of discrete problem.
Full Text Available Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.
Full Text Available Abstract Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.
杨剑影; 张海; 谢邦荣; 尹健
Based on the optimal control theory, a 3-dimensionnal direct generation algorithm is proposed for anti-ground low altitude penetration tasks under complex terrain. By optimizing the terrain following(TF) objective function,terrain coordinate system, missile dynamic model and control vector, the TF issue is turning into the improved optimal control problem whose mathmatical model is simple and need not solve the second order terrain derivative. Simulation results prove that this method is reasonable and feasible. The TF precision is in the scope from 0.3 m to 3.0 m,and the planning time is less than 30 min. This method have the strongpionts such as rapidness, precision and has great application value.
WEN Zhi-qiang; CAI Zi-xing
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted,the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.
Mostafa Lotfi Forushani
Full Text Available This paper presents an optimized controller around the longitudinal axis of multivariable system in one of the aircraft flight conditions. The controller is introduced in order to control the angle of attack from the pitch attitude angle independently (that is required for designing a set of direct force-modes for the longitudinal axis based on particle swarm optimization (PSO algorithm. The autopilot system for military or civil aircraft is an essential component and in this paper, the autopilot system via 6 degree of freedom model for the control and guidance of aircraft in which the autopilot design will perform based on defining the longitudinal and the lateral-directional axes are supposed. The effectiveness of the proposed controller is illustrated by considering HIMAT aircraft. The simulation results verify merits of the proposed controller.
GUO Jun-feng; LI Yan-jun; YU Rui-xing; ZHANG Ke
In this paper,a new optimal time-frequency atom search method based on a modified ant colony algorithm is proposed to improve the precision of the traditional methods.First,the discretization formula of finite length time-frequency atom is inferred at length.Second; a modified ant colony algorithm in continuous space is proposed.Finally,the optimal timefrequency atom search algorithm based on the modified ant colony algorithm is described in detail and the simulation experiment is carried on.The result indicates that the developed algorithm is valid and stable,and the precision of the method is higher than that of the traditional method.
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
R. Venkata Rao
Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.
Shao Wei; Qian Zuping; Yuan Feng
A robust phase-only Direct Data Domain Least Squares (D3LS) algorithm based on generalized Rayleigh quotient optimization using hybrid Genetic Algorithm (GA) is presented in this letter. The optimization efficiency and computational speed are improved via the hybrid GA composed of standard GA and Nelder-Mead simplex algorithms. First, the objective function, with a form of generalized Rayleigh quotient, is derived via the standard D3LS algorithm. It is then taken as a fitness function and the unknown phases of all adaptive weights are taken as decision variables.Then, the nonlinear optimization is performed via the hybrid GA to obtain the optimized solution of phase-only adaptive weights. As a phase-only adaptive algorithm, the proposed algorithm is simpler than conventional algorithms when it comes to hardware implementation. Moreover, it processes only a single snapshot data as opposed to forming sample covariance matrix and operating matrix inversion. Simulation results show that the proposed algorithm has a good signal recovery and interferences nulling performance, which are superior to that of the phase-only D3LS algorithm based on standard GA.
ZHANG Hong-lie; ZHANG Guo-yin; YAO Ai-hong
This paper presents an algorithm that combines the chaos optimization algorithm with the maximum entropy(COA-ME)by using entropy model based on chaos algorithm,in which the maximum entropy is used as the second method of searching the excellent solution.The search direction is improved by chaos optimization algorithm and realizes the selective acceptance of wrong solution.The experimental result shows that the presented algorithm can be used in the partitioning of hardware/software of reconfigurable system.It effectively reduces the local extremum problem,and search speed as well as performance of partitioning is improved.
Motion planning problems have been studied by both the robotics and the controls research communities for a long time, and many algorithms have been developed for their solution. Among them, incremental sampling-based motion planning algorithms, such as the Rapidly-exploring Random Trees (RRTs), and the Probabilistic Road Maps (PRMs) have become very popular recently, owing to their implementation simplicity and their advantages in handling high-dimensional problems. Although these algorithms work very well in practice, the quality of the computed solution is often not good, i.e., the solution can be far from the optimal one. A recent variation of RRT, namely the RRT* algorithm, bypasses this drawback of the traditional RRT algorithm, by ensuring asymptotic optimality as the number of samples tends to infinity. Nonetheless, the convergence rate to the optimal solution may still be slow. This paper presents a new incremental sampling-based motion planning algorithm based on Rapidly-exploring Random Graphs (RRG...
Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
Full Text Available Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO optimized support vector machine (SVM interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.
Jia Chunqiang; Yu Ling; Tian Shujun; Gao Yanming
To solve the combinatorial optimization problem of outer layout and inner connection integrated schemes in the design of hydraulic manifold blocks(HMB),a hybrid genetic simulated annealing algorithm based on niche technology is presented.This hybrid algorithm,which combines genetic algorithm,simulated annealing algorithm and niche technology,has a strong capability in global and local search,and all extrema can be found in a short time without strict requests for preferences.For the complex restricted solid spatial layout problems in HMB,the optimizing mathematical model is presented.The key technologies in the integrated layout and connection design of HMB,including the realization of coding,annealing operation and genetic operation,are discussed.The framework of HMB optimal design system based on hybrid optimization strategy is proposed.An example is given to testify the effectiveness and feasibility of the algorithm.
Full Text Available Recently, Gandomi and Alavi proposed a novel swarm intelligent method, called krill herd (KH, for global optimization. To enhance the performance of the KH method, in this paper, a new improved meta-heuristic simulated annealing-based krill herd (SKH method is proposed for optimization tasks. A new krill selecting (KS operator is used to refine krill behavior when updating krill’s position so as to enhance its reliability and robustness dealing with optimization problems. The introduced KS operator involves greedy strategy and accepting few not-so-good solutions with a low probability originally used in simulated annealing (SA. In addition, a kind of elitism scheme is used to save the best individuals in the population in the process of the krill updating. The merits of these improvements are verified by fourteen standard benchmarking functions and experimental results show that, in most cases, the performance of this improved meta-heuristic SKH method is superior to, or at least highly competitive with, the standard KH and other optimization methods.
Full Text Available A new genetic algorithm (GA methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS, is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA utilizing a single population, a multipopulation-based genetic algorithm (MPGA proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.
Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng
The optimal control problem for switched linear systems with internally forced switching has more constraints than with externally forced switching. Heavy computations and slow convergence in solving this problem is a major obstacle. In this paper we describe a new approach for solving this problem, which is called Migrant Particle Swarm Optimization (Migrant PSO). Imitating the behavior of a flock of migrant birds, the Migrant PSO applies naturally to both continuous and discrete spaces, in which definitive optimization algorithm and stochastic search method are combined. The efficacy of the proposed algorithm is illustrated via a numerical example.
Ma, Tinghuai; Li, Lu; Ji, Sai; Wang, Xin; Tian, Yuan; Al-Dhelaan, Abdullah; Al-Rodhaan, Mznah
In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.
How to solve unit commitment and load dispatch of power system by genetic algorithms is discussed in this paper. A combination encoding scheme of binary encoding and floating number encoding and corresponding genetic operators are developed. Meanwhile a contract mapping genetic algorithm is used to enhance traditional GA' s convergence. The result of a practical example shows that this algorithm is effective.
OpenMP Parallelization and Optimization of Graph-based Machine Learning Algorithms Zhaoyi Meng, Alice Koniges, Yun (Helen) He, Samuel Williams...email@example.com firstname.lastname@example.org Abstract. We investigate the OpenMP parallelization and optimiza- tion of two novel data classification algorithms . The...new algorithms are based on graph and PDE solution techniques and provide significant ac- curacy and performance advantages over traditional data
R. Venkata Rao
Full Text Available This paper presents the performance of teaching–learning-based optimization (TLBO algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH. The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching–learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.
Barmada, Sami; Raugi, Marco; Tucci, Mauro
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
Harizanov, S.; Georgiev, I.
In this paper we propose an iterative steepest-descent-type algorithm that is observed to converge towards the exact solution of the ℓ0 discrete optimization problem, related to graph-Laplacian based image segmentation. Such an algorithm allows for significant additional improvements on the segmentation quality once the minimizer of the associated relaxed ℓ1 continuous optimization problem is computed, unlike the standard strategy of simply hard-thresholding the latter. Convergence analysis of the algorithm is not a subject of this work. Instead, various numerical experiments, confirming the practical value of the algorithm, are documented.
A. S. M. Zahid Kausar
Full Text Available Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented by microfacets, for which it becomes possible to compute the scattering field in all possible directions. New optimization techniques, like dual quadrant skipping (DQS and closest object finder (COF, are implemented for fast characterization of wireless communications and making the ray tracing technique more efficient. In conjunction with the ray tracing technique, probability based coverage optimization algorithm is accumulated with the ray tracing technique to make a compact solution for indoor propagation prediction. The proposed technique decreases the ray tracing time by omitting the unnecessary objects for ray tracing using the DQS technique and by decreasing the ray-object intersection time using the COF technique. On the other hand, the coverage optimization algorithm is based on probability theory, which finds out the minimum number of transmitters and their corresponding positions in order to achieve optimal indoor wireless coverage. Both of the space and time complexities of the proposed algorithm surpass the existing algorithms. For the verification of the proposed ray tracing technique and coverage algorithm, detailed simulation results for different scattering factors, different antenna types, and different operating frequencies are presented. Furthermore, the proposed technique is verified by the experimental results.
Feng Yi; Li Li; Tian Shujun
Optimization design of hydraulic manifold blocks (HMB) is studied as a complex solid spatial layout problem. Based on comprehensive research into structure features and design rules of HMB, an optimal mathematical model for this problem is presented. Using human-computer cooperative genetic algorithm (GA) and its hybrid optimization strategies, integrated layout and connection design schemes of HMB can be automatically optimized. An example is given to testify it.
Full Text Available To clearly find the effect of factors in network classification, the classification process of PNN is analyzed in detail. The XOR problem is described by PNN and the elements in PNN are also studied. Through simulations and combined with genetic algorithm, a novel PNN supervised learning algorithm is proposed. This algorithm introduces the classification accuracy of training samples to the network parameter learning. It adopts genetic algorithm to train the PNN smoothing parameter and hidden centric vector. Then the effects of hidden neuron number, hidden centric vector and smoothing parameter in PNN are verified in the experiments. It is shown that this algorithm is superior to other PNN learning algorithms on classification effect.
Full Text Available We propose a generalization belief propagation (BP decoding algorithm based on particle swarm optimization (PSO to improve the performance of the polar codes. Through the analysis of the existing BP decoding algorithm, we first introduce a probability modifying factor to each node of the BP decoder, so as to enhance the error correcting capacity of the decoding. Then, we generalize the BP decoding algorithm based on these modifying factors and drive the probability update equations for the proposed decoding. Based on the new probability update equations, we show the intrinsic relationship of the existing decoding algorithms. Finally, in order to achieve the best performance, we formulate an optimization problem to find the optimal probability modifying factors for the proposed decoding algorithm. Furthermore, a method based on the modified PSO algorithm is also introduced to solve that optimization problem. Numerical results show that the proposed generalization BP decoding algorithm achieves better performance than that of the existing BP decoding, which suggests the effectiveness of the proposed decoding algorithm.
Akhilesh A. Waoo
Full Text Available Present research work describes advancement in standard routing protocol AODV for mobile ad-hoc networks. Our mechanism sets up multiple optimal paths with the criteria of bandwidth and delay to store multiple optimal paths in the network. At time of link failure, it will switch to next available path. We have used the information that we get in the RREQ packet and also send RREP packet to more than one path, to set up multiple paths, It reduces overhead of local route discovery at the time of link failure and because of this End to End Delay and Drop Ratio decreases. The main feature of our mechanism is its simplicity and improved efficiency. This evaluates through simulations the performance of the AODV routing protocol including our scheme and we compare it with HLSMPRA (Hot Link Split Multi-Path Routing Algorithm Algorithm. Indeed, our scheme reduces routing load of network, end to end delay, packet drop ratio, and route error sent. The simulations have been performed using network simulator OPNET. The network simulator OPNET is discrete event simulation software for network simulations which means it simulates events not only sending and receiving packets but also forwarding and dropping packets. This modified algorithm has improved efficiency, with more reliability than Previous Algorithm.
Rani, R Ranjani; Ramyachitra, D
Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods.
ZHAO Peng; MU Xin; YAO Jin-hua; WANG Yong; YANG Xiu-tai
We established an integrated and optimized model of vehicle scheduling problem and vehicle filling problem for solving an extremely complex delivery mode-multi-type vehicles, non-full loads, pickup and delivery in logistics and delivery system. The integrated and optimized model is based on our previous research result-effective space method. An integrated algorithm suitable for the integrated and optimized model was proposed and corresponding computer programs were designed to solve practical problems. The results indicates the programs can work out optimized delivery routes and concrete loading projects. The model and algorithm have many virtues and are valuable in practice.
Full Text Available Using statistical analysis strategy, a large-scale firewall log files is analyzed and two main characteristics, the protocol field and the IP address field, is extracted in this paper. Based on the extracted features and the characteristics of multi-tree and dual-index strategy, we design a better firewall optimization algorithm. Compared with the Stochastic Distribution Multibit-trie (SDMTrie algorithm, our proposed algorithm can greatly decrease the preprocessing time and improve the searching and filtering process.
Ling-Lai Li; Dong-Hua Zhou; Ling Wang
Fault diagnosis of nonlinear systems is of great importance in theory and practice, and the parameter estimation method is an effective strategy. Based on the framework of moving horizon estimation, fault parameters are identified by a proposed intelligent optimization algorithm called PSOSA, which could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA). Simulations on a three-tank system show the effectiveness of this optimization based fault diagnosis strategy.
Baljit Singh Khehra
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.
Heemstra de Groot, Sonia M.; Herrmann, Otto E.
A method for rate-optimal scheduling of recursive DSP algorithms is presented. The approach is based on the determination of the scheduling window of each operation and the construction of a scheduling-range chart. The information in the chart is used during scheduling to optimize some quality crite
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
Saadatmand-Tarzjan, Mahdi; Moghaddam, Hamid Abrishami
Optimization of content-based image indexing and retrieval (CBIR) algorithms is a complicated and time-consuming task since each time a parameter of the indexing algorithm is changed, all images in the database should be indexed again. In this paper, a novel evolutionary method called evolutionary group algorithm (EGA) is proposed for complicated time-consuming optimization problems such as finding optimal parameters of content-based image indexing algorithms. In the new evolutionary algorithm, the image database is partitioned into several smaller subsets, and each subset is used by an updating process as training patterns for each chromosome during evolution. This is in contrast to genetic algorithms that use the whole database as training patterns for evolution. Additionally, for each chromosome, a parameter called age is defined that implies the progress of the updating process. Similarly, the genes of the proposed chromosomes are divided into two categories: evolutionary genes that participate to evolution and history genes that save previous states of the updating process. Furthermore, a new fitness function is defined which evaluates the fitness of the chromosomes of the current population with different ages in each generation. We used EGA to optimize the quantization thresholds of the wavelet-correlogram algorithm for CBIR. The optimal quantization thresholds computed by EGA improved significantly all the evaluation measures including average precision, average weighted precision, average recall, and average rank for the wavelet-correlogram method.
The proposed algorithm (MOBBO/DE makes the use of nondominated sorting approach improve the convergence ability efficiently and hence it can generate the promising candidate solutions. It also combines crowding distance to guarantee the diversity of Pareto optimal solutions. The proposed approach is validated using several test functions and some metrics taken from the standard literature on evolutionary multi-objective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.
Taher NIKNAM; Babak AMIRI; Javad OLAMAEI; Ali AREFI
The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the Kmeans algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Riplcy's Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.
Xiong LUO; Xiaoping FAN; Heng ZHANG; Tefang CHEN
Optimal trajectory planning for robot manipulators plays an important role in implementing the high productivity for robots.The performance indexes used in optimal trajectory planning are classified into two main categories:optimum traveling time and optimum mechanical energy of the actuators.The current trajectory planning algorithms are designed based on one of the above two performance indexes.So far,there have been few planning algorithms designed to satisfy two performance indexes simultaneously.On the other hand,some deficiencies arise in the existing integrated optimization algorithms of trajectory planning.In order to overcome those deficiencies,the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators.In the algorithm,two object functions are designed based on the specific weight coefficient method and "ideal point" strategy.Moreover,based on the features of optimization problem,the intensified evolutionary programming is proposed to solve the corresponding optimization model.Especially,for the Stanford Robot,the high-quality solutions are found at a lower cost.
Complexity of global optimization algorithms makes implementation of the algorithms difficult and leads the algorithms to require more computer resources for the optimization process. The ability to explore the whole solution space without increasing the complexity of algorithms has a great importance to not only get reliable results but so also make the implementation of these algorithms more convenient for higher dimensional and complex-real world problems in science and engineering. In this paper, we present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique that is designed into a group architecture similar to the architecture of Cooperative Coevolutionary Algorithms. Therefore, we present the implementation method and the experimental results for the single and multidimensional optimization test problems and a scientific real world problem, the energies and the geometric structures of Lennard-Jones clusters.
Rui Ge; Jianqiao Chen; Jnnhong Wei
This paper proposed a reliability design model for composite materials under the mixture of random and interval variables. Together with the inverse reliability analysis technique, the sequential single-loop optimization method is applied to the reliability-based design of com-posites. In the sequential single-loop optimization, the optimization and the reliability analysis are decoupled to improve the computational efficiency. As shown in examples, the minimum weight problems under the constraint of structural reliability are solved for laminated composites. The Particle Swarm Optimization (PSO) algorithm is utilized to search for the optimal solutions. The design results indicate that, under the mixture of random and interval variables, the method that combines the sequential single-loop optimization and the PSO algorithm can deal effectively with the reliability-based design of composites.
Arora, Rajesh Kumar
Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc
Shi Zhifu; Zhang An; Wang Anli
Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.
Full Text Available The excellent spatial localization, frequency spread and multi-resolution characteristics of the Discrete Wavelets Transform (DWT, which were similar to the theoretical models of the human visual system, facilitated the development of many imperceptible and robust DWT-based watermarking algorithms. There had been extremely few proposed algorithms on optimized DWT-based image watermarking that can simultaneously provide perceptual transparency and robustness since these two watermarking requirements are conflicting, in this study we treat the DWT-based image watermarking problem as an optimization problem and solve it using genetic algorithms. We demonstrate through the experimental results we obtained that optimal DWT-based image watermarking can be achieved only if watermarking has been applied at specific wavelet sub-bands and by using specific watermark-amplification values.
Full Text Available A hybrid optimization method is presented to optimum seismic design of steel frames considering four performance levels. These performance levels are considered to determine the optimum design of structures to reduce the structural cost. A pushover analysis of steel building frameworks subject to equivalent-static earthquake loading is utilized. The algorithm is based on the concepts of the charged system search in which each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Comparison of the results of the hybrid algorithm with those of other metaheuristic algorithms shows the efficiency of the hybrid algorithm.
Cheung, Ngaam J; Ding, Xue-Ming; Shen, Hong-Bin
Cuckoo search (CS) algorithm is a nature-inspired search algorithm, in which all the individuals have identical search behaviors. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome the drawback, this paper presents a new variant of CS algorithm with nonhomogeneous search strategies based on quantum mechanism to enhance search ability of the classical CS algorithm. Featured contributions in this paper include: 1) quantum-based strategy is developed for nonhomogeneous update laws and 2) we, for the first time, present a set of theoretical analyses on CS algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the CS algorithm and the proposed algorithm. On 24 benchmark functions, we compare our method with five existing CS-based methods and other ten state-of-the-art algorithms. The numerical results demonstrate that the proposed algorithm is significantly better than the original CS algorithm and the rest of compared methods according to two nonparametric tests.
Full Text Available The disadvantages of particle swarm optimization (PSO algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster.
SHEN Hong; WAN Jianru; ZHANG Zhichao; LIU Yingpei; LI Guangye
Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algo-rithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.
Full Text Available The model and algorithm of BP neural network optimized by expanded multichain quantum optimization algorithm with super parallel and ultra-high speed are proposed based on the analysis of the research status quo and defects of BP neural network to overcome the defects of overfitting, the random initial weights, and the oscillation of the fitting and generalization ability along with subtle changes of the network parameters. The method optimizes the structure of the neural network effectively and can overcome a series of problems existing in the BP neural network optimized by basic genetic algorithm such as slow convergence speed, premature convergence, and bad computational stability. The performance of the BP neural network controller is further improved. The simulation experimental results show that the model is with good stability, high precision of the extracted parameters, and good real-time performance and adaptability in the actual parameter extraction.
DAI Wei-bao; ZOU Ping-hua; FENG Ming-hua; DONG Zhan-shuang
To improve the thermal efficiency and reduce nitrogen oxides (NOx) emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function (RBF) artificial neural network (ANN) models for forecasting unburned carbon in fly ash and NOx emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell (PSO-Powell) algorithm.To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm,and the effectiveness of system. Its prospective application in the optimization of a pulverized coal (PC) fired boiler is presented as well.
Full Text Available The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA, Particle Swarm Optimization (PSO algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core.
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.
Huang, Shuqiang; Tao, Ming
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.
Huang, Shuqiang; Tao, Ming
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
Muhammad Farhan Ausaf
Full Text Available Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven’t been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA. PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources.
Lin, Kuan-Cheng; Hsieh, Yi-Hsiu
The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.
CHENQing-Geng; WANGNing; HUANGShao-Feng
Enlightened by distribution of creatures in natural ecology environment, the distribution population-based genetic algorithm (DPGA) is presented in this paper. The searching capability of the algorithm is improved by competition between distribution populations to reduce the search zone.This method is applied to design of optimal parameters of PID controllers with examples, and the simulation results show that satisfactory performances are obtained.
Liu, Xiaohuan; Liu, Yanhui
The study of this paper focuses on smart isolation structure, a method for realizing structural vibration control by using Simulink simulation is proposed according to the proposed sequential optimal control algorithm. In the Simulink simulation environment, A smart isolation structure is used to compare the control effect of three algorithms, i.e., classical optimal control algorithm, linear quadratic gaussian control algorithm and sequential optimal control algorithm under the condition of sensor contaminated with noise. Simulation results show that this method can be applied to the simulation of sequential optimal control algorithm and the proposed sequential optimal control algorithm has a good ability of resisting the noise and better control efficiency.
Mahmood, Shahid; Huang, Debo
Computational fluid dynamics (CFD) plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and engineers in the ship industry. In this paper, the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool. CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters, automatic generation of mesh, automatic analysis of fluid flow to calculate the required objective/cost function, and finally an optimization tool to evaluate the cost for optimization. In this paper, integration of a genetic algorithm program, written in MATLAB, was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT. Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters. These design variables were optimized to achieve a minimum cost function of "total resistance". Integration of a genetic algorithm with CFD tools proves to be effective for hull form optimization.
Shahid Mahmood; Debo Huang
Computational fluid dynamics (CFD) plays a major role in predicting the flow behavior of a ship.With the development of fast computers and robust CFD software,CFD has become an important tool for designers and engineers in the ship industry.In this paper,the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool.CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters,automatic generation of mesh,automatic analysis of fluid flow to calculate the required objective/cost function,and finally an optimization tool to evaluate the cost for optimization.In this paper,integration of a genetic algorithm program,written in MATLAB,was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT.Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters.These design variables were optimized to achieve a minimum cost function of “total resistance”.Integration of a genetic algorithm with CFD tools proves to be effective for hull form optimization.
Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.
Wu Zhi-jian; Tang Zhi-long; Kang Li-shan
This paper presents a parallel two level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions.By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optirma and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.
Wu, Qiong; Wang, Jihua; Wang, Cheng; Xu, Tongyu
Genetic algorithm (GA) has a significant effect in the band optimization selection of Partial Least Squares (PLS) correction model. Application of genetic algorithm in selection of characteristic bands can achieve the optimal solution more rapidly, effectively improve measurement accuracy and reduce variables used for modeling. In this study, genetic algorithm as a module conducted band selection for the application of hyperspectral imaging in nondestructive testing of corn seedling leaves, and GA-PLS model was established. In addition, PLS quantitative model of full spectrum and experienced-spectrum region were established in order to suggest the feasibility of genetic algorithm optimizing wave bands, and model robustness was evaluated. There were 12 characteristic bands selected by genetic algorithm. With reflectance values of corn seedling component information at spectral characteristic wavelengths corresponding to 12 characteristic bands as variables, a model about SPAD values of corn leaves acquired was established by PLS, and modeling results showed r = 0.7825. The model results were better than those of PLS model established in full spectrum and experience-based selected bands. The results suggested that genetic algorithm can be used for data optimization and screening before establishing the corn seedling component information model by PLS method and effectively increase measurement accuracy and greatly reduce variables used for modeling.
Ning, Xin; Yuan, Jianping; Yue, Xiaokui
A fractionated spacecraft is an innovative application of a distributive space system. To fully understand the impact of various uncertainties on its development, launch and in-orbit operation, we use the stochastic missioncycle cost to comprehensively evaluate the survivability, flexibility, reliability and economy of the ways of dividing the various modules of the different configurations of fractionated spacecraft. We systematically describe its concept and then analyze its evaluation and optimal design method that exists during recent years and propose the stochastic missioncycle cost for comprehensive evaluation. We also establish the models of the costs such as module development, launch and deployment and the impacts of their uncertainties respectively. Finally, we carry out the Monte Carlo simulation of the complete missioncycle costs of various configurations of the fractionated spacecraft under various uncertainties and give and compare the probability density distribution and statistical characteristics of its stochastic missioncycle cost, using the two strategies of timing module replacement and non-timing module replacement. The simulation results verify the effectiveness of the comprehensive evaluation method and show that our evaluation method can comprehensively evaluate the adaptability of the fractionated spacecraft under different technical and mission conditions.
CUI Xiao-fang; MA Jun; MENG Kai; ZHAO Wen-zhong; ZHAO Hai-yan
In this article, The genetic algorithm method was proposed, that is, to establish the box structure's nonlinear three-dimension optimization numerical model based on thermo-mechanical coupling algorithm, and the objective function of welding distortion has been utilized to determine an optimum welding sequence by optimization simulation. The validity of genetic algorithm method combining with the thermo-mechanical nonlinear finite element model is verified by comparison with the experimental data where available. By choosing the appropriate objective function for the considered case, an optimum welding sequence is determined by a genetic algorithm. All done in this study indicates that the new method presented in this article will have important practical application for designing the welding technical parameters in the future.
Baolin Sun; Shangchao Pi; Chao Gui; Yue Zeng; Bing Yan; Wenxiang Wang; Qianqing Qin
Usually multiple quality of service (QoS) guarantees are required in most multicast applications. This paper presents a multiple constraints algorithm for multicast traffic engineering in mobile ad hoc networks (MANET). The proposed algorithm is a new version of multiple constraints QoS multicast routing optimization algorithm in MANET based on genetic algorithm (MQMGA). The proposed MQMGA can optimize the maximum link utilization, the cost of the multicast tree, the selection of the long-life path, the average delay and the maximum end-to-end delay. Experimental result shows that the approach is efficient, has promising performance in multicast traffic engineering and for evaluating the route stability in dynamic mobile networks.
Khehra, Baljit Singh; Pharwaha, Amar Partap Singh
Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.
Full Text Available It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutionary optimization algorithms to determine the optimal parameters. Our results indicate that the combined model using a hybrid algorithm outperforms other methods for the fitting and forecasting processes in terms of mean absolute percentage error.
Full Text Available This article is devoted to the application of the cellular automata mathematical apparatus to the problem of continuous optimization. The cellular automaton with an objective function is introduced as a new modification of the classic cellular automaton. The algorithm of continuous optimization, which is based on dynamics of the cellular automaton having the property of geometric symmetry, is obtained. The results of the simulation experiments with the obtained algorithm on standard test functions are provided, and a comparison between the analogs is shown.
Jiang, Shouyong; Yang, Shengxiang
The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.
李肯立; 蒋盛益; 王卉; 李庆华
A new parallel algorithm is proposed for the knapsack problem where the method of divide and conquer is adopted. Based on an EREW-SIMD machine with shared memory, the proposed algorithm utilizes O(2n/4)1-ε processors, 0≤ε≤1, and O(2n/2) memory to find a solution for the n-element knapsack problem in time O(2n/4(2n/4)ε). The cost of the proposed parallel algorithm is O(2n/2), which is an optimal method for solving the knapsack problem without memory conflicts and an improved result over the past researches.
Full Text Available Today, automatic warehouse system gradually replaced manual labor, and played an important role in the production work, especially in the cargo handling work. It was important to research the time-consuming and efficiency of stacker in the automated warehouse system. This paper researched the path of stacker in automated warehouse and calculated the operation time of stacker working path according to actual working condition, and then put forward a route optimization method of stacker based on genetic algorithm, finally simulated this algorithm by using MATLAB. The simulation results showed that this algorithm could shorten the way of stacker, and increase the working efficiency of the warehouse.
Full Text Available To resolve the problems that cloud particle swarm optimization(CPSO was easily trapped in local minimum and possessed slow convergence speed and early-maturing during distribution grid reactive power optimization, CPSO algorithm was improved based on cloud digital features in this paper. The method firstly combined Local search with global search together based on solution space transform, where the crossover and mutation operation of the particles were implemented based on normal cloud operator. And then the dramatic achievements were acquired in time-consuming and storage-cost using the improved algorithm. Finally, applied in bus IEEE30 system, the simulation results show that the better global solution is attained using the improved CPSO algorithm, and its convergence speed and accuracy possesses a dramatic improvement.
Xu Chen; Wenli Du; Feng Qian
Dynamic optimization problems (DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are in-valid. In this article, a technology named ranking-based mutation operator (RMO) is presented to enhance the previous differential evolution (DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
Full Text Available In this study, it explores the procedures and methods of the system for establishing food sensory evaluation based on particle swarm optimization algorithm, by means of explaining the interpretation of sensory evaluation and sensory analysis, combined with the applying situation of sensory evaluation in food industry.
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.;
. Accordingly, this paper proposes a dynamic consensus algorithm based distributed optimization method aiming at improving the system efficiency while offering higher expandability and flexibility when compared to centralized control. Hardware-in-the-loop (HIL) results are shown to demonstrate the effectiveness...
Pedersen, C.; Thoft-Christensen, Palle
Guidelines for interactive reliability-based structural optimization problems are outlined in terms of modifications of standard quasi-Newton algorithms. The proposed modifications minimize the condition number of the approximate Hessian matrix in each iteration, restrict the relative and absolute...
Full Text Available Life testing plans are more vital for carrying out researches on reliability and survival analysis. The inadequacy in the number of testing units or the timing limitations prevents the experiment from being continued until all the failures are detected. Hence, censoring grows to be an inheritably important and well-organized methodology for estimating the model parameters of underlying distributions. Type I and II censoring schemes are the most widely employed censoring schemes. The chief problem associated with the designing of life testing experiments practically is the determination of optimum censoring scheme. Hence, this study attempts to determine the optimum censoring through the minimization of total cost spent for the experiment, consuming less termination time and reasonable number of failures. The ABC algorithm is being employed in this study for obtaining the optimal censoring schemes. Entropy and variance serves as the optimal criterion. The proposed method utilizes Risk analysis to evaluate the efficiency or reliability of the optimal censoring scheme that is being determined. Optimum censoring scheme indicates the process of determining the best scheme from among the entire censoring schemes possible, in accordance to a specific optimality criterion.
Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul
This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.
Ling Ai Wong
Full Text Available This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.
A partial distortion theorem based channel-optimized vector quantization(COVQ)design algorithm using the evolutionary algorithm on noisy algorithm is introduced into the design of COVQ to achieve a significant improvement of vector quantization(VQ)performance for given noisy channel status.The evolutionary strategy is utilized to adjust the subdistortion of each region determined by each codevector in order to improve the total expected distortion.Finally,compared with other conventional codebook design algorithms,the presented algorithm better adjusts the subdistortion of each region and achieves significant gains in average distortion due to hannel errors,over other conventional VQ design methods,as confirmed by the experimental results.
Chen Xiaofang; Gui Weihua; Wang Yalin
Considering premature convergence in the searching process of genetic algorithm, a chaotic migration-based pseudo parallel genetic algorithm (CMPPGA) is proposed, which applies the idea of isolated evolution and information exchanging in distributed Parallel Genetic Algorithm by serial program structure to solve optimization problem of low real-time demand. In this algorithm,asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence. Infcrmation exchanging among sub-populations is ensured to be efficient and sufficient due to that the sequence is ergodic and stochastic. Simulation study of CMPPGA shows its strong global search ability, superiority to standard genetic algorithm and high immunity against premature convergence. According to the practice of raw material supply, an inventory prcgramming model is set up and solved by CMPPGA with satisfactory results returned.
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning
Full Text Available In this paper we present the design and implementation of a generic GA-based optimization framework iMASKO (iNL@MATLAB Genetic Algorithm-based Sensor NetworK Optimizer to optimize the performance metrics of wireless sensor networks. Due to the global search property of genetic algorithms, the framework is able to automatically and quickly fine tune hundreds of possible solutions for the given task to find the best suitable tradeoff. We test and evaluate the framework by using it to explore a SystemC-based simulation process to tune the configuration of the unslotted CSMA/CA algorithm of IEEE 802.15.4, aiming to discover the most available tradeoff solutions for the required performance metrics. In particular, in the test cases different sensor node platforms are under investigation. A weighted sum based cost function is used to measure the optimization effectiveness and capability of the framework. In the meantime, another experiment is performed to test the framework’s optimization characteristic in multi-scenario and multi-objectives conditions.
E. E. Miandoab
Full Text Available The inherent uncertainty to factors such as technology and creativity in evolving software development is a major challenge for the management of software projects. To address these challenges the project manager, in addition to examining the project progress, may cope with problems such as increased operating costs, lack of resources, and lack of implementation of key activities to better plan the project. Software Cost Estimation (SCE models do not fully cover new approaches. And this lack of coverage is causing problems in the consumer and producer ends. In order to avoid these problems, many methods have already been proposed. Model-based methods are the most familiar solving technique. But it should be noted that model-based methods use a single formula and constant values, and these methods are not responsive to the increasing developments in the field of software engineering. Accordingly, researchers have tried to solve the problem of SCE using machine learning algorithms, data mining algorithms, and artificial neural networks. In this paper, a hybrid algorithm that combines COA-Cuckoo optimization and K-Nearest Neighbors (KNN algorithms is used. The so-called composition algorithm runs on six different data sets and is evaluated based on eight evaluation criteria. The results show an improved accuracy of estimated cost.
CHEN Xun-xun; FANG Bin-xing
Because the small CACHE size of computers, the scanning speed of DFA based multi-pattern stringmatching algorithms slows down rapidly especially when the number of patterns is very large. For solving such problems, we cut down the scanning time of those algorithms (i.e. DFA based) by rearranging the states table and shrinking the DFA alphabet size. Both the methods can decrease the probability of large-scale random memory accessing and increase the probability of continuously memory accessing. Then the hitting rate of the CACHE is increased and the searching time of on the DFA is reduced. Shrinking the alphabet size of the DFA also reduces the storage complication. The AC + + algorithm, by optimizing the Aho-Corasick ( i. e. AC) algorithm using such methods, proves the theoretical analysis. And the experimentation results show that the scanning time of AC + + and the storage occupied is better than that of AC in most cases and the result is much attractive when the number of patterns is very large. Because DFA is a widely used base algorithm in may string matching algorithms, such as DAWG, SBOM etc. , the optimizing method discussed is significant in practice.
Zhang, Jiawei; Tang, Ming; Shi, Jun; Fu, Songnian; Li, Lihua; Liu, Ying; Cheng, Xueping; Liu, Jian; Shum, Ping
Although the Master Oscillator Power-Amplifier (MOPA) based fiber laser has received much attention for laser marking process due to its large tunabilty of pulse duration (from 10ns to 1ms), repetition rate (100Hz to 500kHz), high peak power and extraordinary heat dissipating capability, the output pulse deformation due to the saturation effect of fiber amplifier is detrimental for many applications. We proposed and demonstrated that, by utilizing Genetic algorithm (GA) based optimization technique, the input pulse profile from the master oscillator (current-driven laser diode) could be conveniently optimized to achieve targeted output pulse shape according to real parameters' constraints. In this work, an Yb-doped high power fiber amplifier is considered and a 200ns square shaped pulse profile is the optimization target. Since the input pulse with longer leading edge and shorter trailing edge can compensate the saturation effect, linear, quadratic and cubic polynomial functions are used to describe the input pulse with limited number of unknowns(<5). Coefficients of the polynomial functions are the optimization objects. With reasonable cost and hardware limitations, the cubic input pulse with 4 coefficients is found to be the best as the output amplified pulse can achieve excellent flatness within the square shape. Considering the bandwidth constraint of practical electronics, we examined high-frequency component cut-off effect of input pulses and found that the optimized cubic input pulses with 300MHz bandwidth is still quite acceptable to satisfy the requirement for the amplified output pulse and it is feasible to establish such a pulse generator in real applications.
Norlina Mohd Sabri
Full Text Available This research is focusing on the radio frequency (RF magnetron sputtering process, a physical vapor deposition technique which is widely used in thin film production. This process requires the optimized combination of deposition parameters in order to obtain the desirable thin film. The conventional method in the optimization of the deposition parameters had been reported to be costly and time consuming due to its trial and error nature. Thus, gravitational search algorithm (GSA technique had been proposed to solve this nano-process parameters optimization problem. In this research, the optimized parameter combination was expected to produce the desirable electrical and optical properties of the thin film. The performance of GSA in this research was compared with that of Particle Swarm Optimization (PSO, Genetic Algorithm (GA, Artificial Immune System (AIS and Ant Colony Optimization (ACO. Based on the overall results, the GSA optimized parameter combination had generated the best electrical and an acceptable optical properties of thin film compared to the others. This computational experiment is expected to overcome the problem of having to conduct repetitive laboratory experiments in obtaining the most optimized parameter combination. Based on this initial experiment, the adaptation of GSA into this problem could offer a more efficient and productive way of depositing quality thin film in the fabrication process.
Full Text Available Mitigation of global warming gases from burning gasoline for transportation in vehicles is one of the biggest and most complex issues the world has ever faced. In an intention to eradicate the environmental crisis caused due to global warming, electric vehicles were been introduced that are powered by electric motor which works on the energy stored in a battery pack. Inspired by the research on power management in electric vehicles, this paper focuses on the development of an energy management system for electric vehicles (EMSEV to optimally balance the energy from battery pack. The proposed methodology uses firefly optimization algorithm to optimize the power consumption of the devices like electric motor, power steering, air conditioner, power window, automatic door locks, radio, speaker, horn, wiper, GPS, internal and external lights etc., from the battery in electric vehicles. Depending upon the distance to cover and the battery availability, the devices are made to switch down automatically through dynamic EDF scheduling. CAN protocol is used for effective communication between the devices and the controller. Simulation results are obtained using MATLAB.
Zhang, Yanjun; Zhao, Yu; Fu, Xinghu; Xu, Jinrui
A novel particle swarm optimization algorithm based on adaptive inertia weight and chaos optimization is proposed for extracting the features of Brillouin scattering spectra. Firstly, the adaptive inertia weight parameter of the velocity is introduced to the basic particle swarm algorithm. Based on the current iteration number of particles and the adaptation value, the algorithm can change the weight coefficient and adjust the iteration speed of searching space for particles, so the local optimization ability can be enhanced. Secondly, the logical self-mapping chaotic search is carried out by using the chaos optimization in particle swarm optimization algorithm, which makes the particle swarm optimization algorithm jump out of local optimum. The novel algorithm is compared with finite element analysis-Levenberg Marquardt algorithm, particle swarm optimization-Levenberg Marquardt algorithm and particle swarm optimization algorithm by changing the linewidth, the signal-to-noise ratio and the linear weight ratio of Brillouin scattering spectra. Then the algorithm is applied to the feature extraction of Brillouin scattering spectra in different temperatures. The simulation analysis and experimental results show that this algorithm has a high fitting degree and small Brillouin frequency shift error for different linewidth, SNR and linear weight ratio. Therefore, this algorithm can be applied to the distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can effectively improve the accuracy of Brillouin frequency shift extraction.
Wenjing Du; Peili Wang; Lipeng Song; Lin Cheng
A conduction heat transfer process is enhanced by filling prescribed quantity and optimized-shaped high thermal conductivity materials to the substrate. Numerical simulations and analyses are performed on a volume to point conduction problem based on the principle of minimum entropy generation. In the optimization, the arrange-ment of high thermal conductivity materials is variable, the quantity of high thermal-conductivity material is constrained, and the objective is to obtain the maximum heat conduction rate as the entropy is the minimum. A novel algorithm of thermal conductivity discretization is proposed based on large quantity of calculations. Compared with other algorithms in literature, the average temperature in the substrate by the new algorithm is lower, while the highest temperature in the substrate is in a reasonable range. Thus the new algorithm is fea-sible. The optimization of volume to point heat conduction is carried out in a rectangular model with radiation boundary condition and constant surface temperature boundary condition. The results demonstrate that the al-gorithm of thermal conductivity discretization is applicable for volume to point heat conduction problems.
Arteaga-Sierra, F R; Torres-Gómez, I; Torres-Cisneros, M; Moltó, G; Ferrando, A
We present a numerical strategy to design fiber based dual pulse light sources exhibiting two predefined spectral peaks in the anomalous group velocity dispersion regime. The frequency conversion is based on the soliton fission and soliton self-frequency shift occurring during supercontinuum generation. The optimization process is carried out by a genetic algorithm that provides the optimum input pulse parameters: wavelength, temporal width and peak power. This algorithm is implemented in a Grid platform in order to take advantage of distributed computing. These results are useful for optical coherence tomography applications where bell-shaped pulses located in the second near-infrared window are needed.
Yang, Dixiong; Liu, Zhenjun; Zhou, Jilei
Chaos optimization algorithms (COAs) usually utilize the chaotic map like Logistic map to generate the pseudo-random numbers mapped as the design variables for global optimization. Many existing researches indicated that COA can more easily escape from the local minima than classical stochastic optimization algorithms. This paper reveals the inherent mechanism of high efficiency and superior performance of COA, from a new perspective of both the probability distribution property and search speed of chaotic sequences generated by different chaotic maps. The statistical property and search speed of chaotic sequences are represented by the probability density function (PDF) and the Lyapunov exponent, respectively. Meanwhile, the computational performances of hybrid chaos-BFGS algorithms based on eight one-dimensional chaotic maps with different PDF and Lyapunov exponents are compared, in which BFGS is a quasi-Newton method for local optimization. Moreover, several multimodal benchmark examples illustrate that, the probability distribution property and search speed of chaotic sequences from different chaotic maps significantly affect the global searching capability and optimization efficiency of COA. To achieve the high efficiency of COA, it is recommended to adopt the appropriate chaotic map generating the desired chaotic sequences with uniform or nearly uniform probability distribution and large Lyapunov exponent.
Mahdi M. M. El-Arini
Full Text Available In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM, then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.
Full Text Available Three-dimensional path planning for underwater vehicles is an important problem that focuses on optimizing the route with consideration of various constraints in a complex underwater environment. In this paper, an improved ant colony optimization (IACO algorithm based on pheromone exclusion is proposed to solve the underwater vehicle 3D path planning problem. The IACO algorithm can balance the tasks of exploration and development in the ant search path, and enable the ants in the search process to explore initially and develop subsequently. Then, the underwater vehicle can find the safe path by connecting the chosen nodes of the 3D mesh while avoiding the threat area. This new approach can overcome common disadvantages of the basic ant colony algorithm, such as falling into local extremum, poor quality, and low accuracy. Experimental comparative results demonstrate that this proposed IACO method is more effective and feasible in underwater vehicle 3D path planning than the basic ACO model.
XIAO Wen-yong; WANG Feng; ZHUO Bin
A novel regenerative braking algorithm based on regenerative torque optimization with emulate engine compression braking (EECB) was proposed to make effective and maximum use of brake energy in order to improve fuel economy. The actual brake oil pressure of driving wheel which is reduced by the amount of the regenerative braking force is supplied from the electronic hydraulic brake system. Regenerative torque optimization maximizes the actual regenerative power recuperation by energy storage component, and EECB is a useful extended type of regenerative braking. The simulation results show that actual regenerative power recuperation for the novel regenerative braking algorithm is more than using conventional one, and life-span of brake disks is prolonged for the novel algorithm.
Full Text Available Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results.
TANGHao; XIHong-Sheng; YINBao-Qun
Based on the theory of Markov performance potentials and neuro-dynamic programming(NDP) methodology, we study simulation optimization algorithm for a class of continuous timeMarkov decision processes (CTMDPs) under randomized stationary policies. The proposed algo-rithm will estimate the gradient of average cost performance measure with respect to policy param-eters by transforming a continuous time Markov process into a uniform Markov chain and simula-ting a single sample path of the chain. The goal is to look for a suboptimal randomized stationarypolicy. The algorithm derived here can meet the needs of performance optimization of many diffi-cult systems with large-scale state space. Finally, a numerical example for a controlled Markovprocess is provided.
Lim, Wee Loon; Wibowo, Antoni; Desa, Mohammad Ishak; Haron, Habibollah
The quadratic assignment problem (QAP) is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO), a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.
Wee Loon Lim
Full Text Available The quadratic assignment problem (QAP is an NP-hard combinatorial optimization problem with a wide variety of applications. Biogeography-based optimization (BBO, a relatively new optimization technique based on the biogeography concept, uses the idea of migration strategy of species to derive algorithm for solving optimization problems. It has been shown that BBO provides performance on a par with other optimization methods. A classical BBO algorithm employs the mutation operator as its diversification strategy. However, this process will often ruin the quality of solutions in QAP. In this paper, we propose a hybrid technique to overcome the weakness of classical BBO algorithm to solve QAP, by replacing the mutation operator with a tabu search procedure. Our experiments using the benchmark instances from QAPLIB show that the proposed hybrid method is able to find good solutions for them within reasonable computational times. Out of 61 benchmark instances tested, the proposed method is able to obtain the best known solutions for 57 of them.
Full Text Available In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO algorithm and artificial bee colony (ABC algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.
Wang, Jie-sheng; Li, Shu-xia; Song, Jiang-di
In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird's nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.
谭冠政; 肖宏峰; 王越超
A new kind of optimal fuzzy PID controller is proposed, which contains two parts. One is an on-line fuzzy inference system, and the other is a conventional PID controller. In the fuzzy inference system, three adjustable factors xp, xi, and xd are introduced. Their functions are to further modify and optimize the result of the fuzzy inference so as to make the controller have the optimal control effect on a given object. The optimal values of these adjustable factors are determined based on the ITAE criterion and the Nelder and Mead′s flexible polyhedron search algorithm. This optimal fuzzy PID controller has been used to control the executive motor of the intelligent artificial leg designed by the authors. The result of computer simulation indicates that this controller is very effective and can be widely used to control different kinds of objects and processes.
A new superstructure from of heat exchanger networks(HEN) is proposed based on expert system (ES). The new superstructure from is combined with the practical engineering.The different investment cost formula for different heat exchanger is also presented based on ES.The mathematical model for the simultaneous optimization of network configuration is established and solved by a genetic algorithm.This method can deal with larger scale HEN synthesis and the optimal HEN configuration is obtained automatically.Finally,a case study is presented to demonstrate the effectiveness of the method.
Ausaf, Muhammad Farhan; Gao, Liang; Li, Xinyu
For increasing the overall performance of modern manufacturing systems, effective integration of process planning and scheduling functions has been an important area of consideration among researchers. Owing to the complexity of handling process planning and scheduling simultaneously, most of the research work has been limited to solving the integrated process planning and scheduling (IPPS) problem for a single objective function. As there are many conflicting objectives when dealing with process planning and scheduling, real world problems cannot be fully captured considering only a single objective for optimization. Therefore considering multi-objective IPPS (MOIPPS) problem is inevitable. Unfortunately, only a handful of research papers are available on solving MOIPPS problem. In this paper, an optimization algorithm for solving MOIPPS problem is presented. The proposed algorithm uses a set of dispatching rules coupled with priority assignment to optimize the IPPS problem for various objectives like makespan, total machine load, total tardiness, etc. A fixed sized external archive coupled with a crowding distance mechanism is used to store and maintain the non-dominated solutions. To compare the results with other algorithms, a C-matric based method has been used. Instances from four recent papers have been solved to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed method is an efficient approach for solving the MOIPPS problem.
YANG Jian-qiu; WANG Yan-rong
Several structural design parameters for the description of the geometric features of a hollow fan blade were determined. A structural design optimization model of a hollow fan blade which based on the strength constraint and minimum mass was established based on the finite element method through these parameters. Then, the sequential quadratic programming algorithm was employed to search the optimal solutions. Several groups of value for initial design variables were chosen, for the purpose of not only finding much more local optimal results but also analyzing which discipline that the variables according to could be benefit for the convergence and robustness. Response surface method and Monte Carlo simulations were used to analyze whether the objective function and constraint function are sensitive to the variation of variables or not. Then the robust results could be found among a group of different local optimal solutions.
Popkov, Yuri S.; Darkhovskiy, Boris S.; Popkov, Alexey Y.
A new method for global optimization of Hölder goal functions under compact sets given by inequalities is proposed. All functions are defined only algorithmically. The method is based on performing simple Monte Carlo trials and constructing the sequences of records and the sequence of their decrements. An estimating procedure of Hölder constants is proposed. Probability estimation of exact global minimum neighborhood using Hölder constants estimates is presented. Results on some analytical and algorithmic test problems illustrate the method's performance.
Full Text Available Optimizing Route for Hazardous Materials Logistics (ORHML belongs to a class of problems referred to as NP-Hard, and a strict constraint of it makes it harder to solve. In order to dealing with ORHML, an improved hybrid ant colony algorithm (HACA was devised. To achieve the purpose of balancing risk and cost for route based on the principle of ACA that used to solve TSP, the improved HACA was designed. Considering the capacity of road network and the maximum expected risk limits, a route optimization model to minimize the total cost is established based on network flow theory. Improvement on route construction rule and pheromone updating rule was adopted on the basis of the former algorithm. An example was analyzed to demonstrate the correctness of the application. It is proved that improved HACA is efficient and feasible in solving ORHML.
Full Text Available Emergency rescues require that first responders provide support to evacuate injured and other civilians who are obstructed by the hazards. In this case, the emergency personnel can take actions strategically in order to rescue people maximally, efficiently and quickly. The paper studies the effectiveness of a random neural network (RNN-based task assignment algorithm involving optimally matching emergency personnel and injured civilians, so that the emergency personnel can aid trapped people to move towards evacuation exits in real-time. The evaluations are run on a decision support evacuation system using the Distributed Building Evacuation Simulator (DBES multi-agent platform in various emergency scenarios. The simulation results indicate that the RNN-based task assignment algorithm provides a near-optimal solution to resource allocation problems, which avoids resource wastage and improves the efficiency of the emergency rescue process.
Wang, Jiabiao; Wang, Zhongbin; Xu, Jing; Tan, Chao; Si, Lei
Due to the inaccurate and unreliable moving distance measurement of the hydraulic support in mines, a method based on the random circle detection (RCD) algorithm and the fruit fly optimization algorithm (FOA) is proposed. According to the changing center and radium of the circle on the support, the relative position of adjacent supports is acquired by the camera. The noise of the collected image is moved, and the edge feature is protected using a bilateral filter. A local adaptive threshold algorithm is used for binary processing of the image. Then, RCD is used to detect the contour, which is similar to the circle. A method to detect the circle based on FOA is used to accurately detect the circle. Subsequently, the relative distance is calculated according to the change of the circle. Finally, the accuracy and reliability of the proposed method are verified though the experiment.
LIU Zi-ping; LI Li-xin
Based on the niche genetic algorithm, the intelligent and optimizing model for the rolling force distribution in hot strip mills was put forward. The research showed that the model had many advantages such as fast searching speed, high calculating pre-cision and suiting for on-line calculation. A good strip shape could be achieved by using the model and it is appropriate and practica-ble for rolling producing.
Bima Sena Bayu Dewantara
Full Text Available Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Intuitively and trial‐error determination of fuzzy rule is very difficult. This paper addresses the problem of optimizing Fuzzy rule using Genetic Algorithm to compensate illumination effect in face recognition. Since uneven illumination contributes negative effects to the performance of face recognition, those effects must be compensated. We have developed a novel algorithmbased on a reflectance model to compensate the effect of illumination for human face recognition. We build a pair of model from a single image and reason those modelsusing Fuzzy.Fuzzy rule, then, is optimized using Genetic Algorithm. This approachspendsless computation cost by still keepinga high performance. Based on the experimental result, we can show that our algorithm is feasiblefor recognizing desired person under variable lighting conditions with faster computation time. Keywords: Face recognition, harsh illumination, reflectance model, fuzzy, genetic algorithm
Hu, Xingzhi; Chen, Xiaoqian; Zhao, Yong; Yao, Wen
The separation systems are crucial for the launch of satellites. With respect to the existing design issues of satellite separation systems, an optimization design approach based on Multi-Island Genetic Algorithm is proposed, and a hierarchical optimization of system mass and separation angular velocity is designed. Multi-Island Genetic Algorithm is studied for the problem and the optimization parameters are discussed. Dynamic analysis of ADAMS used to validate the designs is integrated with iSIGHT. Then the optimization method is employed for a typical problem using the helical compression spring mechanism, and the corresponding objective functions are derived. It turns out that the mass of compression spring catapult is decreased by 30.7% after optimization and the angular velocity can be minimized considering spring stiffness errors. Moreover, ground tests and on-orbit flight indicate that the error of separation speed is controlled within 1% and the angular velocity is reduced by nearly 90%, which proves the design result and the optimization approach.
Full Text Available As the energy conservation and emission reduction and sustainable development have become the hot topics in the world, low carbon issues catch more and more attention. Logistics, which is one of the important economic activities, plays a crucial role in the low carbon development. Logistics leads to some significant issues about consuming energy and carbon emissions. Therefore, reducing energy consumption and carbon emissions has become the inevitable trend for logistics industry. Low carbon logistics is introduced in these situations. In this paper, from the microcosmic aspects, we will bring the low carbon idea in the path optimization issues and change the amount of carbon emissions into carbon emissions cost to establish the path optimization model based on the optimization objectives of the lowest cost of carbon emissions. According to different levels of air pollution, we will establish the double objectives path optimization model with the consideration of carbon emissions cost and economy cost. Use DNA-ant colony algorithm to optimize and simulate the model. The simulation indicates that DNA-ant colony algorithm could find a more reasonable solution for low carbon logistics path optimization problems.
Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.
Eisenhammer, T; Lazarov, M; Leutbecher, M; Schöffel, U; Sizmann, R
In the optimization of multilayer stacks for various optical filtering purposes not only the thicknesses of the thin films are to be optimized, but also the sequence of materials. Materials with very different optical properties, such as metals and dielectrics, may be combined. A genetic algorithm is introduced to search for the optimal sequence of materials along with their optical thicknesses. This procedure is applied to a heat mirror in combination with a blackbody absorber for thermal solar energy applications at elevated temperatures (250 °C). The heat mirror is based on silver films with antireflective dielectric layers. Seven dielectrics have been considered. For a five-layer stack the sequence (TiO(2)/Ag/TiO(2)/Ag/Y(2)O(3)) is found to be optimal.
In order to avoid such problems as low convergent speed and local optimal solution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In this algorithm, a mutative scale chaos optimization strategy is operated on the population after a genetic operation. And according to the searching process, the searching space of the optimal variables is gradually diminished and the regulating coefficient of the secondary searching process is gradually changed which will lead to the quick evolution of the population. The algorithm has such advantages as fast search, precise results and convenient using etc. The simulation results show that the performance of the method is better than that of simple genetic algorithms.
Full Text Available Plug-in hybrid electric vehicles (PHEVs have been recognized as one of the most promising vehicle categories nowadays due to their low fuel consumption and reduced emissions. Energy management is critical for improving the performance of PHEVs. This paper proposes an energy management approach based on a particle swarm optimization (PSO algorithm. The optimization objective is to minimize total energy cost (summation of oil and electricity from vehicle utilization. A main drawback of optimal strategies is that they can hardly be used in real-time control. In order to solve this problem, a rule-based strategy containing three operation modes is proposed first, and then the PSO algorithm is implemented on four threshold values in the presented rule-based strategy. The proposed strategy has been verified by the US06 driving cycle under the MATLAB/Simulink software environment. Two different driving cycles are adopted to evaluate the generalization ability of the proposed strategy. Simulation results indicate that the proposed PSO-based energy management method can achieve better energy efficiency compared with traditional blended strategies. Online control performance of the proposed approach has been demonstrated through a driver-in-the-loop real-time experiment.
YANG YongMing; XU Chao
As an elegant generalization of wavelet transform, wavelet packet (WP) provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quantization scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality.
Full Text Available Intelligent bionic leg (IBL is an advanced prosthesis which can maximum functionally simulate and approach the motion trajectory of human leg. Knee joint is the most important bone of human leg and its bionic design has great significance to prosthesis performance. The structural components of IBL are introduced and virtual prototype is given. The advantages of 4-bar knee joint are analyzed and are adopted in IBL design. The kinematics model of 4-bar knee joint is established. The objective function, constraint condition, parameters selection and setting of genetic algorithm are discussed in detail. Based on genetic algorithm, the optimization design of IBL knee joint is done. The optimization results indicate that the 4-bar mechanism can achieve better anthropomorphic characteristics of human knee joint.
陈义保; 姚建初; 钟毅芳
Identical parallel machine scheduling problem for minimizing the makespan is a very important productionscheduling problem. When its scale is large, many difficulties will arise in the course of solving identical parallel machinescheduling problem. Ant system based optimization algorithm (ASBOA) has shown great advantages in solving thecombinatorial optimization problem in view of its characteristics of high efficiency and suitability for practical applications.In this paper, an ASBOA for minimizing the makespan in identical machine scheduling problem is presented. Twodifferent scale numerical examples demonstrate that the ASBOA proposed is efficient and fit for large-scale identicalparallel machine scheduling problem for minimizing the makespan, the quality of its solution has advantages over heuristicprocedure and simulated annealing method, as well as genetic algorithm.
Full Text Available The inbound and outbound operation of plate yards in shipyards lacks effective scheduling with high operation costs. Based on the analysis of steel-in and steel-out operation process, an optimization model aiming to minimize the operation cost was established. The model was formulated as a multi-level combinatorial optimization model, which is finding proper storage locations during the steel-in stage to minimize the cost during the steel-out stage. Furthermore, greedy algorithm was implemented to solve this problem. Finally, application data obtained from a shipyard was used to validate the model, and the result shows that the proposed algorithm is effective to solve the steel stockyards scheduling problem.
Geng, Ying; Xiang, Bing-ren; He, Lan
In this paper, net analysis signal (NAS)-based concept was introduced to the analysis of multi-component Ginkgo biloba leaf extracts. NAS algorithm was utilized for the preprocessing of spectra, and NAS-based two-dimensional correlation analysis was used for the optimization of NIR model building. Simultaneous quantitative models for three flavonol aglycones: quercetin, keampferol and isorhamnetin were established respectively. The NAS vectors calculated using two algorithms introduced from Lorber and Goicoechea and Olivieri (HLA/GO) were applied in the development of calibration models, the reconstructed spectra were used as input of PLS modeling. For the first time, NAS-based two-dimensional correlation spectroscopy was used for wave number selection. The regions appeared in the main diagonal were selected as useful regions for model building. The results implied that two NAS-based preprocessing methods were successfully used for the analysis of quercetin, keampferol and isorhamnetin with a decrease of factor number and an improvement of model robustness. NAS-based algorithm was proven to be a useful tool for the preprocessing of spectra and for optimization of model calibration. The above research showed a practical application value for the NIRS in the analysis of complex multi-component petrochemical medicine with unknown interference.
Meghini, Carlo; Analyti, Anastasia
In this study, we address the problem of answering queries over a peer-to-peer system of taxonomy-based sources. A taxonomy states subsumption relationships between negation-free DNF formulas on terms and negation-free conjunctions of terms. To the end of laying the foundations of our study, we first consider the centralized case, deriving the complexity of the decision problem and of query evaluation. We conclude by presenting an algorithm that is efficient in data complexity and is based on hypergraphs. More expressive forms of taxonomies are also investigated, which however lead to intractability. We then move to the distributed case, and introduce a logical model of a network of taxonomy-based sources. On such network, a distributed version of the centralized algorithm is then presented, based on a message passing paradigm, and its correctness is proved. We finally discuss optimization issues, and relate our work to the literature.
Full Text Available An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II, the multiobjective particle swarm optimizer (MOPSO, and the multiobjective ABC (MOABC. Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.
Full Text Available Wireless Sensor Networks (WSN represent a new dimension in the field of network research. The cluster algorithm can significantly reduce the energy consumption of wireless sensor networks and prolong the network lifetime. This paper uses neuron to describe the WSN node and constructs neural network model for WSN. The neural network model includes three aspects: WSN node neuron model, WSN node control model and WSN node connection model. Through learning the framework of cluster algorithm for wireless sensor networks, this paper presents a weighted average of cluster-head selection algorithm based on an improved Genetic Optimization which makes the node weights directly related to the decision-making predictions. The Algorithm consists of two stages: single-parent evolution and population evolution. The initial population is formed in the stage of single-parent evolution by using gene pool, then the algorithm continues to the next further evolution process, finally the best solution will be generated and saved in the population. The simulation results illustrate that the new algorithm has the high convergence speed and good global searching capacity. It is to effectively balance the network energy consumption, improve the network life-cycle, ensure the communication quality and provide a certain theoretical foundation for the applications of the neural networks.
周杰; 卓芳; 黄磊; 罗艳
To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental (CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model (RSM) was set up and the results of the analysis of variance (ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the“trial and error”procedure.
Jie-sheng Wang; Shu-xia Li; Jiang-di Song
In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-...
Abdeljaber, Osama; Avci, Onur; Inman, Daniel J.
One of the major challenges in civil, mechanical, and aerospace engineering is to develop vibration suppression systems with high efficiency and low cost. Recent studies have shown that high damping performance at broadband frequencies can be achieved by incorporating periodic inserts with tunable dynamic properties as internal resonators in structural systems. Structures featuring these kinds of inserts are referred to as metamaterials inspired structures or metastructures. Chiral lattice inserts exhibit unique characteristics such as frequency bandgaps which can be tuned by varying the parameters that define the lattice topology. Recent analytical and experimental investigations have shown that broadband vibration attenuation can be achieved by including chiral lattices as internal resonators in beam-like structures. However, these studies have suggested that the performance of chiral lattice inserts can be maximized by utilizing an efficient optimization technique to obtain the optimal topology of the inserted lattice. In this study, an automated optimization procedure based on a genetic algorithm is applied to obtain the optimal set of parameters that will result in chiral lattice inserts tuned properly to reduce the global vibration levels of a finite-sized beam. Genetic algorithms are considered in this study due to their capability of dealing with complex and insufficiently understood optimization problems. In the optimization process, the basic parameters that govern the geometry of periodic chiral lattices including the number of circular nodes, the thickness of the ligaments, and the characteristic angle are considered. Additionally, a new set of parameters is introduced to enable the optimization process to explore non-periodic chiral designs. Numerical simulations are carried out to demonstrate the efficiency of the optimization process.
Norlina, M. S.; Diyana, M. S. Nor; Mazidah, P.; Rusop, M.
In the RF magnetron sputtering process, the desirable layer properties are largely influenced by the process parameters and conditions. If the quality of the thin film has not reached up to its intended level, the experiments have to be repeated until the desirable quality has been met. This research is proposing Gravitational Search Algorithm (GSA) as the optimization model to reduce the time and cost to be spent in the thin film fabrication. The optimization model's engine has been developed using Java. The model is developed based on GSA concept, which is inspired by the Newtonian laws of gravity and motion. In this research, the model is expected to optimize four deposition parameters which are RF power, deposition time, oxygen flow rate and substrate temperature. The results have turned out to be promising and it could be concluded that the performance of the model is satisfying in this parameter optimization problem. Future work could compare GSA with other nature based algorithms and test them with various set of data.
Full Text Available In the multiple-reader environment (MRE of radio frequency identification (RFID system, multiple readers are often scheduled to interrogate the randomized tags via operating at different time slots or frequency channels to decrease the signal interferences. Based on this, a Geometric Distribution-based Multiple-reader Scheduling Optimization Algorithm using Artificial Immune System (GD-MRSOA-AIS is proposed to fairly and optimally schedule the readers operating from the viewpoint of resource allocations. GD-MRSOA-AIS is composed of two parts, where a geometric distribution function combined with the fairness consideration is first introduced to generate the feasible scheduling schemes for reader operation. After that, artificial immune system (including immune clone, immune mutation and immune suppression quickly optimize these feasible ones as the optimal scheduling scheme to ensure that readers are fairly operating with larger effective interrogation range and lower interferences. Compared with the state-of-the-art algorithm, the simulation results indicate that GD-MRSOA-AIS could efficiently schedules the multiple readers operating with a fairer resource allocation scheme, performing in larger effective interrogation range.
Lin Jiang; Ruolin Wu∗and Zhichao Zhu
The dynamic characteristics of hydraulic self servo swing cylinder were analyzed according to the hydraulic system natural frequency formula. Based on that, a method of the hydraulic self servo swing cylinder structure optimization based on genetic algorithm was proposed in this paper. By analyzing the four parameters that affect the dynamic characteristics, we had to optimize the structure to obtain as larger the Dm ( displacement) as possible under the condition with the purpose of improving the dynamic characteristics of hydraulic self servo swing cylinder. So three state equations were established in this paper. The paper analyzed the effect of the four parameters in hydraulic self servo swing cylinder natural frequency equation and used the genetic algorithm to obtain the optimal solution of structure parameters. The model was simulated by substituting the parameters and initial value to the simulink model. Simulation results show that: using self servo hydraulic swing cylinder natural frequency equation to study its dynamic response characteristics is very effective. Compared with no optimization, the overall system dynamic response speed is significantly improved.
Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.
Full Text Available Ant Colony Optimization (ACO is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.
According to the Wide Area Network model, we formulate Internet flow control as a constrained convex programming problem, where the objective is to maximize the total utility of all sources over their transmission rates. Based on this formulation, flow control can be converted to a normal unconstrained optimization problem through the barrier function method, so that it can be solved by means of a gradient projection algorithm with properly rate iterations. We prove that the algorithm converges to the global optimal point, which is also a stable proportional fair rate allocation point, provided that the step size is properly chosen. The main difficulty facing the realization of iteration algorithm is the distributed computation of congestion measure. Fortunately, Explicit Congestion Notification (ECN) is likely to be used to improve the performance of TCP in the near future. By using ECN, it is possible to realize the iteration algorithm in IP networks. Our algorithm is divided into two parts, algorithms in the router and in the source. The router marks the ECN bit with a probability that varies as its buffer occupancy varies, so that the congestion measure of links can be communicated to the source when the marked ECN bits are reflected back from its destination. Source rates are then updated by all sessions according to the received congestion measure. The main advantage of our scheme is its fast convergence ability and robustness; it can also provide the network with zero packet loss by properly choosing the queue threshold and provide differentiated service to users by applying different utility functions.
Jang, In Gwun; Kim, Il Yong; Kwak, Byung Ban
In bone-remodeling studies, it is believed that the morphology of bone is affected by its internal mechanical loads. From the 1970s, high computing power enabled quantitative studies in the simulation of bone remodeling or bone adaptation. Among them, Huiskes et al. (1987, "Adaptive Bone Remodeling Theory Applied to Prosthetic Design Analysis," J. Biomech. Eng., 20, pp. 1135-1150) proposed a strain energy density based approach to bone remodeling and used the apparent density for the characterization of internal bone morphology. The fundamental idea was that bone density would increase when strain (or strain energy density) is higher than a certain value and bone resorption would occur when the strain (or strain energy density) quantities are lower than the threshold. Several advanced algorithms were developed based on these studies in an attempt to more accurately simulate physiological bone-remodeling processes. As another approach, topology optimization originally devised in structural optimization has been also used in the computational simulation of the bone-remodeling process. The topology optimization method systematically and iteratively distributes material in a design domain, determining an optimal structure that minimizes an objective function. In this paper, we compared two seemingly different approaches in different fields-the strain energy density based bone-remodeling algorithm (biomechanical approach) and the compliance based structural topology optimization method (mechanical approach)-in terms of mathematical formulations, numerical difficulties, and behavior of their numerical solutions. Two numerical case studies were conducted to demonstrate their similarity and difference, and then the solution convergences were discussed quantitatively.
Full Text Available This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC using neural networks (NNs based on genetic algorithms (GAs for increasing the use of recycled aggregate (RA. NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that the proposed method, which is NN based on GA, is proper for optimizing appropriate mixing proportion of RAC. Also, this method would help the construction engineers to utilize the recycled aggregate and reduce the concrete waste in construction process.
Suresh Kumar Sudabattula
Full Text Available With increased demand of electrical energy, limited availability of fossil fuels and environmental concerns, it is necessary to consider renewable energy based generation in a power system network. Optimal allocation of renewable based distributed generators in the distribution system is a challenging task in the recent years. In this paper an effective technique is proposed for optimal allocation of solar based distributed generators in the distribution network using a Bat algorithm (BA is presented. The objective is to minimize power loss of radial distribution system. Different operating constraints related to the distribution network are considered. The stochastic nature of solar irradiance is modeled by using suitable probability distribution function (PDF. The proposed method is tested and validated on IEEE 33 bus test system.
Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing
An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.
<正>Dijkstra algorithm is a theoretical basis to solve transportation network problems of the shortest path, which has a wide range of application in path optimization. Through analyzing traditional Dijkstra algorithm,on account of the insufficiency of this algorithm in path optimization,this paper uses adjacency list and circular linked list with combination to store date,and through the improved quick sorting algorithm for weight sorting, accomplish a quick search to the adjacent node,and so an improved Dijkstra algorithm is got.Then apply it to the optimal path search,and make simulation analysis for this algorithm through the example,also verify the effectiveness of the proposed algorithm.
Li, Miqing; Yang, Shengxiang; Zheng, Jinhua; Liu, Xiaohui
The Euclidean minimum spanning tree (EMST), widely used in a variety of domains, is a minimum spanning tree of a set of points in space where the edge weight between each pair of points is their Euclidean distance. Since the generation of an EMST is entirely determined by the Euclidean distance between solutions (points), the properties of EMSTs have a close relation with the distribution and position information of solutions. This paper explores the properties of EMSTs and proposes an EMST-based evolutionary algorithm (ETEA) to solve multi-objective optimization problems (MOPs). Unlike most EMO algorithms that focus on the Pareto dominance relation, the proposed algorithm mainly considers distance-based measures to evaluate and compare individuals during the evolutionary search. Specifically, in ETEA, four strategies are introduced: (1) An EMST-based crowding distance (ETCD) is presented to estimate the density of individuals in the population; (2) A distance comparison approach incorporating ETCD is used to assign the fitness value for individuals; (3) A fitness adjustment technique is designed to avoid the partial overcrowding in environmental selection; (4) Three diversity indicators-the minimum edge, degree, and ETCD-with regard to EMSTs are applied to determine the survival of individuals in archive truncation. From a series of extensive experiments on 32 test instances with different characteristics, ETEA is found to be competitive against five state-of-the-art algorithms and its predecessor in providing a good balance among convergence, uniformity, and spread.
BP is a commonly used neural network training method, which has some disadvantages, such as local minima,sensitivity of initial value of weights, total dependence on gradient information. This paper presents some methods to train a neural network, including standard particle swarm optimizer (PSO), guaranteed convergence particle swarm optimizer (GCPSO), an improved PSO algorithm, and GCPSO-BP, an algorithm combined GCPSO with BP. The simulation results demonstrate the effectiveness of the three algorithms for neural network training.
With the rapid development of Internet, mobile networks and high-performance networking technology,multiple constrained QoS multicast routing optimization in networks with uncertain parameters has become a very important research issue in the areas of networks and distributed systems. It is also a challenging and hard problem to the next generation Internet and high-performance networks, and has attracted the interests of many people. This paper discusses the multiple constrained QoS multicast routing problem, which may deal with the delay, delay jitter,bandwidth and packet loss metrics, and describes a network model for researching the routing problem. The paper mainly presents multiple constrained QoS multicast routing algorithm (MCQMRA), a QoS multicast routing policy for Internet,mobile network or other high-performance networks, which is based on the genetic algorithm (GA) and can provide QoS-sensitive paths in a scalable and flexible wayin the network environment with uncertain parameters. The MCQMRA can also optimize the network resources such as bandwidth, delay, packet loss metrics and can converge to the optimal or near-optimal solution within few iterations, even for the network environment with uncertain parameters. Simulation results show that MCQMRA is an available approach to QoS multicast routing decision.
Xiao, Guang; Shi, Xu-li; An, Ping; Zhang, Zhao-yang; Gao, Ge; Teng, Guo-wei
Context-based Adaptive Binary Arithmetic Coding (CABAC) is a new entropy coding method presented in H.264/AVC that is highly efficient in video coding. In the method, the probability of current symbol is estimated by using the wisely designed context model, which is adaptive and can approach to the statistic characteristic. Then an arithmetic coding mechanism largely reduces the redundancy in inter-symbol. Compared with UVLC method in the prior standard, CABAC is complicated but efficiently reduce the bit rate. Based on thorough analysis of coding and decoding methods of CABAC, This paper proposed two methods, sub-table method and stream-reuse methods, to improve the encoding efficiency implemented in H.264 JM code. In JM, the CABAC function produces bits one by one of every syntactic element. Multiplication operating times after times in the CABAC function lead to it inefficient.The proposed algorithm creates tables beforehand and then produce every bits of syntactic element. In JM, intra-prediction and inter-prediction mode selection algorithm with different criterion is based on RDO(rate distortion optimization) model. One of the parameter of the RDO model is bit rate that is produced by CABAC operator. After intra-prediction or inter-prediction mode selection, the CABAC stream is discard and is recalculated to output stream. The proposed Stream-reuse algorithm puts the stream in memory that is created in mode selection algorithm and reuses it in encoding function. Experiment results show that our proposed algorithm can averagely speed up 17 to 78 MSEL higher speed for QCIF and CIF sequences individually compared with the original algorithm of JM at the cost of only a little memory space. The CABAC was realized in our progressive h.264 encoder.
Full Text Available In this paper the optimization of kinematics, which has great influence in performance of flapping foil propulsion, is investigated. The purpose of optimization is to design a flapping-wing micro aircraft with appropriate kinematics and aerodynamics features, making the micro aircraft suitable for transportation over large distance with minimum energy consumption. On the point of optimal design, the pitch amplitude, wing reduced frequency and phase difference between plunging and pitching are considered as given parameters and consumed energy, generated thrust by wings and lost power are computed using the 2D quasi-steady aerodynamic model and multi-objective genetic algorithm. Based on the thrust optimization, the increase in pitch amplitude reduces the power consumption. In this case the lost power increases and the maximum thrust coefficient is computed of 2.43. Based on the power optimization, the results show that the increase in pitch amplitude leads to power consumption increase. Additionally, the minimum lost power obtained in this case is 23% at pitch amplitude of 25°, wing reduced frequency of 0.42 and phase angle difference between plunging and pitching of 77°. Furthermore, the wing reduced frequency can be estimated using regression with respect to pitch amplitude, because reduced frequency variations with pitch amplitude is approximately a linear function.
Full Text Available Based on molecular dynamics simulation results, a model was developed for determining elastic properties of aluminum nanocomposites reinforced with silicon carbide particles. Also, two models for prediction of density and price of nanocomposites were suggested. Then, optimal volume fraction of reinforcement was obtained by genetic algorithm method for the least density and price, and the highest elastic properties. Based on optimization results, the optimum volume fraction of reinforcement was obtained equal to 0.44. For this optimum volume fraction, optimum Young’s modulus, shear modulus, the price and the density of the nanocomposite were obtained 165.89 GPa, 111.37 GPa, 8.75 $/lb and 2.92 gr/cm3, respectively.
Full Text Available Cognitive radio (CR is to detect the presence of primary users (PUs reliably in order to reduce theinterference to licensed communications. Genetic algorithms (GAs are well suited for CR optimizationproblems to increase efficiency of bandwidth utilization by manipulating its unused portions of theapparent spectrum. In this paper, a binary genetic algorithm (BGA-based soft fusion (SF scheme forcooperative spectrum sensing in cognitive radio network is proposed to improve detection performance andbandwidth utilization. The BGA-based optimization method is implemented at the fusion centre of a linearSF scheme to optimize the weighting coefficients vector to maximize global probability of detectionperformance. Simulation results and analyses confirm that the proposed scheme meets real timerequirements of cognitive radio spectrum sensing and it outperforms conventional natural deflectioncoefficient- (NDC-, modified deflection coefficient- (MDC-, maximal ratio combining- (MRC- and equalgain combining- (EGC- based SDF schemes as well as the OR-rule based hard decision fusion (HDF. Thepropose BGA scheme also converges fast and achieves the optimum performance, which means that BGAbasedmethod is efficient and quite stable also.
Wang, Deguang; Han, Baochang; Huang, Ming
Computer forensics is the technology of applying computer technology to access, investigate and analysis the evidence of computer crime. It mainly include the process of determine and obtain digital evidence, analyze and take data, file and submit result. And the data analysis is the key link of computer forensics. As the complexity of real data and the characteristics of fuzzy, evidence analysis has been difficult to obtain the desired results. This paper applies fuzzy c-means clustering algorithm based on particle swarm optimization (FCMP) in computer forensics, and it can be more satisfactory results.
ZHAO Zhibin; YAO Lan; YANG Xiaochun; LI Binyang; YU Ge
In this paper we propose a Filter-based Uniform Algorithm (FbUA) for optimizing top-k query in distributed networks, which has been a topic of much recent interest.The basic idea of FbUA is to set a filter at each node to prevent it from sending out the data with little chance to contribute to the top-k result.FbUA can gain exact answers to top-k query through two phrases of round-trip communications between query station and participant nodes.The experiment results show that FbUA reduces network bandwidth consumption dramatically.
Sultan, Tipu; Ahmad, Zeshan; Cho, Jinsoo
The choice for the arrangement of the UV lamps in a closed-conduit ultraviolet (CCUV) reactor significantly affects the performance. However, a systematic methodology for the optimal lamp arrangement within the chamber of the CCUV reactor is not well established in the literature. In this research work, we propose a viable systematic methodology for the lamp arrangement based on a genetic algorithm (GA). In addition, we analyze the impacts of the diameter, angle, and symmetry of the lamp arrangement on the reduction equivalent dose (RED). The results are compared based on the simulated RED values and evaluated using the computational fluid dynamics simulations software ANSYS FLUENT. The fluence rate was calculated using commercial software UVCalc3D, and the GA-based lamp arrangement optimization was achieved using MATLAB. The simulation results provide detailed information about the GA-based methodology for the lamp arrangement, the pathogen transport, and the simulated RED values. A significant increase in the RED values was achieved by using the GA-based lamp arrangement methodology. This increase in RED value was highest for the asymmetric lamp arrangement within the chamber of the CCUV reactor. These results demonstrate that the proposed GA-based methodology for symmetric and asymmetric lamp arrangement provides a viable technical solution to the design and optimization of the CCUV reactor.
Full Text Available To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT, this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.
Santhan Kumar Cherukuri
Full Text Available To harvest maximum amount of solar energy and to attain higher efficiency, photovoltaic generation (PVG systems are to be operated at their maximum power point (MPP under both variable climatic and partial shaded condition (PSC. From literature most of conventional MPP tracking (MPPT methods are able to guarantee MPP successfully under uniform shading condition but fails to get global MPP as they may trap at local MPP under PSC, which adversely deteriorates the efficiency of Photovoltaic Generation (PVG system. In this paper a novel MPPT based on Whale Optimization Algorithm (WOA is proposed to analyze analytic modeling of PV system considering both series and shunt resistances for MPP tracking under PSC. The proposed algorithm is tested on 6S, 3S2P and 2S3P Photovoltaic array configurations for different shading patterns and results are presented. To compare the performance, GWO and PSO MPPT algorithms are also simulated and results are also presented. From the results it is noticed that proposed MPPT method is superior to other MPPT methods with reference to accuracy and tracking speed. Article History: Received July 23rd 2016; Received in revised form September 15th 2016; Accepted October 1st 2016; Available online How to Cite This Article: Kumar, C.H.S and Rao, R.S. (2016 A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. Int. Journal of Renewable Energy Development, 5(3, 225-232. http://dx.doi.org/10.14710/ijred.5.3.225-232
Full Text Available Abstract- This article proposes a probabilistic frame built on Scenario fabrication to considerate the uncertainties in the finest action managing of Micro Grids MGs. The MG contains different recoverable energy resources such as Wind Turbine WT Micro Turbine MT Photovoltaic PV Fuel Cell FC and one battery as the storing device. The advised frame is based on scenario generation and Roulette wheel mechanism to produce different circumstances for handling the uncertainties of altered factors. It habits typical spreading role as a probability scattering function of random factors. The uncertainties which are measured in this paper are grid bid alterations cargo request calculating error and PV and WT yield power productions. It is well-intentioned to asset that solving the MG difficult for 24 hours of a day by considering diverse uncertainties and different constraints needs one powerful optimization method that can converge fast when it doesnt fall in local optimal topic. Simultaneously single Group Search Optimization GSO system is presented to vision the total search space globally. The GSO algorithm is instigated from group active of beasts. Also the GSO procedure one change is similarly planned for this algorithm. The planned context and way is applied o one test grid-connected MG as a typical grid.
An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amplitude response of the desired FIR filter and that of the designed by training the weights of neural networks, then obtains the impulse response of FIR digital filter . The convergence theorem of the neural networks algorithm is presented and proved,and the optimal design method is introduced by designing four kinds of FIR digital filters , i.e., low-pass, high-pass,bandpass , and band-stop FIR digital filter. The results of the amplitude responses show that attenuation in stop-bands is more than 60 dB with no ripple and pulse existing in pass-bands, and cutoff frequency of passband and stop-band is easily controlled precisely . The presented optimal design approach of high order FIR digital filter is significantly effective.
Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.
Full Text Available A thrust allocation method was proposed based on a hybrid optimization algorithm to efficiently and dynamically position a semisubmersible drilling rig. That is, the thrust allocation was optimized to produce the generalized forces and moment required while at the same time minimizing the total power consumption under the premise that forbidden zones should be taken into account. An optimization problem was mathematically formulated to provide the optimal thrust allocation by introducing the corresponding design variables, objective function, and constraints. A hybrid optimization algorithm consisting of a genetic algorithm and a sequential quadratic programming (SQP algorithm was selected and used to solve this problem. The proposed method was evaluated by applying it to a thrust allocation problem for a semisubmersible drilling rig. The results indicate that the proposed method can be used as part of a cost-effective strategy for thrust allocation of the rig.
Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M
One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.
Ahmed F. Mohamed
Full Text Available One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC. The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.
Zhu, Binqi; Gao, Yesheng; Wang, Kaizhi; Liu, Xingzhao
A computational method for suppressing clutter and generating clear microwave images of targets is proposed in this paper, which combines synthetic aperture radar (SAR) principles with recursive method and waveform design theory, and it is suitable for SAR for special applications. The nonlinear recursive model is introduced into the SAR operation principle, and the cubature Kalman filter algorithm is used to estimate target and clutter responses in each azimuth position based on their previous states, which are both assumed to be Gaussian distributions. NP criteria-based optimal waveforms are designed repeatedly as the sensor flies along its azimuth path and are used as the transmitting signals. A clutter suppression filter is then designed and added to suppress the clutter response while maintaining most of the target response. Thus, with fewer disturbances from the clutter response, we can generate the SAR image with traditional azimuth matched filters. Our simulations show that the clutter suppression filter significantly reduces the clutter response, and our algorithm greatly improves the SINR of the SAR image based on different clutter suppression filter parameters. As such, this algorithm may be preferable for special target imaging when prior information on the target is available.
Jamal Salahaldeen Majeed Alneamy
Full Text Available Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO algorithm and fuzzy wavelet neural network (FWNN for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI machine learning repository. The performance of the proposed method (TLBO_FWNN is estimated using K-fold cross validation based on mean square error (MSE, classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.
Anderson, Benjamin R; Gunawidjaja, Ray; Eilers, Hergen
One of the main limitations of utilizing optimal wavefront shaping in imaging and authentication applications is the slow speed of the optimization algorithms currently being used. To address this problem we develop a micro-genetic optimization algorithm ($\\mu$GA) for optimal wavefront shaping. We test the abilities of the $\\mu$GA and make comparisons to previous algorithms (iterative and simple-genetic) by using each algorithm to optimize transmission through an opaque medium. From our experiments we find that the $\\mu$GA is faster than both the iterative and simple-genetic algorithms and that both genetic algorithms are more resistant to noise and sample decoherence than the iterative algorithm.
Full Text Available In order to change the insufficiency of traditional network flow prediction and improve its accuracy, the paper proposed a kind of network flow prediction method based on the self-adaptive genetic least square support vector machine optimization. Through analyzing the individual parameter of the LS-SVM principle and self-adaptive remains algorithm, the network flow prediction model structure of GA-LSSVM, and the genetic model global operation parameters, this paper would conduct a performance test to the network flow simulation experiment. The simulation result showed that: compared with the traditional forecasting methods, the accuracy of its network flow prediction was higher than the traditional forecasting methods by using the least square support vector machine genetic optimization.
Lai Xinmin; Luo Laijun; Lin Zhongqin
There are many welding fixture layout design problems of flexible parts in body-in-white assembly process, which directly cause body assemble variation.The fixture layout design quality is mainly influenced by the position and quantity of fixture locators and clamps.A general analysis model of flexible assembles deformation caused by fixture is set up based on "N-2-1" locating principle, in which the locator and clamper are treated as the same fixture layout elements.An analysis model for the flexible part deformation in fixturing is set up in order to obtain the optimization object function and constraints accordingly.The final fixture element layout could be obtained through global optimal research by using improved genetic algorithm, which effectively decreases fixture elements layout influence on flexible assembles deformation.
Full Text Available Resource allocation is the task of convenient resources to different uses. In the context of an resources, entire economy, can be assigned by different means, such as markets or central planning. Cloud computing has become a new age technology that has got huge potentials in enterprises and markets. Clouds can make it possible to access applications and associated data from anywhere. The fundamental motive of the resource allocation is to allot the available resource in the most effective manner. In the initial phase, a representative resource usage distribution for a group of nodes with identical resource usage patterns is evaluated as resource bundle which can be easily employed to locate a group of nodes fulfilling a standard criterion. In the document, an innovative clustering-based resource aggregation viz. the Improved Hierarchal Agglomerative Clustering Algorithm (IHAC is elegantly launched to realize the compact illustration of a set of identically behaving nodes for scalability. In the subsequent phase concerned with energetic resource allocation procedure, the hybrid optimization technique is brilliantly brought in. The novel technique is devised for scheduling functions to cloud resources which duly consider both financial and evaluation expenses. The efficiency of the novel Resource allocation system is assessed by means of several parameters such the reliability, reusability and certain other metrics. The optimal path choice is the consequence of the hybrid optimization approach. The new-fangled technique allocates the available resource based on the optimal path.
Aim to the manufacturing supply chain optimization problem with time windows, presents an improved orthogonal genetic algorithm to solve it. At first, we decompose this problem into two sub-problems (distribution and routing) plus an interface mechanism to allow the two algorithms to collaborate in a master-slave fashion, with the distribution algorithm driving the routing algorithm. At second, we describe the proposed improved orthogonal genetic algorithm for solving giving problem detailedly. Finally, the examples suggest that this proposed approach is feasible, correct and valid.
Liu Zheng; Mu Xuehua
The Frequency-Coded Pulse (FCP) signal has good performance of range and Doppler resolution. This paper first gives the mathematical expression of the ambiguity function for FCP signals, and then presents a coding rule for optimizing FCP signal. The genetic algorithm is presented to solve this kind of problem for optimizing codes. Finally, an example for optimizing calculation is illustrated and the optimized frequency coding results are given with the code length N=64 and N=128 respectively.
LI Qi; DAI Chao-hua; Chen Wei-rong; JIA Jun-bo; HAN Ming
Seeker optimization algorithm (SOA) has applications in continuous space of swarm intelligence. In the fields of proton ex-change membrane fuel cell (PEMFC) modeling, SOA was proposed to research a set of optimized parameters in PEMFC polariza-tion curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10-23. Hence, the optimization model could fit the experiment data with high precision.
Mortezanejad, R.; Gholami, A.
One of the most crucial challenges in seismic data processing is the reduction of noise in the data or improving the signal-to-noise ratio (SNR). Wavelet- and curvelet-based denoising algorithms have become popular to address random noise attenuation for seismic sections. Wavelet basis, thresholding function, and threshold value are three key factors of such algorithms, having a profound effect on the quality of the denoised section. Therefore, given a signal, it is necessary to optimize the denoising operator over these factors to achieve the best performance. In this paper a general denoising algorithm is developed as a multi-variant (variable) filter which performs in multi-scale transform domains (e.g. wavelet and curvelet). In the wavelet domain this general filter is a function of the type of wavelet, characterized by its smoothness, thresholding rule, and threshold value, while in the curvelet domain it is only a function of thresholding rule and threshold value. Also, two methods, Stein’s unbiased risk estimate (SURE) and generalized cross validation (GCV), evaluated using a Monte Carlo technique, are utilized to optimize the algorithm in both wavelet and curvelet domains for a given seismic signal. The best wavelet function is selected from a family of fractional B-spline wavelets. The optimum thresholding rule is selected from general thresholding functions which contain the most well known thresholding functions, and the threshold value is chosen from a set of possible values. The results obtained from numerical tests show high performance of the proposed method in both wavelet and curvelet domains in comparison to conventional methods when denoising seismic data.
Full Text Available Demand prediction of supply chain is an important content and the first premise in supply management of different enterprises and has become one of the difficulties and hot research fields for the researchers related. The paper takes fresh food demand prediction for example and presents a new algorithm for predicting demand of fresh food supply chain. First, the working principle and the root causes of the defects of particle swarm optimization algorithm are analyzed in the study; Second, the study designs a new cloud particle swarm optimization algorithm to guarantee the effectiveness of particles in later searching phase and redesigns its cloud global optimization searching method and crossover operation; Finally, a certain fresh food supply chain is taken for example to illustrate the validity and feasibility of the improved algorithm and the experimental results show that the improved algorithm can improve prediction accuracy and calculation efficiency when used for demand prediction of fresh food supply chain.
Full Text Available This paper presents an optimized speech compression algorithm using discrete wavelet transform, and its real time implementation on fixed-point digital signal processor (DSP. The optimized speech compression algorithm presents the advantages to ensure low complexity, low bit rate and achieve high speech coding efficiency, and this by adding a voice activity detector (VAD module before the application of the discrete wavelet transform. The VAD module avoids the computation of the discrete wavelet coefficients during the inactive voice signal. In addition, a real-time implementation of the optimized speech compression algorithm is performed using fixed-point processor. The optimized and the original algorithms are evaluated and compared in terms of CPU time (sec, Cycle count (MCPS, Memory consumption (Ko, Compression Ratio (CR, Signal to Noise Ratio (SNR, Peak Signal to Noise Ratio (PSNR and Normalized Root Mean Square Error (NRMSE.
Hartmann, Alexander K
Many physicists are not aware of the fact that they can solve their problems by applying optimization algorithms. Since the number of such algorithms is steadily increasing, many new algorithms have not been presented comprehensively until now. This presentation of recently developed algorithms applied in physics, including demonstrations of how they work and related results, aims to encourage their application, and as such the algorithms selected cover concepts and methods from statistical physics to optimization problems emerging in theoretical computer science.
Sharvani G S
Full Text Available Designing an effective load balancing algorithm is difficult due to Dynamic topology of MANET. Toaddress the problem, a load balancing routing algorithm namely Modified Termite Algorithm (MTA hasbeen developed based on ant’s food foraging behavior. Stability of the link is determined based on nodestability factor ‘’. The stability factor “ “of the node is the ratio defined between the “hello sent” and“hello replied” by a node to its neighbors. This also indicates the link stability in relation to other pathstowards the destination. A higher ratio of “” indicates that the neighbor node is more stable. Using thisconcept pheromone evaporation for the stable node is fine tuned such that if the ratio “” is more, theevaporation is slow and if “” is less the evaporation is faster. This leads to decreasing of the pheromonecontent in an optimal path which may result in congestion. These paths can be avoided using efficientevaporation technique. The MTA developed by adopting efficient pheromone evaporation technique willaddress the load balancing problems and expected to enhance the performance of the network in terms ofthroughput, and reduces End-to-end delay and Routing overheads
Full Text Available Cutting parameter optimization dramatically affects the production time, cost, profit rate, and the quality of the final products, in milling operations. Aiming to select the optimum machining parameters in multitool milling operations such as corner milling, face milling, pocket milling, and slot milling, this paper presents a novel version of TLBO, TLBO with dynamic assignment learning strategy (DATLBO, in which all the learners are divided into three categories based on their results in “Learner Phase”: good learners, moderate learners, and poor ones. Good learners are self-motivated and try to learn by themselves; each moderate learner uses a probabilistic approach to select one of good learners to learn; each poor learner also uses a probabilistic approach to select several moderate learners to learn. The CEC2005 contest benchmark problems are first used to illustrate the effectiveness of the proposed algorithm. Finally, the DATLBO algorithm is applied to a multitool milling process based on maximum profit rate criterion with five practical technological constraints. The unit time, unit cost, and profit rate from the Handbook (HB, Feasible Direction (FD method, Genetic Algorithm (GA method, five other TLBO variants, and DATLBO are compared, illustrating that the proposed approach is more effective than HB, FD, GA, and five other TLBO variants.
Shi Chuan; Kang Li-shan; Li Yan; Yan Zhen-yu
Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare to front, retain the diversity of the population, and use less time.
Quantum-inspired genetic algorithm (QGA) is applied to simulated annealing (SA) to develop a class of quantum-inspired simulated annealing genetic algorithm (QSAGA) for combinatorial optimization. With the condition of preserving QGA advantages, QSAGA takes advantage of the SA algorithm so as to avoid premature convergence. To demonstrate its effectiveness and applicability, experiments are carried out on the knapsack problem. The results show that QSAGA performs well, without premature conve...
Thompson, Mary Kathryn; Espensen, Christina; Clemmensen, Line Katrine Harder
This work characterizes and optimizes an outlier detection algorithm to identify potentially invalid scores produced by jury members while grading engineering design projects. The paper describes the original algorithm and the associated adjudication process in detail. The impact of the various...
Air data and inertial reference system (ADIRS) is one of the complex sub-system in the aircraft navigation system and it plays an important role into the flight safety of the aircraft. This paper propose an optimize neural network algorithm which is a combination of neural network and ant colony algorithm to improve efficiency of maintenance engineer job task.
This thesis presents new running time analyses of nature-inspired algorithms on various dynamic problems. It aims to identify and analyse the features of algorithms and problem classes which allow efficient optimization to occur in the presence of dynamic behaviour. We consider the following...
Li, R. N.; Liu, X.; Liu, S. J.
In order to ensure the high efficiency of the whole flexible drive train of the front-end speed adjusting wind turbine, the working principle of the main part of the drive train is analyzed. As critical parameters, rotating speed ratios of three planetary gear trains are selected as the research subject. The mathematical model of the torque converter speed ratio is established based on these three critical variable quantity, and the effect of key parameters on the efficiency of hydraulic mechanical transmission is analyzed. Based on the torque balance and the energy balance, refer to hydraulic mechanical transmission characteristics, the transmission efficiency expression of the whole drive train is established. The fitness function and constraint functions are established respectively based on the drive train transmission efficiency and the torque converter rotating speed ratio range. And the optimization calculation is carried out by using MATLAB genetic algorithm toolbox. The optimization method and results provide an optimization program for exact match of wind turbine rotor, gearbox, hydraulic mechanical transmission, hydraulic torque converter and synchronous generator, ensure that the drive train work with a high efficiency, and give a reference for the selection of the torque converter and hydraulic mechanical transmission.
Full Text Available This paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV path planning with particle swarm optimization (PSO technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samples t-test is made and an index called straight line rate (SLR is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques.
Full Text Available This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on the Mobile Century dataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.
ZHANG Qiang; MAO Jun; DING Fei
It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strategy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system research screw coal mine machine.
ZHANG Qiang; MAO Jun; DING Fei
It took that the weight minimum and drive efficiency maximal were as double optimizing target, the optimization model had built the drilling string, and the optimization solution was used of the ant colony algorithm to find in progress. Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat-egy. The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design, the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re-search screw coal mine machine.
Golubović Ružica M.
Full Text Available We present the results for two different antenna optimization problems that are found using the Particle Swarm Optimization (PSO algorithm. The first problem is finding the maximal forward gain of a Yagi antenna. The second problem is finding the optimal feeding of a broadside antenna array. The optimization problems have 6 and 20 optimization variables, respectively. The preferred values of the parameters of the PSO algorithm are found for presented problems. The results show that the preferred parameters of PSO are somewhat different for optimization problems with different number of dimensions of the optimization space. The results that are found using the PSO algorithm are compared with the results that are found using other optimization algorithms, in order to estimate the efficiency of the PSO.
Heaney, Kevin D.; Lermusiaux, Pierre F. J.; Duda, Timothy F.; Haley, Patrick J.
Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the genetic algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root mean square error (RMSE) between the "true" data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A five-glider optimal sampling study is set up for a 400 km × 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions.
A global optimization approach to turbine blade design based on hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs) coupled with Reynolds-averaged Navier-Stokes (RANS) equation is presented. In order to meet the search theory of GAs and the aerodynamic performances of turbine, Bezier curve is adopted to parameterize the turbine blade profile, and a fitness function pertaining to optimization is designed. The design variables are the control points' ordinates of characteristic polygon of Bezier curve representing the turbine blade profile. The object function is the maximum lift-drag ratio of the turbine blade. The constraint conditions take into account the leading and trailing edge metal angle, and the strength and aerodynamic performances of turbine blade. And the treatment method of the constraint conditions is the flexible penalty function. The convergence history of test function indicates that HFCDN-GAs can locate the global optimum within a few search steps and have high robustness. The lift-drag ratio of the optimized blade is 8.3% higher than that of the original one. The results show that the proposed global optimization approach is effective for turbine blade.
Su, Yonggang; Tang, Chen; Chen, Xia; Li, Biyuan; Xu, Wenjun; Lei, Zhenkun
We propose an image encryption scheme using chaotic phase masks and cascaded Fresnel transform holography based on a constrained optimization algorithm. In the proposed encryption scheme, the chaotic phase masks are generated by Henon map, and the initial conditions and parameters of Henon map serve as the main secret keys during the encryption and decryption process. With the help of multiple chaotic phase masks, the original image can be encrypted into the form of a hologram. The constrained optimization algorithm makes it possible to retrieve the original image from only single frame hologram. The use of chaotic phase masks makes the key management and transmission become very convenient. In addition, the geometric parameters of optical system serve as the additional keys, which can improve the security level of the proposed scheme. Comprehensive security analysis performed on the proposed encryption scheme demonstrates that the scheme has high resistance against various potential attacks. Moreover, the proposed encryption scheme can be used to encrypt video information. And simulations performed on a video in AVI format have also verified the feasibility of the scheme for video encryption.
XU Wet-qiang; WU Tie-jun
In this paper, based on the inherent characteristic of the contention relation between flows in ad hoc networks, we introduce the notion of the link's interference set, extend the utility maximization problem representing congestion control in wireline networks to ad hoc networks, apply the penalty function approach and the subgradient method to solve this problem, and propose the congestion control algorithm Penalty function-based Optical Congestion Control (POCC) which is implemented in NS2 simulator. Specifically, each link transmits periodically the information on its congestion state to its interference set; the session at each source adjusts the transmission rate based on the optimal tradeoffbetween the utility value and the congestion level which the interference set of the links that this session goes though suffers from. MATLAB-based simulation results showed that POCC can approach the globally optimal solution. The NS2-based simulation results showed that POCC outperforms default TCP and ATCP to achieve efficient and fair resource allocation in ad hoc networks.
Ming, Dongping; Zhou, Tianning; Wang, Min; Tan, Tian
Land cover classification based on remote sensing imagery is an important means to monitor, evaluate, and manage land resources. However, it requires robust classification methods that allow accurate mapping of complex land cover categories. Random forest (RF) is a powerful machine-learning classifier that can be used in land remote sensing. However, two important parameters of RF classification, namely, the number of trees and the number of variables tried at each split, affect classification accuracy. Thus, optimal parameter selection is an inevitable problem in RF-based image classification. This study uses the genetic algorithm (GA) to optimize the two parameters of RF to produce optimal land cover classification accuracy. HJ-1B CCD2 image data are used to classify six different land cover categories in Changping, Beijing, China. Experimental results show that GA-RF can avoid arbitrariness in the selection of parameters. The experiments also compare land cover classification results by using GA-RF method, traditional RF method (with default parameters), and support vector machine method. When the GA-RF method is used, classification accuracies, respectively, improved by 1.02% and 6.64%. The comparison results show that GA-RF is a feasible solution for land cover classification without compromising accuracy or incurring excessive time.
Yu-xiang LI; Yin-liang ZHAO‡; Bin LIU; Shuo JI
Thread partition plays an important role in speculative multithreading (SpMT) for automatic parallelization of ir-regular programs. Using unified values of partition parameters to partition different applications leads to the fact that every ap-plication cannot own its optimal partition scheme. In this paper, five parameters affecting thread partition are extracted from heuristic rules. They are the dependence threshold (DT), lower limit of thread size (TSL), upper limit of thread size (TSU), lower limit of spawning distance (SDL), and upper limit of spawning distance (SDU). Their ranges are determined in accordance with heuristic rules, and their step-sizes are set empirically. Under the condition of setting speedup as an objective function, all com-binations of five threshold values form the solution space, and our aim is to search for the best combination to obtain the best thread granularity, thread dependence, and spawning distance, so that every application has its best partition scheme. The issue can be attributed to a single objective optimization problem. We use the artificial immune algorithm (AIA) to search for the optimal solution. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, Olden bench-marks are used to implement the process. Experiments show that we can obtain the optimal parameter values for every benchmark, and Olden benchmarks partitioned with the optimized parameter values deliver a performance improvement of 3.00%on a 4-core platform compared with a machine learning based approach, and 8.92%compared with a heuristics-based approach.
Full Text Available For dams and rock foundations of ages, the actual mechanical parameters sometimes differed from the design and the experimental values. Therefore, it is necessary to carry out the inversion analysis on main physical and mechanical parameters of dams and rock foundations. However, only the integrated deformation modulus can be inversed by utilizing the conventional inversion method, and it does not meet the actual situation. Therefore, a new method is developed in this paper to inverse the actual initial zoning deformation modulus and to determine the inversion objective function for the actual zoning deformation modulus, based on the dam displacement measured data and finite element calculation results. Furthermore, based on the chaos genetic optimization algorithm, the inversion method for zoning deformation modulus of dam, dam foundation and, reservoir basin is proposed. Combined with the project case, the feasibility and validity of the proposed method are verified.
Full Text Available In underwater blasting experiment, the layout of the sensor has always been highly concerned. From the perspective of reconstruction with explosion overpressure field, the paper presents four indicators, which can obtain the optimal sensor layout scheme and guide sensor layout in practical experiment, combining with the genetic algorithm with global search. Then, a multi-scale model in every subregion of underwater blasting field was established to be used simulation experiments. By Matlab, the variation of these four indicators with different sensor layout, and reconstruction accuracy are analyzed and discussed. Finally, a conclusion has been raised through the analysis and comparison of simulation results, that the program can get a better sensor layout. It requires fewer number of sensors to be able to get good results with high accuracy. In the actual test explosions, we can refer to this scheme laid sensors.
Kontoleontos, E.; Weissenberger, S.
In order to be able to predict the maximum Annual Energy Production (AEP) for tidal power plants, an advanced AEP optimization procedure is required for solving the optimization problem which consists of a high number of design variables and constraints. This efficient AEP optimization procedure requires an advanced optimization tool (EASY software) and an AEP calculation tool that can simulate all different operating modes of the units (bidirectional turbine, pump and sluicing mode). The EASY optimization software is a metamodel-assisted Evolutionary Algorithm (MAEA) that can be used in both single- and multi-objective optimization problems. The AEP calculation tool, developed by ANDRITZ HYDRO, in combination with EASY is used to maximize the tidal annual energy produced by optimizing the plant operation throughout the year. For the Swansea Bay Tidal Power Plant project, the AEP optimization along with the hydraulic design optimization and the model testing was used to evaluate all different hydraulic and operating concepts and define the optimal concept that led to a significant increase of the AEP value. This new concept of a triple regulated “bi-directional bulb pump turbine” for Swansea Bay Tidal Power Plant (16 units, nominal power above 320 MW) along with its AEP optimization scheme will be presented in detail in the paper. Furthermore, the use of an online AEP optimization during operation of the power plant, that will provide the optimal operating points to the control system, will be also presented.
Zhang, Yan-jun; Liu, Wen-zhe; Fu, Xing-hu; Bi, Wei-hong
According to the high precision extracting characteristics of scattering spectrum in Brillouin optical time domain reflection optical fiber sensing system, this paper proposes a new algorithm based on flies optimization algorithm with adaptive mutation and generalized regression neural network. The method takes advantages of the generalized regression neural network which has the ability of the approximation ability, learning speed and generalization of the model. Moreover, by using the strong search ability of flies optimization algorithm with adaptive mutation, it can enhance the learning ability of the neural network. Thus the fitting degree of Brillouin scattering spectrum and the extraction accuracy of frequency shift is improved. Model of actual Brillouin spectrum are constructed by Gaussian white noise on theoretical spectrum, whose center frequency is 11.213 GHz and the linewidths are 40-50, 30-60 and 20-70 MHz, respectively. Comparing the algorithm with the Levenberg-Marquardt fitting method based on finite element analysis, hybrid algorithm particle swarm optimization, Levenberg-Marquardt and the least square method, the maximum frequency shift error of the new algorithm is 0.4 MHz, the fitting degree is 0.991 2 and the root mean square error is 0.024 1. The simulation results show that the proposed algorithm has good fitting degree and minimum absolute error. Therefore, the algorithm can be used on distributed optical fiber sensing system based on Brillouin optical time domain reflection, which can improve the fitting of Brillouin scattering spectrum and the precision of frequency shift extraction effectively.
Full Text Available In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO algorithm combined with the least squares method (LMS to optimize the adaptive network-based fuzzy inference system (ANFIS model parameters. Through the introduction of metric function of population diversity to ensure the diversity of population and adaptive changes in inertia weight and learning factors, the optimization ability of the particle swarm optimization (PSO algorithm is improved, which avoids the premature convergence problem of the PSO algorithm. The simulation comparison experiments are carried out with BP-LMS algorithm and standard PSO-LMS by adopting real commercial banks’ cash flow data to verify the effectiveness of the proposed time series prediction of bank cash flow based on improved PSO-ANFIS optimization method. Simulation results show that the optimization speed is faster and the prediction accuracy is higher.
Hashemi-Dezaki, Hamed; Mohammadalizadeh-Shabestary, Masoud; Askarian-Abyaneh, Hossein; Rezaei-Jegarluei, Mohammad
In electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.
NIAN Xiaoyu; WANG Zhenlei; QIAN Feng
To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online,a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed,which is based on the differential evolution (DE) and the group search optimization (GSO).The DEGSO combines the advantages of the two algorithms:the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum.A cooperative method is also proposed for switching between these two algorithms.If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold,it is considered to fall into the local optimization and the other algorithm is chosen.Experiments on benchmark functions show that.the hybrid algorithm outperforms GSO in accuracy,global searching ability and efficiency.The optimization of ethylene and propylene yields is illustrated as a case by DEGSO.After optimization,the yield of ethylene and propylene is increased remarkably,which provides the proper operational condition of the ethylene cracking furnace.
WANG Guo-liang; YAN Wei-wu; SHAO Hui-he
To get the satisfying performance of a PID controller, this paper presents a novel Pareto - based multi-objective genetic algorithm ( MOGA), which can be used to find the appropriate setting of the PID controller by analyzing the pareto optimal surfaces. Rated settings of the controller by two criteria, the error between output and reference signals and control moves, are listed on the pareto surface. Appropriate setting can be chosen under a balance between two criteria for different control purposes. A controller tuning problem for a plant with high order and time delay is chosen as an example. Simulation results show that the method of MOGA is more efficient compared with traditional tuning methods.
A multiuser detector was developed for fast fading code-division multiple-access systems by representing the channels as a system with the multiplicative noise (SMN) model and then using the known optimal filtering algorithm for the SMN for multiuser detection (MUD). This multiuser detector allows the channel response to be stochastic in one symbol duration, which can be regarded as an effective method of MUD for fast fading CDMA systems. Performance analyses show that the multiuser detector is theoretically valid for CDMA systems over fast fading channels. Simulations show that the multiuser detector performs better than the Kalman filter-based multiuser detector with a faster convergence rate and lower bit error rate.
Surafel Luleseged Tilahun
Full Text Available Transportation plays a vital role in the development of a country and the car is the most commonly used means. However, in third world countries long waiting time for public buses is a common problem, especially when people need to switch buses. The problem becomes critical when one considers buses joining different villages and cities. Theoretically this problem can be solved by assigning more buses on the route, which is not possible due to economical problem. Another option is to schedule the buses so that customers who want to switch buses at junction cities need not have to wait long. This paper discusses how to model single frequency routes bus timetabling as a fuzzy multiobjective optimization problem and how to solve it using preference-based genetic algorithm by assigning appropriate fuzzy preference to the need of the customers. The idea will be elaborated with an example.
Liu Jianghua(刘江华); Chen Jiapin; Cheng Junshi
Coupled Hidden Markov Model (CHMM) is the extension of traditional HMM, which is mainly used for complex interactive process modeling such as two-hand gestures. However, the problems of finding optimal model parameter are still of great interest to the researches in this area. This paper proposes a hybrid genetic algorithm (HGA) for the CHMM training. Chaos is used to initialize GA and used as mutation operator. Experiments on Chinese TaiChi gestures show that standard GA (SGA) based CHMM training is superior to Maximum Likelihood (ML) HMM training. HGA approach has the highest recognition rate of 98.0769%, then 96.1538% for SGA. The last one is ML method, only with a recognition rate of 69.2308%.
Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.
Le-Duc, Thang; Ho-Huu, Vinh; Nguyen-Thoi, Trung; Nguyen-Quoc, Hung
In recent years, various types of magnetorheological brakes (MRBs) have been proposed and optimized by different optimization algorithms that are integrated in commercial software such as ANSYS and Comsol Multiphysics. However, many of these optimization algorithms often possess some noteworthy shortcomings such as the trap of solutions at local extremes, or the limited number of design variables or the difficulty of dealing with discrete design variables. Thus, to overcome these limitations and develop an efficient computation tool for optimal design of the MRBs, an optimization procedure that combines differential evolution (DE), a gradient-free global optimization method with finite element analysis (FEA) is proposed in this paper. The proposed approach is then applied to the optimal design of MRBs with different configurations including conventional MRBs and MRBs with coils placed on the side housings. Moreover, to approach a real-life design, some necessary design variables of MRBs are considered as discrete variables in the optimization process. The obtained optimal design results are compared with those of available optimal designs in the literature. The results reveal that the proposed method outperforms some traditional approaches.
Cui, Laizhong; Jiang, Yong; Wu, Jianping; Xia, Shutao
Most large-scale Peer-to-Peer (P2P) live streaming systems are constructed as a mesh structure, which can provide robustness in the dynamic P2P environment. The pull scheduling algorithm is widely used in this mesh structure, which degrades the performance of the entire system. Recently, network coding was introduced in mesh P2P streaming systems to improve the performance, which makes the push strategy feasible. One of the most famous scheduling algorithms based on network coding is R2, with a random push strategy. Although R2 has achieved some success, the push scheduling strategy still lacks a theoretical model and optimal solution. In this paper, we propose a novel optimal pull-push scheduling algorithm based on network coding, which consists of two stages: the initial pull stage and the push stage. The main contributions of this paper are: 1) we put forward a theoretical analysis model that considers the scarcity and timeliness of segments; 2) we formulate the push scheduling problem to be a global optimization problem and decompose it into local optimization problems on individual peers; 3) we introduce some rules to transform the local optimization problem into a classical min-cost optimization problem for solving it; 4) We combine the pull strategy with the push strategy and systematically realize our scheduling algorithm. Simulation results demonstrate that decode delay, decode ratio and redundant fraction of the P2P streaming system with our algorithm can be significantly improved, without losing throughput and increasing overhead.
Du, Yanqin; Huang, Hua
Fetal electrocardiogram (FECG) is an objective index of the activities of fetal cardiac electrophysiology. The acquired FECG is interfered by maternal electrocardiogram (MECG). How to extract the fetus ECG quickly and effectively has become an important research topic. During the non-invasive FECG extraction algorithms, independent component analysis(ICA) algorithm is considered as the best method, but the existing algorithms of obtaining the decomposition of the convergence properties of the matrix do not work effectively. Quantum particle swarm optimization (QPSO) is an intelligent optimization algorithm converging in the global. In order to extract the FECG signal effectively and quickly, we propose a method combining ICA and QPSO. The results show that this approach can extract the useful signal more clearly and accurately than other non-invasive methods.
Nazri Mohd. Nawi
Full Text Available Training an artificial neural network is an optimization task, since it is desired to find optimal weight sets for a neural network during training process. Traditional training algorithms such as back propagation have some drawbacks such as getting stuck in local minima and slow speed of convergence. This study combines the best features of two algorithms; i.e. Levenberg Marquardt back propagation (LMBP and Cuckoo Search (CS for improving the convergence speed of artificial neural networks (ANN training. The proposed CSLM algorithm is trained on XOR and OR datasets. The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study.
YU Ting-chao; ZHANG Tu-qiao; LI Xun
In view of the poor water supply system's network properties, the system's complicated network hydraulic equations were replaced by macroscopic nodal pressure model and the model of relationship between supply flow and water source head. By using pump-station pressure head and initial tank water levels as decision variables, the model of optimal allocation of water supply between pump-sources was developed. Genetic algorithm was introduced to deal with the model of optimal allocation of water supply. Methods for handling each constraint condition were put forward, and overcome the shortcoming such as premature convergence of genetic algorithm;a solving method was brought forward in which genetic algorithm was combined with simulated annealing technology and self-adaptive crossover and mutation probabilities were adopted. An application example showed the feasibility of this algorithm.
Full Text Available A method is designed to optimize the weight matrix of the LQR controller by using the simulated annealing algorithm. This method utilizes the random searching characteristics of the algorithm to optimize the weight matrices with the target function of suspension performance indexes. This method improves the design efficiency and control performance of the LQR control, and solves the problem of the LQR controller when defining the weight matrices. And a simulation is provided for vehicle active chassis control. The result shows that the active suspension using LQR optimized by the genetic algorithm compared to the chassis controlled by the normal LQR and the passive one, shows better performance. Meanwhile, the problem of defining the weight matrices is greatly solved.
Shahriar Afandizadeh Zargari
Full Text Available The primary objective of this research is to optimize signal timing in consecutive signalized intersections. In this paper, the combination of genetic programming (GP with genetic algorithms (GA and neural network (NN with genetic algorithm (GA were used and compared in order to optimize signal timing in consecutive signalized intersections. First, genetic programming and neural network were constructed from existing signal timing data to predict the delay of intersections. Then genetic algorithm was applied to optimize these predictive networks (GP and NN. The results and comparisons of timing process and error percentage showed that neural network is more efficient than genetic programming. However, the ability of genetic programming in producing formula is a specific characteristic which makes it more applicable than neural network. Finally, for validating the results, Aimsun and Synchro micro simulation software were used, and accuracy of our models was approved.
Full Text Available Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR.
Full Text Available Effective, simulation-based trajectory optimization algorithms adapted to heterogeneous computers are studied with reference to the problem taken from alpine ski racing (the presented solution is probably the most general one published so far. The key idea behind these algorithms is to use a grid-based discretization scheme to transform the continuous optimization problem into a search problem over a specially constructed finite graph, and then to apply dynamic programming to find an approximation of the global solution. In the analyzed example it is the minimum-time ski line, represented as a piecewise-linear function (a method of elimination of unfeasible solutions is proposed. Serial and parallel versions of the basic optimization algorithm are presented in detail (pseudo-code, time and memory complexity. Possible extensions of the basic algorithm are also described. The implementation of these algorithms is based on OpenCL. The included experimental results show that contemporary heterogeneous computers can be treated as μ-HPC platforms-they offer high performance (the best speedup was equal to 128 while remaining energy and cost efficient (which is crucial in embedded systems, e.g., trajectory planners of autonomous robots. The presented algorithms can be applied to many trajectory optimization problems, including those having a black-box represented performance measure
Full Text Available In this paper, we apply The Bees Algorithm to find optimal PID controller gains to control angular positions of robot leg joints with the minimum position error. In order to present more realistic simulation, system modelled in MATLAB/Simulink environment which is close to experimental set up. Solid model of system, which has two degrees of freedom, drawn by using a CAD software. Required physical specifications of robot leg for MATLAB/Simulink modelling is obtained from this CAD model. Controller of the system is designed in MATLAB/Simulink interface. Simulation results derived to show effectiveness of The Bees Algorithm to find optimal PID controller gains.
Chambers, D; Lehman, S; Dowla, F
A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.
Full Text Available Trajectory simplification has become a research hotspot since it plays a significant role in the data preprocessing, storage, and visualization of many offline and online applications, such as online maps, mobile health applications, and location-based services. Traditional heuristic-based algorithms utilize greedy strategy to reduce time cost, leading to high approximation error. An Optimal Trajectory Simplification Algorithm based on Graph Model (OPTTS is proposed to obtain the optimal solution in this paper. Both min-# and min-ε problems are solved by the construction and regeneration of the breadth-first spanning tree and the shortest path search based on the directed acyclic graph (DAG. Although the proposed OPTTS algorithm can get optimal simplification results, it is difficult to apply in real-time services due to its high time cost. Thus, a new Online Trajectory Simplification Algorithm based on Directed Acyclic Graph (OLTS is proposed to deal with trajectory stream. The algorithm dynamically constructs the breadth-first spanning tree, followed by real-time minimizing approximation error and real-time output. Experimental results show that OPTTS reduces the global approximation error by 82% compared to classical heuristic methods, while OLTS reduces the error by 77% and is 32% faster than the traditional online algorithm. Both OPTTS and OLTS have leading superiority and stable performance on different datasets.
Full Text Available In this paper, design of fuzzy proportional derivative controller and fuzzy proportional derivative integral controller for speed control of brushless direct current drive has been presented. Optimization of the above controllers design is carried out using nature inspired optimization algorithms such as particle swarm, cuckoo search, and bat algorithms. Time domain specifications such as overshoot, undershoot, settling time, recovery time, and steady state error and performance indices such as root mean squared error, integral of absolute error, integral of time multiplied absolute error and integral of squared error are measured and compared for the above controllers under different operating conditions such as varying set speed and load disturbance conditions. The precise investigation through simulation is performed using simulink toolbox. From the simulation test results, it is evident that bat optimized fuzzy proportional derivative controller has superior performance than the other controllers considered. Experimental test results have also been taken and analyzed for the optimal controller identified through simulation.
Bakir, Pelin Gundes; Reynders, Edwin; De Roeck, Guido
The use of changes in dynamic system characteristics to detect damage has received considerable attention during the last years. Within this context, FE model updating technique, which belongs to the class of inverse problems in classical mechanics, is used to detect, locate and quantify damage. In this study, a sensitivity-based finite element (FE) model updating scheme using a trust region algorithm is developed and implemented in a complex structure. A damage scenario is applied on the structure in which the stiffness values of the beam elements close to the beam-column joints are decreased by stiffness reduction factors. A worst case and complex damage pattern is assumed such that the stiffnesses of adjacent elements are decreased by substantially different stiffness reduction factors. The objective of the model updating is to minimize the differences between the eigenfrequency and eigenmodes residuals. The updating parameters of the structure are the stiffness reduction factors. The changes of these parameters are determined iteratively by solving a nonlinear constrained optimization problem. The FE model updating algorithm is also tested in the presence of two levels of noise in simulated measurements. In all three cases, the updated MAC values are above 99% and the relative eigenfrequency differences improve substantially after model updating. In cases without noise and with moderate levels of noise; detection, localization and quantification of damage are successfully accomplished. In the case with substantially noisy measurements, detection and localization of damage are successfully realized. Damage quantification is also promising in the presence of high noise as the algorithm can still predict 18 out of 24 damage parameters relatively accurately in that case.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.
Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan
A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987
杨璐鸿; 刘顺安; 张冠宇; 王春雪
To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization (PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.
Wang, Hong; Wang, Xicheng; Li, Zheng; Li, Keqiu
The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.
Full Text Available In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA and Cognitive Development Optimization Algorithm (CoDOA, have been used to solve some multidisciplinary optimization problems provided in the source book Thomas' Calculus 11th Edition and the obtained results have compared with classical optimization solutions.
Full Text Available Electrochemical micromachining (EMM appears to be a very promising micromachining process for having higher machining rate, better precision and control, reliability, flexibility, environmental acceptability, and capability of machining a wide range of materials. It permits machining of chemically resistant materials, like titanium, copper alloys, super alloys and stainless steel to be used in biomedical, electronic, micro-electromechanical system and nano-electromechanical system applications. Therefore, the optimal use of an EMM process for achieving enhanced machining rate and improved profile accuracy demands selection of its various machining parameters. Various optimization tools, primarily Derringer’s desirability function approach have been employed by the past researchers for deriving the best parametric settings of EMM processes, which inherently lead to sub-optimal or near optimal solutions. In this paper, an attempt is made to apply an almost new optimization tool, i.e. differential search algorithm (DSA for parametric optimization of three EMM processes. A comparative study of optimization performance between DSA, genetic algorithm and desirability function approach proves the wide acceptability of DSA as a global optimization tool.
Zhao, Jianhu; Wang, Xiao; Zhang, Hongmei; Hu, Jun; Jian, Xiaomin
To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantum-behaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.
Wang, Yubo; Veluvolu, Kalyana C.
The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance. PMID:28203141
石明洪; 王思兵; 白英彩
QoS routing is one of the key technologies for providing guaranteed service in IP networks. The paper focuses on the optimization problem for bandwidth constrained QoS routing, and proposes an optimal algorithm based on the global optimization of path bandwidth and hop counts. The main goal of the algorithm is to minimize the consumption of network resource, and at the same time to minimize the network congestion caused by irrational path selection. The simulation results show that our algorithm has lower call blocking rate and higher throughput than traditional algorithms.
Thierens, D.; Bosman, P.A.N.; Krasnogor, N.
A key search mechanism in Evolutionary Algorithms is the mixing or juxtaposing of partial solutions present in the parent solutions. In this paper we look at the efficiency of mixing in genetic algorithms (GAs) and estimation-of-distribution algorithms (EDAs). We compute the mixing probabilities of
Chastikova V. A.
Full Text Available Griffon-vultures with input parameters minimal value for compound functions optimization that change during the time searching hybrid algorithm offered in this article. Researches of its efficiency and comparing analysis with some other systems have been performed
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.
He Suxiang; Meng Hongchao; Wang Hui [School of Science, Wuhan University of Technology, Wuhan 430070 (China); Zhao Yanli, E-mail: email@example.com [Wuhan National Laboratory for Optoelectronics, School of Optoelectronics Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)
The optimal model for wireless optical communication system with Gaussian pointing loss factor is studied, in which the value of bit error probability (BEP) is prespecified and the optimal system parameters is to be found. For the superiority of augmented Lagrange method, the model considered is solved by using a classical quadratic augmented Lagrange algorithm. The detailed numerical results are reported. Accordingly, the optimal system parameters such as transmitter power, transmitter wavelength, transmitter telescope gain and receiver telescope gain can be established, which provide a scheme for efficient operation of the wireless optical communication system.
Lahanas, M; Baltas, D; Zamboglou, N
Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives.
Lahanas, M [Department of Medical Physics and Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach (Germany); Baltas, D [Department of Medical Physics and Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach (Germany); Zamboglou, N [Department of Medical Physics and Engineering, Strahlenklinik, Klinikum Offenbach, 63069 Offenbach (Germany)
Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives.
Full Text Available The feasibility design method with multidisciplinary and multiobjective optimization is applied in the research of lightweight design and NVH performances of crankshaft in high-power marine reciprocating compressor. Opt-LHD is explored to obtain the experimental scheme and perform data sampling. The elliptical basis function neural network (EBFNN model considering modal frequency, static strength, torsional vibration angular displacement, and lightweight design of crankshaft is built. Deterministic optimization and reliability optimization for lightweight design of crankshaft are operated separately. Multi-island genetic algorithm (MIGA combined with multidisciplinary cooptimization method is used to carry out the multiobjective optimization of crankshaft structure. Pareto optimal set is obtained. Optimization results demonstrate that the reliability optimization which considers the uncertainties of production process can ensure product stability compared with deterministic optimization. The coupling and decoupling of structure mechanical properties, NVH, and lightweight design are considered during the multiobjective optimization of crankshaft structure. Designers can choose the optimization results according to their demands, which means the production development cycle and the costs can be significantly reduced.
Full Text Available In recent years, evolutionary optimization (EO techniques have attracted considerable attention in the design of electromagnetic systems of increasing complexity. This paper presents a comparison between two optimization algorithms for the synthesis of uniform linear and planar antennas arrays, the first one is an adaptive particle swarm optimization (APSO where the inertia weight and acceleration coefficient are adjusted dynamically according to feedback taken from particles best memories to overcome the limitations of the standard PSO which are: premature convergence, low searching accuracy and iterative inefficiency. The second method is the genetic algorithms (GA inspired from the processes of the evolution of the species and the natural genetics. The results show that the design of uniform linear and planar antennas arrays using APSO method provides a low side lobe level and achieve faster convergence speed to the optimum solution than those obtained by a GA.
Shooshtari, Alireza Tavakoli; Joorabian, Mahmood; Milani, Armin Ebrahimi; Gholamzadeh, Arash
Any plan for modeling the components of transmission service costs should be able to consider congestion as well as loss cost. Assessing the real value of congestion and loss costs in each network has a substantial contribution to analyze the grid's weaknesses in order to release capacity of power network. As much as the amount of congestion and loss costs in the transmission grid reduces the amount of power passing through transmission lines increases. Therefore, the transmission service cost will be optimized and revenues of the regional electricity company from transmission services will be increased. In this paper, a new power flow algorithm with congestion and loss considerations of a power network is presented. Thus, optimal power flow and a multi-objectives optimization algorithm, called NSGA-II, is used in this work. The real data of Khuzestan regional power grid is implemented to confirm the efficiency of proposed method.
Victor II M. Romero
Full Text Available Harmony Search (HS algorithm, a relatively recent meta-heuristic optimization algorithm based on the music improvisation process of musicians, is applied to one of today's most appealing problems in the field of Computer Science, Tetris. Harmony Search algorithm was used as the underlying optimization algorithm to facilitate the learning process of an intelligent agent whose objective is to play the game of Tetris in the most optimal way possible, that is, to clear as many rows as possible. The application of Harmony Search algorithm to Tetris is a good illustration of the involvement of optimization process to decision-making problems. Experiment results show that Harmony Search algorithm found the best possible solution for the problem at hand given a random sequence of Tetrominos.
Full Text Available Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO. This paper presents an improved MPSCO algorithm (IMPSCO firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA. The results show threefold: (1 the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2 the damage location can be accurately detected using the damage threshold proposed in this paper; and (3 compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.
Bernau, Christoph; Augustin, Thomas; Boulesteix, Anne-Laure
High-dimensional binary classification tasks, for example, the classification of microarray samples into normal and cancer tissues, usually involve a tuning parameter. By reporting the performance of the best tuning parameter value only, over-optimistic prediction errors are obtained. For correcting this tuning bias, we develop a new method which is based on a decomposition of the unconditional error rate involving the tuning procedure, that is, we estimate the error rate of wrapper algorithms as introduced in the context of internal cross-validation (ICV) by Varma and Simon (2006, BMC Bioinformatics 7, 91). Our subsampling-based estimator can be written as a weighted mean of the errors obtained using the different tuning parameter values, and thus can be interpreted as a smooth version of ICV, which is the standard approach for avoiding tuning bias. In contrast to ICV, our method guarantees intuitive bounds for the corrected error. Additionally, we suggest to use bias correction methods also to address the conceptually similar method selection bias that results from the optimal choice of the classification method itself when evaluating several methods successively. We demonstrate the performance of our method on microarray and simulated data and compare it to ICV. This study suggests that our approach yields competitive estimates at a much lower computational price.
Zhao, Changchen; Hwang, Wen-Liang; Lin, Chun-Liang; Chen, Weihai
Proximal K-singular value decomposition (PK-SVD) is a dictionary updating algorithm that incorporates proximal point method into K-SVD. The attempt of combining proximal method and K-SVD has achieved promising result in such areas as sparse approximation, image denoising, and image compression. However, the optimization procedure of PK-SVD is complicated and, therefore, limits the algorithm in both theoretical analysis and practical use. This article proposes a simple but effective optimization approach to the formulation of PK-SVD. We cast this formulation as a fitting problem and relax the constraint on the direction of the k'th row in the sparse coefficient matrix. This relaxation strengthens the regularization effect of the proximal point. The proposed algorithm needs fewer steps to implement and further boost the performance of PK-SVD while maintaining the same computational complexity. Experimental results demonstrate that the proposed algorithm outperforms conventional algorithms in reconstruction error, recovery rate, and convergence speed for sparse approximation and achieves better results in image denoising.
Yan Qin BAI; Guo Qiang WANG
A class of polynomial primal-dual interior-point algorithms for second-order cone optimization based on a new parametric kernel function, with parameters p and q, is presented. Its growth term is between linear and quadratic. Some new tools for the analysis of the algorithms are proposed.The complexity bounds of O(√Nlog N log N/ε) for large-update methods and O(√Nlog N/ε) for smallupdate methods match the best known complexity bounds obtained for these methods. Numerical tests demonstrate the behavior of the algorithms for different results of the parameters p and q.
Full Text Available In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO and genetic algorithm (GA. A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.
Full Text Available The many-objective optimization performance of the Kriging-surrogate-based evolutionary algorithm (EA, which maximizes expected hypervolume improvement (EHVI for updating the Kriging model, is investigated and compared with those using expected improvement (EI and estimation (EST updating criteria in this paper. Numerical experiments are conducted in 3- to 15-objective DTLZ1-7 problems. In the experiments, an exact hypervolume calculating algorithm is used for the problems with less than six objectives. On the other hand, an approximate hypervolume calculating algorithm based on Monte Carlo sampling is adopted for the problems with more objectives. The results indicate that, in the nonconstrained case, EHVI is a highly competitive updating criterion for the Kriging model and EA based many-objective optimization, especially when the test problem is complex and the number of objectives or design variables is large.
S. V. Durga Bhavani,
Full Text Available Power system throughout the world is undergoing tremendous changes and developments due to rapid Restructuring, Deregulation and Open-access policies. Greater liberalization, larger market and increasing dependency on the electricity lead to the system operators to work on limited spinning reserve and to operate on vicinities to maximize the economy compromising on the reliability and security of the system for greater profits, which lead to establishment of a monitoring authority and accurate electronic system to prevent any untoward incidents like Blackouts. In any power system, unexpected outages of lines or transformers occur due to faults or other disturbances. These events may cause significant overloading of transmission lines or transformers, which in turn may lead to a viability crisis of the power system. The main role of power system control is to maintain a secure system state, i.e., to prevent the power system, moving from secure state into emergency state over the widest range of operating conditions. Security Constrained Optimal Power Flow (SCOPF is major tool used to improve the security of the system. In this work, Genetic algorithm has been used to solve the OPF and SCOPF problems. As initial effort conventional GA (binary coded based OPF and SCOPF is going to be attempted. The difficulties of binary coded GA in handling continuous search space lead to the evolution of real coded GA‟s. Solutions obtained using both the algorithms are compared. Case studies are made on the IEEE30 bus test system to demonstrate the ability of real coded GA in solving the OPF and SCOPF problems.
Full Text Available In order to replace the traditional Internet software usage patterns and enterprise management mode, this paper proposes a new business calculation mode- cloud computing, resources scheduling strategy is the key technology in cloud computing, Based on the study of cloud computing system structure and the mode of operation, The key research for cloud computing the process of the work scheduling and resource allocation problems based on ant colony algorithm , Detailed analysis and design of the specific implementation for cloud resources scheduling . And in CloudSim simulation environment and simulation experiments, the results show that the algorithm has better scheduling performance and load balance than general algorithm.
A trading strategy based on a natural learning process, which asymptotically outperforms any trading strategy from RKHS (Reproduced Kernel Hilbert Space), is presented. In this process, the trader rationally chooses his gambles using predictions made by a randomized well calibrated algorithm. Our strategy is based on Dawid's notion of calibration with more general changing checking rules and on some modification of Kakade and Foster's randomized algorithm for computing calibrated forecasts. We use also Vovk's method of defensive forecasting in RKHS.
A method for optimizing automotive doors under multiple criteria involving the side impact,stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques, i. e. polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.
Full Text Available In this paper, an optimal design of linear phase digital finite impulse response (FIR highpass (HP filter using the L1-norm based real-coded genetic algorithm (RCGA is investigated. A novel fitness function based on L1 norm is adopted to enhance the design accuracy. Optimized filter coefficients are obtained by defining the filter objective function in L1 sense using RCGA. Simulation analysis unveils that the performance of the RCGA adopting this fitness function is better in terms of signal attenuation ability of the filter, flatter passband and the convergence rate. Observations are made on the percentage improvement of this algorithm over the gradient-based L1 optimization approach on various factors by a large amount. It is concluded that RCGA leads to the best solution under specified parameters for the FIR filter design on account of slight unnoticeable higher transition width.
Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.
Full Text Available We propose an optimized Support Vector Machine classifier, named PMSVM, in which System Normalization, PCA, and Multilevel Grid Search methods are comprehensively considered for data preprocessing and parameters optimization, respectively. The main goals of this study are to improve the classification efficiency and accuracy of SVM. Sensitivity, Specificity, Precision, and ROC curve, and so forth, are adopted to appraise the performances of PMSVM. Experimental results show that PMSVM has relatively better accuracy and remarkable higher efficiency compared with traditional SVM algorithms.
Lihui CEN; Yugeng XI
By considering the flow control of urban sewer networks to minimize the electricity consumption of pumping stations.a decomposition-coordination strategy for energy savings based on network community division is developed in this paper. A mathematical model characterizing the smady-state flow of urball sewer networks is first constructed,consisting of a set of algebraic equations with the structure transportation capacities captured as constraints.Since the sewer networks have no apparent natural hierarchical structure in general.it is very difficult to identify the clustered groups.A fast network division approach through calculating the betweenness of each edge is successfully applied to identify the groups and a sewer network with arbitrary configuration could be then decomposed into subnetworks.By integrating the coupling constraints of the subnetworks.the original problem is separated into N optimization subproblems in accordance with the network decomposition.Each subproblem is solved locally and the solutions to the subproblems are coordinated to form an appropriate global solution.Finally,an application to a specified large-scale sewer network is also investigated to demonstrate the validity of the proposed algorithm.
Full Text Available The definition, diagnosis and classification of Diabetes Mellitus and its complications are very important. First of all, the World Health Organization (WHO and other societies, as well as scientists have done lots of studies regarding this subject. One of the most important research interests of this subject is the computer supported decision systems for diagnosing diabetes. In such systems, Artificial Intelligence techniques are often used for several disease diagnostics to streamline the diagnostic process in daily routine and avoid misdiagnosis. In this study, a diabetes diagnosis system, which is formed via both Support Vector Machines (SVM and Cognitive Development Optimization Algorithm (CoDOA has been proposed. Along the training of SVM, CoDOA was used for determining the sigma parameter of the Gauss (RBF kernel function, and eventually, a classification process was made over the diabetes data set, which is related to Pima Indians. The proposed approach offers an alternative solution to the field of Artificial Intelligence-based diabetes diagnosis, and contributes to the related literature on diagnosis processes.
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.
Wang, Zhaocai; Pu, Jun; Cao, Liling; Tan, Jian
The unbalanced assignment problem (UAP) is to optimally resolve the problem of assigning n jobs to m individuals (m parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn) time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation.
Full Text Available In order to extract the maximum power from PV system, the maximum power point tracking (MPPT technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP. In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions.
Full Text Available Based on triangular centroid locating algorithm,this paper will use the idea of collaboration to indoor locating system.On account of the condition which has two nodes to be located in the test environment,we have designed a circular type optimization algorithm.Verified simulation results show that the circular type optimization algorithm,compared with the triangular centroid locating algorithm,can decrease the average error by 11.62%,decrease the maximum error by 7.74% and decrease the minimum error by 22.66%.The maximum value of the optimize degree of the circular type optimization algorithm is 28.63%,and the minimum value of that is 0.05%.
Navarro-Munoz, J C; Rosu, H C; Navarro-Munoz, Jorge C.
We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach
Full Text Available Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM, K-nearest neighbor (KNN, Interval Valued Classification (IVC and the improvised Interval Value based Particle Swarm Optimization (IVPSO algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.
Ramyachitra, D; Sofia, M; Manikandan, P
Microarray technology allows simultaneous measurement of the expression levels of thousands of genes within a biological tissue sample. The fundamental power of microarrays lies within the ability to conduct parallel surveys of gene expression using microarray data. The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high compared to the number of data samples. Thus the difficulty that lies with data are of high dimensionality and the sample size is small. This research work addresses the problem by classifying resultant dataset using the existing algorithms such as Support Vector Machine (SVM), K-nearest neighbor (KNN), Interval Valued Classification (IVC) and the improvised Interval Value based Particle Swarm Optimization (IVPSO) algorithm. Thus the results show that the IVPSO algorithm outperformed compared with other algorithms under several performance evaluation functions.
Rabindra Kumar Sahu
Full Text Available This paper presents the design and analysis of Proportional-Integral-Double Derivative (PIDD controller for Automatic Generation Control (AGC of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization (TLBO algorithm. At first, a two-area reheat thermal power system with appropriate Generation Rate Constraint (GRC is considered. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PIDD controller. The superiority of the proposed TLBO based PIDD controller has been demonstrated by comparing the results with recently published optimization technique such as hybrid Firefly Algorithm and Pattern Search (hFA-PS, Firefly Algorithm (FA, Bacteria Foraging Optimization Algorithm (BFOA, Genetic Algorithm (GA and conventional Ziegler Nichols (ZN for the same interconnected power system. Also, the proposed approach has been extended to two-area power system with diverse sources of generation like thermal, hydro, wind and diesel units. The system model includes boiler dynamics, GRC and Governor Dead Band (GDB non-linearity. It is observed from simulation results that the performance of the proposed approach provides better dynamic responses by comparing the results with recently published in the literature. Further, the study is extended to a three unequal-area thermal power system with different controllers in each area and the results are compared with published FA optimized PID controller for the same system under study. Finally, sensitivity analysis is performed by varying the system parameters and operating load conditions in the range of ±25% from their nominal values to test the robustness.
N. M. Okasha
Full Text Available In this paper, an approach for conducting a Reliability-Based Design Optimization (RBDO of truss structures with linked-discrete design variables is proposed. The sections of the truss members are selected from the AISC standard tables and thus the design variables that represent the properties of each section are linked. Latin hypercube sampling is used in the evaluation of the structural reliability. The improved firefly algorithm is used for the optimization solution process. It was found that in order to use the improved firefly algorithm for efficiently solving problems of reliability-based design optimization with linked-discrete design variables; it needs to be modified as proposed in this paper to accelerate its convergence.
The primary objective of this research is to develop new parallel techniques for solving optimal control problems that occur in online real-time applications. In view of the availability of inexpensive yet powerful hardware, the use of parallel-processing techniques is proposed to satisfy both the speed constraints imposed by a real-time setting as well as the reliability requirements of an online system. Unlike previous parallel approaches to the solution of optimal control problems, the goal is to obtain an efficient solution by structuring the control algorithms to exhibit parallelism that matches the given machine architecture. In order to achieve the goal, this work reexamines optimal control problems from the perspective of their first-order optimality conditions so that the issues of parallelism and machine architecture may be considered in the forefront of the algorithm synthesis. Results include the development of an efficient parallel procedure for gradient evaluation. Embedded in the parallel-gradient evaluation procedure is a new technique for solving first-order linear recurrence systems, which is synthesized as a function of the number of available computers.
ZHANG Zhenxing∗; SUN Baomin; XIN Jing
In order to control NOx emissions and enhance boiler efficiency in coal-fired boilers,the thermal operating data from an ultra-supercritical 1 000 MW unit boiler were analyzed.On the basis of the support vector regression machine (SVM),the fruit fly optimization algorithm (FOA)was applied to optimize the penalty parameter C,ker-nel parameter g and insensitive loss coefficient of the model.Then,the FOA-SVM model was established to predict the NOx emissions and boiler efficiency,and the performance of this model was compared with that of the GA-SVM model optimized by genetic algorithm (GA).The results show the FOA-SVM model has better prediction accuracy and generalization capability,of which the maximum average relative error of testing set lies in the NOx emissions model,which is only 3 .5 9%.The above models can predict the NOx emissions and boiler efficiency accurately,so they are very suitable for on-line modeling prediction,which provides a good model foundation for further optimiza-tion operation of large capacity boilers.
贺建军; 喻寿益; 钟掘
A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings. The difference from GA is that AGA takes objective function as adaptability function directly, so it cuts down some unnecessary time expense because of float-point calculation of function conversion. The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented. It can be applied to a wide class of problems. The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA. The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.
Full Text Available A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of “high-performance sailing”. The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties. Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC platforms-they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL.
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally ...
Wang, Li; Jia, Pengfei; Huang, Tailai; Duan, Shukai; Yan, Jia; Wang, Lidan
An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C₆H₆), toluene (C₇H₈), formaldehyde (CH₂O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms' applications in all E-nose application areas.
Astudillo, Leslie; Castillo, Oscar
In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance.
Full Text Available The network of Chinese Waterborne Petroleum Logistics (CWPL is so complex that reasonably disposing and choosing Chinese Waterborne Petroleum Logistics Distribution Center (CWPLDC take on the important theory value and the practical significance. In the study, the network construct of CWPL distribution is provided and the corresponding mathematical model for locating CWPLDC is established, which is a nonlinear mixed interger model. In view of the nonlinerar programming characteristic of model, the genetic algorithm as the solution strategy is put forward here, the strategies of hybrid coding, constraint elimination , fitness function and genetic operator are given followed the algorithm. The result indicates that this model is effective and reliable. This method could also be applicable for other types of large-scale logistics distribution center optimization.
为了将微粒群优化(Particle Swarm Optimization,PSO)算法应用到离散领域,并使算法仍具有较佳的搜索性能,以信息素积累为基础,由离散状态的信息素构成微粒编码,重新构造算法的迭代公式.微粒信息素的变化基于信息素原先的情况、个体历史最优及全局历史最优.以信息素为依据进行离散状态的选择,由此提出一种基于信息素的离散PSO算法.将该方法应用于基于时延约束的最小能耗路由优化问题,能获得较好的路由优化结果,表明该算法具有优良的收敛性能.%To make particle swarm optimization algorithm with good search performance be applied to discrete domain, the particle coding is made up of the pheromones of discrete states based on the accumulation of pheromones. The iteration expressions are rebuilt. The changes of pheromones in particles are based on the original situation of pheromones, personal optimal pheromones and global optimal pheromones. The discrete states are chosen according to the pheromones. A discrete particle swarm optimization algorithm based on the pheromone is put forward. This method is applied to minimum energy routing optimization problem on the basis of delay constraints. The better routing optimization result shows that this algorithm possesses strong convergence capability.
Full Text Available This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR research and application.
Jie Zeng; Wei Nie
Many multi-objective evolutionary algorithms (MOEAs) can converge to the Pareto optimal front and work wel on two or three objectives, but they deteriorate when faced with many-objective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values (instead of the Pareto-dominance relation to select candidate solutions), have been regarded as promising schemes that yield more satisfactory re-sults than wel-known algorithms, such as non-dominated sort-ing genetic algorithm (NSGA-II) and strength Pareto evolution-ary algorithm (SPEA2). However, they can suffer from having a slow convergence speed. This paper proposes a new indicator-based multi-objective optimization algorithm, namely, the multi-objective shuffled frog leaping algorithm based on the ε indicator (ε-MOSFLA). This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capa-bility of global search and quick convergence as an evolutionary strategy and a simple and effective ε-indicator as a fitness as-signment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show thatε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as wel as the speed of convergence.
黄磊; 刘建业; 曾庆化
Traditional coning algorithms are based on the first-order coning correction reference model .Usually they reduce the algorithm error of coning axis (z) by increasing the sample numbers in one iteration interval .But the increase of sample numbers requires the faster output rates of sensors .Therefore ,the algorithms are often lim-ited in practical use .Moreover ,the noncommutivity error of rotation usually exists on all three axes and the in-crease of sample numbers has little positive effect on reducing the algorithm errors of orthogonal axes (x ,y) . Considering the errors of orthogonal axes cannot be neglected in the high-precision applications ,a coning algorithm with an additional second-order coning correction term is developed to further improve the performance of coning algorithm .Compared with the traditional algorithms ,the new second-order coning algorithm can effectively reduce the algorithm error without increasing the sample numbers .Theoretical analyses validate that in a coning environ-ment with low frequency ,the new algorithm has the better performance than the traditional time-series and fre-quency-series coning algorithms ,while in a maneuver environment the new algorithm has the same order accuracy as the traditional time-series and frequency-series algorithms .Finally ,the practical feasibility of the new coning al-gorithm is demonstrated by digital simulations and practical turntable tests .
López Cruz, I.L.
If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use of global optimisation algorithms to solve optimal control problems, wh
One of the greatest scientific achievements of physics in the 20th century is the discovery of quantum mechanics. The Schrodinger equation is the most fundamental equation in quantum mechanics describing the time-based evolution of the quantum state of a physical system. It has been found that the time-independent version of the equation can be derived from a general optimization algorithm. Instead of arguing for a new interpretation and possible deeper principle for quantum mechanics, this paper elaborates a few points of the equation as a general global optimization algorithm. Benchmarked against randomly generated hard optimization problems, this paper shows that the algorithm significantly outperformed a classic local optimization algorithm. The former found a solution in one second with a single trial better than the best one found by the latter around one hour after one hundred thousand trials.
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
Time optimal reachability analysis is a novel model based technique for solving scheduling and planning problems. After modeling them as reachability problems using timed automata, a real-time model checker can compute the fastest trace to the goal states which constitutes a time optimal schedule....... We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general....
In order to improve the picking efficiency, reduce the picking time, this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object. Discuss the picking task allocation and routing problems. Establish the TSP model of order-picking system. Create a heuristic algorithm bases on the Genetic Algorithm (GA) which help to solve the task allocating problem and to get the associated order-picking routes. And achieve the simulation experiment with the Visual 6.0C++platform to prove the rationality of the model and the effectiveness of the arithmetic.
ZHANG Jie; MA Hao; WU Zhen-yang
In the research on spatial hearing and virtual auditory space,it is important to effectively model the head-related transfer functions (HRTFs).Based on the analysis of the HRTFs' spectrum and some perspectives of psychoacoustics,this paper applied multiple demes' parallel and real-valued coding genetic algorithm (GA) to approximate the HRTFs' zero-pole model.Using the logarithmic magnitude's error criterion for the human auditory sense,the results show that the performance of the GA is on the average 39% better than that of the traditional Prony method,and 46% better than that of the Yule-Walker algorithm.
LI xiang; CUI Ji-feng; QI Jian-xun; YANG Shang-dong
In China, economic centers are far from energy storage bases, so it is significant to select a proper energy transferring mode to improve the efficiency of energy usage, To solve this problem, an optimal allocation model based on energy transfer mode was proposed after objective function for optimizing energy using efficiency Was established, and then, a new Tabu search and power transmission was gained.Based on the above discussion, some proposals were put forward for optimal allocation of energy transfer modes in China. By comparing other three traditional methodsthat are based on regional price differences. freight rates and annual cost witll the proposed method, the result indicates that the economic efficiency of the energy transfer Can be enhanced by 3.14%, 5.78% and 6.01%, respectively.
Full Text Available In order to increase the driving range and improve the overall performance of all-electric vehicles, a new dual-motor hybrid driving system with two power sources was proposed. This system achieved torque-speed coupling between the two power sources and greatly improved the high performance working range of the motors; at the same time, continuously variable transmission (CVT was achieved to efficiently increase the driving range. The power system parameters were determined using the “global optimization method”; thus, the vehicle’s dynamics and economy were used as the optimization indexes. Based on preliminary matches, quantum genetic algorithm was introduced to optimize the matching in the dual-motor hybrid power system. Backward simulation was performed on the combined simulation platform of Matlab/Simulink and AVL-Cruise to optimize, simulate, and verify the system parameters of the transmission system. Results showed that quantum genetic algorithms exhibited good global optimization capability and convergence in dealing with multiobjective and multiparameter optimization. The dual-motor hybrid-driving system for electric cars satisfied the dynamic performance and economy requirements of design, efficiently increasing the driving range of the car, having high performance, and reducing energy consumption of 15.6% compared with the conventional electric vehicle with single-speed reducers.
LI Xing-mei; ZHANG Li-hui; QI Jian-xun; ZHANG Su-fang
In order to study the problem that particle swarm optimization (PSO) algorithm can easily trap into local mechanism when analyzing the high dimensional complex optimization problems, the optimization calculation using the information in the iterative process of more particles was analyzed and the optimal system of particle swarm algorithm was improved. The extended particle swarm optimization algorithm (EPSO) was proposed. The coarse-grained and free-grained criteria that can control the selection were given to ensure the convergence of the algorithm. The two criteria considered the parameter selection mechanism under the situation of random probability. By adopting MATLAB7.1, the extended particle swarm optimization algorithm was demonstrated in the resource leveling of power project scheduling. EPSO was compared with genetic algorithm (GA) and common PSO, the result indicates that the variance of the objective function of resource leveling is decreased by 7.9%, 18.2%, respectively, certifying thee effectiveness and stronger global convergence ability of the EPSO.
Combinatorial optimization is one of the youngest and most active areas of discrete mathematics, and is probably its driving force today. This book describes the most important ideas, theoretical results, and algorithms of this field. It is conceived as an advanced graduate text, and it can also be used as an up-to-date reference work for current research. The book includes the essential fundamentals of graph theory, linear and integer programming, and complexity theory. It covers classical topics in combinatorial optimization as well as very recent ones. The emphasis is on theoretical results and algorithms with provably good performance. Some applications and heuristics are mentioned, too.
Full Text Available A single DC motor can be substituted by two different couple DC motors in submarines. By this way, by varying the speed of submarine, the power of propellant and subsequently the mechanical power of these motors would vary. One important promlem in controlling the mechanical coupling of these motors is the power sharing between them. In the previous reports the mechanical power was shared between them in nonoptimized manner. In this paper an optimized cantroller is indroduced that optimize the efficiency of the system. The power sharing between these motors would vary according to their speed. The proposed controller is based on Genetic Algoritm and is able to share the mechanical power between the motors in an optimized manner at different speeds. The simutation results shows the well behavior of system and also the optimize power sharing.
Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.
Full Text Available Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs. In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors-the ridge distance features, global gray features, frequency feature and Harris Corner feature-are extracted. Then, a support vector machine (SVM is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%.
Chen, Suhang; Chang, Sheng; Huang, Qijun; He, Jin; Wang, Hao; Huang, Qiangui
Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors-the ridge distance features, global gray features, frequency feature and Harris Corner feature-are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%.
张火明; 黄赛花; 管卫兵
The highest similarity degree of static characteristics including both horizontal and vertical restoring force-displacement characteristics of total mooring system, as well as the tension-displacement characteristics of the representative single mooring line between the truncated and full depth system are obtained by annealing simulation algorithm for hybrid discrete variables (ASFHDV, in short). A“baton” optimization approach is proposed by utilizing ASFHDV. After each baton of optimization, if a few dimensional variables reach the upper or lower limit, the boundary of certain dimensional variables shall be expanded. In consideration of the experimental requirements, the length of the upper mooring line should not be smaller than 8 m, and the diameter of the anchor chain on the bottom should be larger than 0.03 m. A 100000 t turret mooring FPSO in the water depth of 304 m, with the truncated water depth being 76 m, is taken as an example of equivalent water depth truncated mooring system optimal design and calculation, and is performed to obtain the conformation parameters of the truncated mooring system. The numerical results indicate that the present truncated mooring system design is successful and effective.
Lutz, Adam; Giansiracusa, Michael; Messer, Neal; Ezekiel, Soundararajan; Blasch, Erik; Alford, Mark
Multi-focus image fusion is becoming increasingly prevalent, as there is a strong initiative to maximize visual information in a single image by fusing the salient data from multiple images for visualization. This allows an analyst to make decisions based on a larger amount of information in a more efficient manner because multiple images need not be cross-referenced. The contourlet transform has proven to be an effective multi-resolution transform for both denoising and image fusion through its ability to pick up the directional and anisotropic properties while being designed to decompose the discrete two-dimensional domain. Many studies have been done to develop and validate algorithms for wavelet image fusion, but the contourlet has not been as thoroughly studied. When the contourlet coefficients for the wavelet coefficients are substituted in image fusion algorithms, it is contourlet image fusion. There are a multitude of methods for fusing these coefficients together and the results demonstrate that there is an opportunity for fusing coefficients together in the contourlet domain for multi-focus images. This paper compared the algorithms with a variety of no reference image fusion metrics including information theory based, image feature based and structural similarity based assessments to select the image fusion method.
Full Text Available Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA.
Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping
Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA.
Sarvesh Kumar Sharma
Full Text Available In this paper different optimization of Ad-hoc routing algorithm is surveyed and a new method using training based optimization algorithm for reducing the complexity of routing algorithms is suggested. A binary matrix is assigned to each node in the network and gets updated after each data transfer using the protocols. The use of optimization algorithm in routing algorithm can reduce the complexity of routing to the least amount possible.
Xu, Lili; Luo, Shuqian
Microaneurysms (MAs) are the first manifestations of the diabetic retinopathy (DR) as well as an indicator for its progression. Their automatic detection plays a key role for both mass screening and monitoring and is therefore in the core of any system for computer-assisted diagnosis of DR. The algorithm basically comprises the following stages: candidate detection aiming at extracting the patterns possibly corresponding to MAs based on mathematical morphological black top hat, feature extraction to characterize these candidates, and classification based on support vector machine (SVM), to validate MAs. Feature vector and kernel function of SVM selection is very important to the algorithm. We use the receiver operating characteristic (ROC) curve to evaluate the distinguishing performance of different feature vectors and different kernel functions of SVM. The ROC analysis indicates the quadratic polynomial SVM with a combination of features as the input shows the best discriminating performance.
Liu, Yafan; Qian, Chen; Cao, Bingyao; Dun, Han; Shi, Yan; Zou, Junni; Lin, Rujian; Wang, Min
Orthogonal frequency division multiple access passive optical network (OFDMA-PON) has being considered as a promising solution for next generation PONs due to its high spectral efficiency and flexible bandwidth allocation scheme. In order to take full advantage of these merits of OFDMA-PON, a high-efficiency medium access control (MAC) dynamic bandwidth allocation (DBA) scheme is needed. In this paper, we propose two DBA algorithms which can act on two different stages of a resource allocation process. To achieve higher bandwidth utilization and ensure the equity of ONUs, we propose a DBA algorithm based on frame structure for the stage of physical layer mapping. Targeting the global quality of service (QoS) of OFDMA-PON, we propose a full-range DBA algorithm with service level agreement (SLA) and class of service (CoS) for the stage of bandwidth allocation arbitration. The performance of the proposed MAC DBA scheme containing these two algorithms is evaluated using numerical simulations. Simulations of a 15 Gbps network with 1024 sub-carriers and 32 ONUs demonstrate the maximum network throughput of 14.87 Gbps and the maximum packet delay of 1.45 ms for the highest priority CoS under high load condition.
Full Text Available A mathematical model of electroslag remelting (ESR process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID controller tuned optimally by an improved particle swarm optimization (IPSO algorithm is proposed to control the two-input/two-output (TITO ESR process. An adaptive chaotic migration mutation operator is used to tackle the particles trapped in the clustering field in order to enhance the diversity of the particles in the population, prevent premature convergence and improve the search efficiency of PSO algorithm. The simulation results show the feasibility and effectiveness of the proposed control method. The new method can overcome dynamic working conditions and coupling features of the system in a wide range, and it has strong robustness and adaptability.
Full Text Available The objective of vehicle routing problem is usually to minimize the total traveling distance or cost. But in practice, there are a lot of problems needed to minimize the fastest completion time. The milk-run vehicle routing problem (MRVRP is widely used in milk-run distribution. The mutation ACO is given to solve MRVRP with fastest completion time in this paper. The milk-run VRP with fastest completion time is introduced first, and then the customer division method based on dynamic optimization and split algorithm is given to transform this problem into finding the optimal customer order. At last the mutation ACO is given and the numerical examples verify the effectiveness of the algorithm.
Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu
This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.
Chi, Chih-Lin; Street, W Nick; Ward, Marcia M
This study presents a new method for constructing an expert system using a hospital referral problem as an example. Many factors, such as institutional characteristics, patient risks, traveling distance, and chances of survival and complications should be included in the hospital-selection decision. Ideally, each patient should be treated individually, with the decision process including not only their condition but also their beliefs about trade-offs among the desired hospital features. An expert system can help with this complex decision, especially when numerous factors are to be considered. We propose a new method, called the Prediction and Optimization-Based Decision Support System (PODSS) algorithm, which constructs an expert system without an explicit knowledge base. The algorithm obtains knowledge on its own by building machine learning classifiers from a collection of labeled cases. In response to a query, the algorithm gives a customized recommendation, using an optimization step to help the patient maximize the probability of achieving a desired outcome. In this case, the recommended hospital is the optimal solution that maximizes the probability of the desired outcome. With proper formulation, this expert system can combine multiple factors to give hospital-selection decision support at the individual level.
Full Text Available Problem statement: Network topology design problems find application in several real life scenario. Approach: Most designs in the past either optimize for a single criterion like shortest or cost minimization or maximum flow. Results: This study discussed about solving a multi objective network topology design problem for a realistic traffic model specifically in the pipeline transportation. Here flow based algorithm focusing to transport liquid goods with maximum capacity with shortest distance, this algorithm developed with the sense of basic pert and critical path method. Conclusion/Recommendations: This flow based algorithm helps to give optimal result for transporting maximum capacity with minimum cost. It could be used in the juice factory, milk industry and its best alternate for the vehicle routing problem.
Full Text Available The last few years have seen an increased interest in the potential use of wireless sensor networks (WSNs in various fields like disaster management, battle field surveillance, and border security surveillance. In such applications, a large number of sensor nodes are deployed, which are often unattended and work autonomously. The process of dividing the network into interconnected substructures is called clustering and the interconnected substructures are called clusters. The cluster head (CH of each cluster act as a coordinator within the substructure. Each CH acts as a temporary base station within its zone or cluster. It also communicates with other CHs. Clustering is a key technique used to extend the lifetime of a sensor network by reducing energy consumption. It can also increase network scalability. Researchers in all fields of wireless sensor network believe that nodes are homogeneous, but some nodes may be of different characteristics to prolong the lifetime of a WSN and its reliability. We have proposed an algorithm for better cluster head selection based on weights for different parameter that influence on energy consumption which includes distance from base station as a new parameter to reduce number of transmissions and reduce energy consumption by sensor nodes. Finally proposed algorithm compared with the WCA, IWCA algorithm in terms of number of clusters and energy consumption.
Koziel, Slawomir; Bandler, John W.; Madsen, Kaj
A simple, efficient optimization algorithm based on space mapping (SM) is presented. It utilizes input SM to reduce the misalignment between the coarse and fine models of the optimized object over a region of interest, and output space mapping (OSM) to ensure matching of response and first......-order derivatives between the mapped coarse model and the fine model at the current iteration point. We also consider an enhanced version in which the input SM coefficients are frequency dependent. The performance of our new algorithms is comparable with the recently published SMIS algorithm when applied...... to a benchmark problem. In comparison with SMIS, the models presented are simple and have a small number of parameters that need to be extracted. The new algorithm is applied to the optimization of coupled-line band-pass filter....
郭崇慧; 陆玉昌; 唐焕文
A modification of evolutionary programming or evolution strategies for n-dimensional global optimization is proposed. Based on the ergodicity and inherent-randomness of chaos, the main characteristic of the new algorithm which includes two phases is that chaotic behavior is exploited to conduct a rough search of the problem space in order to find the promising individuals in Phase Ⅰ. Adjustment strategy of step-length and intensive searches in Phase Ⅱ are employed.The population sequences generated by the algorithm asymptotically converge to global optimal solutions with probability one. The proposed algorithm is applied to several typical test problems. Numerical results illustrate that this algorithm can more efficiently solve complex global optimization problems than evolutionary programming and evolution strategies in most cases.
Full Text Available The unbalanced assignment problem (UAP is to optimally resolve the problem of assigning n jobs to m individuals (m < n, such that minimum cost or maximum profit obtained. It is a vitally important Non-deterministic Polynomial (NP complete problem in operation management and applied mathematics, having numerous real life applications. In this paper, we present a new parallel DNA algorithm for solving the unbalanced assignment problem using DNA molecular operations. We reasonably design flexible-length DNA strands representing different jobs and individuals, take appropriate steps, and get the solutions of the UAP in the proper length range and O(mn time. We extend the application of DNA molecular operations and simultaneity to simplify the complexity of the computation.
A. S. M. Zahid Kausar; Ahmed Wasif Reza; Lau Chun Wo; Harikrishnan Ramiah
Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented...
Kausar, A. S. M. Zahid; Reza, Ahmed Wasif; Wo, Lau Chun; Ramiah, Harikrishnan
Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented ...
An efficient importance sampling algorithm is presented to analyze reliability of complex structural system with multiple failure modes and fuzzy-random uncertainties in basic variables and failure modes. In order to improve the sampling efficiency, the simulated annealing algorithm is adopted to optimize the density center of the importance sampling for each failure mode, and results that the more significant contribution the points make to fuzzy failure probability, the higher occurrence possibility the points are sampled. For the system with multiple fuzzy failure modes, a weighted and mixed importance sampling function is constructed. The contribution of each fuzzy failure mode to the system failure probability is represented by the appropriate factors, and the efficiency of sampling is improved furthermore. The variances and the coefficients of variation are derived for the failure probability estimations. Two examples are introduced to illustrate the rationality of the present method. Comparing with the direct Monte-Carlo method, the improved efficiency and the precision of the method are verified by the examples.
韩志甲; 邓海峡; 李晓平
With the fast development of Internet, many systems have emerged in e-commerce applications to support the product recommendation. Collaborative filtering is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. In practice, with the adding of user and item scales, user-item ratings are becoming extremely sparsity and recommender systems utilizing traditional collaborative filtering are facing serious challenges. To address the issue, this paper presents an approach to compute item genre similarity, through mapping each item with a corresponding descriptive genre, and computing similarity between genres as similarity, then make basic predictions according to those similarities to lower sparsity of the user-item ratings. After that, item-based collaborative filtering steps are taken to generate predictions. Compared with previous methods, the presented collaborative filtering employs the item genre similarity can alleviate the sparsity issue in the recommender systems, and can improve accuracy of recommendation.
Mohamed M. Marzouk
Full Text Available Construction of low-income housing projects is a replicated process and is associated with uncertainties that arise from the unavailability of resources. Government agencies and/or contractors have to select a construction system that meets low-income housing projects constraints including project conditions, technical, financial and time constraints. This research presents a framework, using computer simulation, which aids government authorities and contractors in the planning of low-income housing projects. The proposed framework estimates the time and cost required for the construction of low-income housing using pre-cast hollow core with hollow blocks bearing walls. Five main components constitute the proposed framework: a network builder module, a construction alternative selection module, a simulation module, an optimization module and a reporting module. An optimization module utilizing a genetic algorithm enables the defining of different options and ranges of parameters associated with low-income housing projects that influence the duration and total cost of the pre-cast hollow core with hollow blocks bearing walls method. A computer prototype, named LIHouse_Sim, was developed in MS Visual Basic 6.0 as proof of concept for the proposed framework. A numerical example is presented to demonstrate the use of the developed framework and to illustrate its essential features.
Yang, Yi-Chen E.; Cai, Ximing; Stipanović, DušAn M.
A watershed can be simulated as a multiagent system (MAS) composed of spatially distributed land and water users (agents) within a common defined environment. The watershed system is characterized by distributed decision processes at the agent level with a coordination mechanism organizing the interactions among individual decision processes at the system level. This paper presents a decentralized (distributed) optimization method known as constraint-based reasoning, which allows individual agents in an MAS to optimize their behaviors over various alternatives. The method incorporates the optimization of all agents' objectives through an interaction scheme, in which the ith agent optimizes its objective with a selected priority for collaboration and forwards the solution and consequences to all agents that interact with it. Agents are allowed to determine how important their own objectives are in comparison with the constraints, using a local interest factor (βi). A large βi value indicates a selfish agent who puts high priority on its own benefit and ignores collaboration requirements. This bottom-up problem-solving approach mimics real-world watershed management problems better than conventional "top-down" optimization methods in which it is assumed that individual agents will completely comply with any recommendations that the coordinator makes. The method is applied to a steady state hypothetical watershed with three off-stream human agents, one in-stream human agent (reservoir), and two ecological agents.
WangWenjun; YuLongjiang; HePu; ZhouPengpeng
Genetic algorithms (GA) based on the principle of mimicing Darwinian evolution and survival of the fittest in a natural environment was used to optimize the medium for astaxanthin production by the mutant strain W6-8 of Xantho-phyllomyces dendrorhous. The 50 concentration levels of 6 medium components were optimized within 50 experiments (full experimental plan: 506 experiments). The results showed that GA could be applied in the medium optimization and better results were obtained. By employing optimized medium components (glucose 39.8 g l-1, yeast extract 4.08 g l-1,(NH4)2SO4 7.36 g l-1, MgSO4 2 g l-1, K2HPO4 2.04 g l-1 and KH2PO4 3.48 g l-1), the highest astaxanthin production was 9.855 mg l-1, approximately 31% higher than that under the initial conditions, and was approximately 15.46% higher than that by orthogonal array but only slightly higher than that by response surface methodology. In the sequent scale-up experiments, the astaxanthin yield was obtained approximately 14.753 mg l-1, employing the optimal medium. The results indicated that GA, as an euiicient method for medium optimization, was superior to other optimal means such as orthogonal array.
Li, Wuzhao; Wang, Lei; Cai, Xingjuan
In classic evolutionary algorithms (EAs), solutions communicate each other in a very simple way so the recombination operator design is simple, which is easy in algorithms’ implementation. However, it is not in accord with nature world. In nature, the species have various kinds of relationships...... and affect each other in many ways. The relationships include competition, predation, parasitism, mutualism and pythogenesis. In this paper, we consider the five relationships between solutions to propose a co-evolutionary algorithm termed species co-evolutionary algorithm (SCEA). In SCEA, five operators...... are designed to recombine individuals in population. A set including several classical benchmarks are used to test the proposed algorithm. We also employ several other classical EAs in comparisons. The comparison results show that SCEA exhibits an excellent performance to show a huge potential of SCEA...
Mahesh S. Narkhede
Full Text Available An attempt has been made in this article to compare the performances of two multiobjective evolutionary algorithms namely ev-MOGA and GODLIKE. The performances of both are evaluated on risk based optimal power scheduling of virtual power plant. The risk based scheduling is proposed as a conflicting bi objective optimization problem with increased number of durations of day. Both the algorithms are elaborated in detail. Results based on the performance analysis are depicted at the end.
A multiple-objective evolutionary algorithm (MOEA) with a new Decision Making (DM) scheme for MOD of conceptual missile shapes was presented, which is contrived to determine suitable tradeoffs from Pareto optimal set using interactive preference articulation. There are two objective functions, to maximize ratio of lift to drag and to minimize radar cross-section (RCS) value. 3D computational electromagnetic solver was used to evaluate RCS, electromagnetic performance. 3D Navier-Stokes flow solver was adopted to evaluate aerodynamic performance. A flight mechanics solver was used to analyze the stability of the missile. Based on the MOEA, a synergetic optimization of missile shapes for aerodynamic and radar cross-section performance is completed. The results show that the proposed approach can be used in more complex optimization case of flight vehicles.
Schjær-Jacobsen, Hans; Madsen, Kaj
New algorithms are presented for the solution of optimum tolerance assignment problems. The problems considered are defined mathematically as a worst-case problem (WCP), a fixed tolerance problem (FTP), and a variable tolerance problem (VTP). The basic optimization problem without tolerances...... is denoted the zero tolerance problem (ZTP). For solution of the WCP we suggest application of interval arithmetic and also alternative methods. For solution of the FTP an algorithm is suggested which is conceptually similar to algorithms previously developed by the authors for the ZTP. Finally, the VTP...... is solved by a double-iterative algorithm in which the inner iteration is performed by the FTP- algorithm. The application of the algorithm is demonstrated by means of relatively simple numerical examples. Basic properties, such as convergence properties, are displayed based on the examples....
Wang, Yong; Cai, Zixing; Zhou, Yuren;
mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...
Zhang, Ying; Liang, Jixing; Jiang, Shengming; Chen, Wei
Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.
Cui Changqing; Wang Yiqiang
Today, automatic warehouse system gradually replaced manual labor, and played an important role in the production work, especially in the cargo handling work. It was important to research the time-consuming and efficiency of stacker in the automated warehouse system. This paper researched the path of stacker in automated warehouse and calculated the operation time of stacker working path according to actual working condition, and then put forward a route optimization method of stacker based o...
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
This paper proposes an epilepsy detection and closed-loop control strategy based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy can effectively suppress the epileptic spikes in neural mass models, where the epileptiform spikes are recognized as the biomarkers of transitions from the normal (interictal) activity to the seizure (ictal) activity. In addition, the PSO algorithm shows capabilities of accurate estimation for the time evolution of key model parameters and practical detection for all the epileptic spikes. The estimation effects of unmeasurable parameters are improved significantly compared with unscented Kalman filter. When the estimated excitatory-inhibitory ratio exceeds a threshold value, the epileptiform spikes can be inhibited immediately by adopting the proportion-integration controller. Besides, numerical simulations are carried out to illustrate the effectiveness of the proposed method as well as the potential value for the model-based early seizure detection and closed-loop control treatment design.
Full Text Available Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.
Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed
Recently, a new metaheuristic called chemical reaction optimization was proposed. This search algorithm, inspired by chemical reactions launched during collisions, inherits several features from other metaheuristics such as simulated annealing and particle swarm optimization. This fact has made it, nowadays, one of the most powerful search algorithms in solving mono-objective optimization problems. In this paper, we propose a multiobjective variant of chemical reaction optimization, called nondominated sorting chemical reaction optimization, in an attempt to exploit chemical reaction optimization features in tackling problems involving multiple conflicting criteria. Since our approach is based on nondominated sorting, one of the main contributions of this paper is the proposal of a new quasi-linear average time complexity quick nondominated sorting algorithm; thereby making our multiobjective algorithm efficient from a computational cost viewpoint. The experimental comparisons against several other multiobjective algorithms on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of this multiobjective version in providing a well-converged and well-diversified approximation of the Pareto front.
Sen, S. K.; Shaykhian, Gholam A.
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.
Li, Si Jia; Cao, Xiang Yu; Xu, Li Ming; Zhou, Long Jian; Yang, Huan Huan; Han, Jiang Feng; Zhang, Zhao; Zhang, Di; Liu, Xiao; Zhang, Chen; Zheng, Yue Jun; Zhao, Yi
We proposed an ultra-broadband reflective metamaterial with controlling the scattering electromagnetic fields based on a polarization convertor. The unit cell of the polarization convertor was composed of a three layers substrate with double metallic split-rings structure and a metal ground plane. The proposed polarization convertor and that with rotation angle of 90 deg had been employed as the “0” and “1” elements to design the digital reflective metamaterial. The numbers of the “0” and “1” elements were chosen based on the information entropy theory. Then, the optimized combinational format was selected by genetic optimization algorithm. The scattering electromagnetic fields had been manipulated due to destructive interference, which was attributed to the control of phase and amplitude by the proposed polarization convertor. Simulated and experimental results indicated that the reflective metamaterial exhibited significantly RCS reduction in an ultra-broad frequency band for both normal and oblique incidences.
Zheng, Ling; Duan, Xuwei; Deng, Zhaoxue; Li, Yinong
A novel flow-mode magneto-rheological (MR) engine mount integrated a diaphragm de-coupler and the spoiler plate is designed and developed to isolate engine and the transmission from the chassis in a wide frequency range and overcome the stiffness in high frequency. A lumped parameter model of the MR engine mount in single degree of freedom system is further developed based on bond graph method to predict the performance of the MR engine mount accurately. The optimization mathematical model is established to minimize the total of force transmissibility over several frequency ranges addressed. In this mathematical model, the lumped parameters are considered as design variables. The maximum of force transmissibility and the corresponding frequency in low frequency range as well as individual lumped parameter are limited as constraints. The multiple interval sensitivity analysis method is developed to select the optimized variables and improve the efficiency of optimization process. An improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization problem. The synthesized distance between the individual in Pareto set and the individual in possible set in engineering is defined and calculated. A set of real design parameters is thus obtained by the internal relationship between the optimal lumped parameters and practical design parameters for the MR engine mount. The program flowchart for the improved non-dominated sorting genetic algorithm (NSGA-II) is given. The obtained results demonstrate the effectiveness of the proposed optimization approach in minimizing the total of force transmissibility over several frequency ranges addressed.
Full Text Available Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO algorithm and the land-surface process model SHAW (Simultaneous Heat and Water, the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.
Aas, Lars Martin S; Ellingsen, Pål G; Letnes, Paul Anton; Kildemo, Morten
The design of broad-band polarimeters with high performance is challenging due to the wavelength dependence of optical components. An efficient Genetic Algorithm (GA) computer code was recently developed in order to design and re-optimize complete broadband Stokes polarimeters and Mueller matrix ellipsometers (MME). Our results are improvements of previous patented designs based on two and three ferroelectric liquid crystals (FLC), and are suited for broad-band hyperspectral imaging, or multichannel spectroscopy applications. We have realized and implemented one design using two FLCs and compare the spectral range and precision with previous designs.
WANG Yunfeng; BIAN Jinian; HONG Xianlong; ZHOU Qiang; WU Qiang
As the feature size of integrated circuits is reduced to the deep sub-micron level or the nanometer level, the interconnect delay is becoming more and more important in determining the total delay of a circuit. Re-synthesis after floorptan is expected to be very helpful for reducing the interconnect delay of a circuit. In this paper,a force-balance-based re-synthesis algorithm for interconnect delay o ptimization after floorplan is proposed. The algorithm optimizes the inter connect delay by changing the operation scheduling and the functional unit allocation andbinding. With this method the number and positions of all functional units are not changed, but some operations are allocated or bound to different units. Preliminary experimental results show that the interconnect wire delays are reduced efficiently without destroying the floorplan performance.
Mao, Heng; Zhao, Dazun
A modified Levenberg-Marquardt (MLM) algorithm is proposed to substitute for modified G-S (MGS) algorithm in some situations of phase-diverse phase retrieval wavefront sensing (WFS), such as the obstructed pupil, in which the second derivative information is specifically employed to eliminate the local minimum stagnation. Experiments have been performed to validate MLM algorithm in WFS accuracy (less than lambda/30 RMS) referring to ZYGO interferometer results and in WFS repeatability (less than lambda/200 RMS), even the dynamic range is more than 7 lambda PV. Moreover, experiments have shown the MLM algorithm is superior to the MGS algorithm both in WFS accuracy and repeatability.
Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.
LIU Xue-Ming; LI Yan-He
@@ Solutions of dual-pump fibre-optical parametric amplifiers (DP-FOPAs) with dispersion fluctuations are derived by using a matrix operator. Based on these solutions and a hybrid genetic algorithm, we have optimized threesection DP-FOPAs to increase the signal band and improve the gain uniformity. The optimizations demonstrate that when dispersion fluctuations are taken into account, the 44-nm signal band with the 0.37-dB ripple and over 14.8-dB gain can be obtained from the three-section DP-FOPA, instead of the lowest gain of ～13dB with the ripple of more than 15dB from the single-section DP-FOPA.
董方; 谢磊; 张建明
自适应粒子群算法(Adaptive Particle Swarm Optimization,APSO)是一种参数自适应的种群智能算法.该算法以种群的分布状态为依据区分优化过程中的不同状态自适应地调整算法参数.基于APSO算法具有参数自适应、快速收敛、全局搜索能力强等优点.将APSO算法应用于动态优化,通过采用按变量比例分配时间的方法构造时间变量,从而将其转化为无约束变量,通过时间变量与控制变量构造控制输入函数控制动态系统,使其达到最优.该方法提供一种转换时间变量约束的方法,使其能够作为一般优化问题,适用于其他类似演化类算法的动态性能的测试.最后,通过4个经典动态优化测试函数,比较APSO算法与蚁群算法,体现APSO算法处理动态优化的性能.%Adaptive particle swarm optimizatioin (APSO) is a swarm intelion algorithm, which regulates parameters adaptively during the optimization process according to the distribution state of the swarm particle. Due to its advantages of paramater adaptively regulating, fast converging and golbal optimizing, we apply the APSO algorithm into the dynamic optimization problems. We free the constraint of the time variable through the method of proportional dividing medthod, and contruct the control function combing input and time variables. Thanks to this method we change this kind of dynamic problems into a basic optimization problems, which makes them applicable to the test of other intelligence based optimization algorithms. In the end, we compare simulation result with the Ant colony algorithm which has the similar mechanism with PSO.
Wei, Zhonghai; Wei, Qingguo
Common spatial pattern (CSP) is a powerful algorithm for extracting discriminative brain patterns in motor imagery-based brain-computer interfaces (BCIs). However, its performance depends largely on the subject-specific frequency band and time segment. Accurate selection of most responsive frequency band and time segment remains a crucial problem. A novel evolutionary algorithm, the backtracking search optimization algorithm is used to find the optimal frequency band and the optimal combination of frequency band and time segment. The former is searched by a frequency window with changing width of which starting and ending points are selected by the backtracking optimization algorithm; the latter is searched by the same frequency window and an additional time window with fixed width. The three parameters, the starting and ending points of frequency window and the starting point of time window, are jointly optimized by the backtracking search optimization algorithm. Based on the chosen frequency band and fixed or chosen time segment, the same feature extraction is conducted by CSP and subsequent classification is carried out by Fisher discriminant analysis. The classification error rate is used as the objective function of the backtracking search optimization algorithm. The two methods, named BSA-F CSP and BSA-FT CSP, were evaluated on data set of BCI competition and compared with traditional wideband (8-30[Formula: see text]Hz) CSP. The classification results showed that backtracking search optimization algorithm can find much effective frequency band for EEG preprocessing compared to traditional broadband, substantially enhancing CSP performance in terms of classification accuracy. On the other hand, the backtracking search optimization algorithm for joint selection of frequency band and time segment can find their optimal combination, and thus can further improve classification rates.
In order to prevent standard genetic algorithm (SGA) from being premature, chaos is introduced into GA,thus forming chaotic anneal genetic algorithm (CAGA). Chaos' ergodicity is used to initialize the population, and chaoticanneal mutation operator is used as the substitute for the mutation operator in SGA. CAGA is a unified framework of theexisting chaotic mutation methods. To validate the proposed algorithm, three algorithms, i.e. Baum-Welch, SGA andCAGA, are compared on training hidden Markov model (HMM) to recognize the hand gestures. Experiments on twenty-six alphabetical gestures show the CAGA's validity.
Deb, Kalyanmoy; Mohan, Manikanth; Mishra, Shikhar
Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the epsilon-dominance concept introduced earlier(Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the epsilon-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.
Klymenko, M. V.; Remacle, F., E-mail: firstname.lastname@example.org [Department of Chemistry, B6c, University of Liege, B4000 Liege (Belgium)
A methodology is proposed for designing a low-energy consuming ternary-valued full adder based on a quantum dot (QD) electrostatically coupled with a single electron transistor operating as a charge sensor. The methodology is based on design optimization: the values of the physical parameters of the system required for implementing the logic operations are optimized using a multiobjective genetic algorithm. The searching space is determined by elements of the capacitance matrix describing the electrostatic couplings in the entire device. The objective functions are defined as the maximal absolute error over actual device logic outputs relative to the ideal truth tables for the sum and the carry-out in base 3. The logic units are implemented on the same device: a single dual-gate quantum dot and a charge sensor. Their physical parameters are optimized to compute either the sum or the carry out outputs and are compatible with current experimental capabilities. The outputs are encoded in the value of the electric current passing through the charge sensor, while the logic inputs are supplied by the voltage levels on the two gate electrodes attached to the QD. The complex logic ternary operations are directly implemented on an extremely simple device, characterized by small sizes and low-energy consumption compared to devices based on switching single-electron transistors. The design methodology is general and provides a rational approach for realizing non-switching logic operations on QD devices.
Klymenko, M. V.; Remacle, F.
A methodology is proposed for designing a low-energy consuming ternary-valued full adder based on a quantum dot (QD) electrostatically coupled with a single electron transistor operating as a charge sensor. The methodology is based on design optimization: the values of the physical parameters of the system required for implementing the logic operations are optimized using a multiobjective genetic algorithm. The searching space is determined by elements of the capacitance matrix describing the electrostatic couplings in the entire device. The objective functions are defined as the maximal absolute error over actual device logic outputs relative to the ideal truth tables for the sum and the carry-out in base 3. The logic units are implemented on the same device: a single dual-gate quantum dot and a charge sensor. Their physical parameters are optimized to compute either the sum or the carry out outputs and are compatible with current experimental capabilities. The outputs are encoded in the value of the electric current passing through the charge sensor, while the logic inputs are supplied by the voltage levels on the two gate electrodes attached to the QD. The complex logic ternary operations are directly implemented on an extremely simple device, characterized by small sizes and low-energy consumption compared to devices based on switching single-electron transistors. The design methodology is general and provides a rational approach for realizing non-switching logic operations on QD devices.
Tung-Kuan Liu; Chiu-Hung Chen; Zu-Shu Li; Jyh-Horng Chou
This article presents a multiobjective approach to the design of the controller for the swing-up and handstand control of a general cart-double-pendulum system (CDPS).The designed controller,which is based on the human-simulated intelligent control (HSIC) method,builds up different control modes to monitor and control the CDPS during four kinetic phases consisting of an initial oscillation phase,a swing-up phase,a posture adjustment phase,and a balance control phase.For the approach,the original method of inequalities-based (MoI) multiobjective genetic algorithm (MMGA) is extended and applied to the case study which uses a set of performance indices that includes the cart displacement over the rail boundary,the number of swings,the settling time,the overshoot of the total energy,and the control effort.The simulation results show good responses of the CDPS with the controllers obtained by the proposed approach.
Full Text Available To produce the final product, parts need to be fabricated in the process stages and thereafter several parts are joined under the assembly operations based on the predefined bill of materials. But assembly relationship between the assembly parts and components has not been considered in general job shop scheduling problem model. The aim of this research is to find the schedule which minimizes completion time of Assembly Job Shop Scheduling Problem (AJSSP. Since the complexity of AJSSP is NP-hard, a hybrid particle swarm optimization (HPSO algorithm integrated PSO with Artificial Immune is proposed and developed to solve AJSSP. The selection strategy based on antibody density makes the particles of HPSO maintain the diversity during the iterative process, thus overcoming the defect of premature convergence. Then HPSO algorithm is applied into a case study development from classical FT06. Finally, the effect of key parameters on the proposed algorithm is analyzed and discussed regarding how to select the parameters. The experiment result confirmed its practice and effectiveness.
Full Text Available Optimization problems are considered to be one kind of NP hard problems. Usually heuristic approaches are found to provide solutions for NP hard problems. There are a plenty of heuristic algorithmsavailable to solve optimization problems namely: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, etc. The basic Bee Colony algorithm, a population based search algorithm, is analyzed to be a novel tool for complex optimization problems. The algorithm mimics the food foraging behavior of swarmsof honey bees. This paper deals with a modified fitness function of Bee Colony algorithm. The effect of problem dimensionality on the performance of the algorithms will be investigated. This enhanced Bee Colony Optimization will be evaluated based on the well-known benchmark problems. The testing functions like Rastrigin, Rosenbrock, Ackley, Griewank and Sphere are used to evaluavate the performance of the enhanced Bee Colony algorithm. The simulation will be developed on MATLAB.
Yang, Dan; Liu, Juan; Zhang, Yingxi; Li, Xin; Wang, Yongtian
Holographic display has been considered as a promising display technology. Currently, low-speed generation of holograms with big holographic data is one of crucial bottlenecks for three dimensional (3D) dynamic holographic display. To solve this problem, the acceleration method computation platform is presented based on look-up table point source method. The computer generated holograms (CGHs) acquisition is sped up by offline file loading and inline calculation optimization, where a pure phase CGH with gigabyte data is encoded to record an object with 10 MB sampling data. Both numerical simulation and optical experiment demonstrate that the CGHs with 1920×1080 resolution by the proposed method can be applied to the 3D objects reconstruction with high quality successfully. It is believed that the CGHs with huge data can be generated by the proposed method with high speed for 3D dynamic holographic display in near future.
Relayed transmission increases the coverage and achievable capacity of communication systems. Adaptive relaying scheme is a relaying technique by which the benefits of the amplifying or decode and forward techniques can be achieved by switching the forwarding technique according to the quality of the signal. A cognitive Orthogonal Frequency-Division Multiplexing (OFDM) based adaptive relaying protocol is considered in this paper. The objective is to maximize the capacity of the cognitive radio system while ensuring that the interference introduced to the primary user is below the tolerated limit. A Near optimal power allocation in the source and the relay is presented for two pairing techniques such that the matching and random pairing. The simulation results confirm the efficiency of the proposed adaptive relaying protocol, and the consequence of choice of pairing technique. © 2012 ICST.
Full Text Available The discontinuous space vector pulse width modulation (SVPWM has well-known that can reduce switching losses. By simplifying the thermal management issues, the discontinuous SVPWM can potentially reduce the inverter size and cost. However, using the modulation due to different time interval equations for each sector can introduce glitches at the points when the sector is changed. The more main problem, it can increase unwanted harmonic content and current ripple. Consider the decrease in switching losses associated with discontinuous modulation allows the system to utilize a higher switching frequency, this paper present high frequency switching of optimized discontinuous SVPWM based on FPGA to overcome the problems above. The proposed SVPWM has been successfully implemented by using APEX20KE Altera FPGA to drive on a three phase inverter system with 1.5 kW induction machine as load. The results have proved that the method can reduce harmonic content and current ripple without glitches.
Wang Ziqiang; Feng Boqin
To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.
Zuo Kongtian; Zhao Yudong; Chen Liping; Zhong Yifang; Huang Yuying
A new hybrid MMA-MGCMMA (HMM) algorithm for solving topology optimization problems is presented. This algorithm combines the method of moving asymptotes (MMA) algorithm and the modified globally convergent version of the method of moving asymptotes (MGCMMA) algorithm in the optimization process. This algorithm preserves the advantages of both MMA and MGCMMA. The optimizer is switched from MMA to MGCMMA automatically, depending on the numerical oscillation value existing in the calculation. This algorithm can improve calculation efficiency and accelerate convergence compared with simplex MMA or MGCMMA algorithms, which is proven with an example.
夏毅敏; 唐露; 暨智勇; 程永亮; 卞章括
In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated, based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress−strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.
Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.
Li Qinghua; Yang Shida; Ruan Youlin
A new genetic algorithm is presented based on the musical performance. The novelty of this algorithm is that a new genetic algorithm, mimicking the musical process of searching for a perfect state of harmony, which increases the robustness of it greatly and gives a new meaning of it in the meantime, has been developed. Combining the advantages of the new genetic algorithm, simplex algorithm and tabu search, a hybrid algorithm is proposed. In order to verify the effectiveness of the hybrid algorithm, it is applied to solving some typical numerical function optimization problems which are poorly solved by traditional genetic algorithms. The experimental results show that the hybrid algorithm is fast and reliable.
Full Text Available Gradient ion chromatography was used for the separation of eight sugars: arabitol, cellobiose, fructose, fucose, lactulose, melibiose, N-acetyl-D-glucosamine, and raffinose. The separation method was optimized using a combination of simplex or genetic algorithm with the isocratic-to-gradient retention modeling. Both the simplex and genetic algorithms provided well separated chromatograms in a similar analysis time. However, the simplex methodology showed severe drawbacks when dealing with local minima. Thus the genetic algorithm methodology proved as a method of choice for gradient optimization in this case. All the calculated/predicted chromatograms were compared with the real sample data, showing more than a satisfactory agreement.
Esquivel-Estrada, Jaime, E-mail: email@example.com [Facultad de Ingenieria, Universidad Autonoma del Estado de Mexico, Cerro de Coatepec S/N, Toluca de Lerdo, Estado de Mexico 50000 (Mexico); Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Ortiz-Servin, Juan Jose, E-mail: firstname.lastname@example.org [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Castillo, Jose Alejandro; Perusquia, Raul [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico)
This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.
Crossley, William A.; Martin. Eric T.
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
Ngo, Hung Q; Ré, Christopher; Rudra, Atri
Efficient join processing is one of the most fundamental and well-studied tasks in database research. In this work, we examine algorithms for natural join queries over many relations and describe a novel algorithm to process these queries optimally in terms of worst-case data complexity. Our result builds on recent work by Atserias, Grohe, and Marx, who gave bounds on the size of a full conjunctive query in terms of the sizes of the individual relations in the body of the query. These bounds, however, are not constructive: they rely on Shearer's entropy inequality which is information-theoretic. Thus, the previous results leave open the question of whether there exist algorithms whose running time achieve these optimal bounds. An answer to this question may be interesting to database practice, as it is known that any algorithm based on the traditional select-project-join style plans typically employed in an RDBMS are asymptotically slower than the optimal for some queries. We construct an algorithm whose runn...
Huang, Biqing; Li, Chenghai; Tao, Fei
This article investigates the problem of cloud service composition optimal-selection (CSCOS) in cloud manufacturing (CMfg). The categories of cloud services and their QoS (quality of service) indexes are established. From the perspective of QoS indexes, the relationship among QoS key factors for different kinds of cloud services are analysed and elaborated, and the corresponding objective functions and constraints of CSCOS are proposed. A new chaos control optimal algorithm (CCOA) is designed to address the CSCOS problem, and the simulation results demonstrate that the proposed algorithm can search better solutions with less time-consumption than widely used algorithms such as genetic algorithm (GA) and typical chaotic genetic algorithm (CGA).
This paper proposes an aggregation pheromone system (APS) for solving real-parameter optimization problems using the collective behavior of individuals which communicate using aggregation pheromones. APS was tested on several test functions used in evolutionary computation. The results showed APS could solve real-parameter optimization problems fairly well. The sensitivity analysis of control parameters of APS is also studied.
Mohamed, Ahmed F.; Mahdi M. Elarini; Othman, Ahmed M.
One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) whic...
Zhou, AiGuo; Cheng, ShuYi; Pan, Qiang Biao; Sun, Dong Yu
Current infrared touch overlay has problems on the touch point recognition which bring some burrs on the touch trajectory. This paper uses the target tracking algorithm to improve the recognition and smoothness of infrared touch overlay. In order to deal with the nonlinear state estimate problem for touch point tracking, we use the extended Kalman filter in the target tracking algorithm. And we also use the data fusion algorithm to match the estimate value with the original target trajectory. The experimental results of the infrared touch overlay demonstrate that the proposed target tracking approach can improve the touch point recognition of the infrared touch overlay and achieve much smoother tracking trajectory than the existing tracking approach.
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
Full Text Available In this paper we study Design of the High pressure electronic ballast algorithm for sodium vapor lamp in MATLAB that regulate power by variable frequency despite of voltage variations and characteristic lamp gnawing in total time of lamp longevity. Electronic ballast structure and algorithm of design it, is explained and its design with mathematic model is described. Design process using offer plan in such that in addition of dominant to voice resonance variable frequency and voltage variations keep lamp power to nominal power. Design process performed by consideration characteristic of 250 W sodium vapor lamp. Theory studies ratification with MATLAB.
Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
Parasuraman, Ramviyas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel
The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide red...
Jihong SHEN; Jialian LI; Bin WEI
Based on Fermat's principle and the automatic optimization mechanism in the propagation process of light,an optimal searching algorithm named light ray optimization is presented,where the laws of refraction and reflection of light rays are integrated into searching process of optimization.In this algorithm,coordinate space is assumed to be the space that is full of media with different refractivities,then the space is divided by grids,and finally the searching path is assumed to be the propagation path of light rays.With the law of refraction,the search direction is deflected to the direction that makes the value of objective function decrease.With the law of reflection,the search direction is changed,which makes the search continue when it cannot keep going with refraction.Only the function values of objective problems are used and there is no artificial rule in light ray optimization,so it is simple and easy to realize.Theoretical analysis and the results of numerical experiments show that the algorithm is feasible and effective.
In some cases, there are some special requirements for the vehicle routing problem. Personnel or goods geographically scattered, should be delivered simultaneously to an assigned place by a fleet of vehicles as soon as possible. In this case the objective is to minimize the distance of the longest route among all sub-routes. An improved genetic algorithm was adopted to solve these problems. Each customer has a unique integer identifier and the chromosome is defined as a string of integers. Initial routes are constructed randomly, and then standard proportional selection incorporating elitist is chosen to guarantee the best member survives. New crossover and 2-exchange mutation is adopted to increase the diversity of group. The algorithm was implemented and tested on some instances. The results demonstrate the effectiveness of the method.
Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen
This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.
Ni, Qingjian; Deng, Jianming
Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.
Aftab, Khurrum; Hartley, Richard; Trumpf, Jochen
In many computer vision applications, a desired model of some type is computed by minimizing a cost function based on several measurements. Typically, one may compute the model that minimizes the L2 cost, that is the sum of squares of measurement errors with respect to the model. However, the Lq solution which minimizes the sum of the qth power of errors usually gives more robust results in the presence of outliers for some values of q, for example, q = 1. The Weiszfeld algorithm is a classic algorithm for finding the geometric L1 mean of a set of points in Euclidean space. It is provably optimal and requires neither differentiation, nor line search. The Weiszfeld algorithm has also been generalized to find the L1 mean of a set of points on a Riemannian manifold of non-negative curvature. This paper shows that the Weiszfeld approach may be extended to a wide variety of problems to find an Lq mean for 1 ≤ q algorithm provably finds the global Lq optimum) and multiple rotation averaging (for which no such proof exists). Experimental results of Lq optimization for rotations show the improved reliability and robustness compared to L2 optimization.
WU Jing-min; ZUO Hong-fu; CHEN Yong
A particle swarm optimization (PSO) algorithm improved by immunity algorithm (IA) was presented.Memory and self-regulation mechanisms of IA were used to avoid PSO plunging into local optima. Vaccination and immune selection mechanisms were used to prevent the undulate phenomenon during the evolutionary process. The algorithm was introduced through an application in the direct maintenance cost (DMC) estimation of aircraft components. Experiments results show that the algorithm can compute simply and run quickly. It resolves the combinatorial optimization problem of component DMC estimation with simple and available parameters. And it has higher accuracy than individual methods, such as PLS, BP and v-SVM, and also has better performance than other combined methods, such as basic PSO and BP neural network.
Hou, Yongchao; Zhao, Yang
A novel 3-PUU parallel robot was put forward, on which kinematic analysis was conducted to obtain its inverse kinematics solution, and on this basis, the limitations of the sliding pair and the Hooke joint on the workspace were analyzed. Moreover, the workspace was solved through the three dimensional limit search method, and then optimization analysis was performed on the workspace of this parallel robot, which laid the foundations for the configuration design and further analysis of the parallel mechanism, with the result indicated that this type of robot was equipped with promising application prospect. In addition that, the workspace after optimization can meet more requirements of patients.
Aupy, Guillaume; Herrmann, Julien; Hovland, Paul; Robert, Yves
We reexamine the work of Stumm and Walther on multistage algorithms for adjoint computation. We provide an optimal algorithm for this problem when there are two levels of checkpoints, in memory and on disk. Previously, optimal algorithms for adjoint computations were known only for a single level of checkpoints with no writing and reading costs; a well-known example is the binomial checkpointing algorithm of Griewank and Walther. Stumm and Walther extended that binomial checkpointing algorithm to the case of two levels of checkpoints, but they did not provide any optimality results. We bridge the gap by designing the first optimal algorithm in this context. We experimentally compare our optimal algorithm with that of Stumm and Walther to assess the difference in performance.
Full Text Available Problem statement: A new multi-objective approach, Strength Pareto Evolutionary Algorithm (SPEA, is presented in this paper to solve the shortest path routing problem. The routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: SPEA handles the shortest path routing problem as a true multi-objective optimization problem with competing and noncommensurable objectives. Results: SPEA combines several features of previous multi-objective evolutionary algorithms in a unique manner. SPEA stores nondominated solutions externally in another continuously-updated population and uses a hierarchical clustering algorithm to provide the decision maker with a manageable pareto-optimal set. SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. Conclusion: The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal nondominated solutions.
Hai-tao Bo; Xiao-feng Jia; Xiao-rui Wang
As in the building of deep buried long tunnels, there are complicated conditions such as great deformation, high stress, multi-variables, high non-linearity and so on, the algorithm for structure optimization and its application in tunnel engineering are still in the starting stage. Along with the rapid development of highways across the country, It has become a very urgent task to be tackled to carry out the optimization design of the structure of the section of the tunnel to lessen excavation workload and to reinforce the support. Artificial intelligence demonstrates an extremely strong capability of identifying, expressing and disposing such kind of multiple variables and complicated non- linear relations. In this paper, a comprehensive consideration of the strategy of the selection and updating of the concentration and adaptability of the immune algorithm is made to replace the selection mode in the original genetic algorithm which depends simply on the adaptability value. Such an algorithm has the advantages of both the immune algorithm and the genetic algorithm, thus serving the purpose of not only enhancing the individual adaptability but maintaining the individual diversity as well. By use of the identifying function of the antigen memory, the global search capability of the immune genetic algorithm is raised, thereby avoiding the occurrence of the premature phenomenon. By optimizing the structure of the section of the Huayuan tunnel, the current excavation area and support design are adjusted. A conclusion with applicable value is arrived at. At a higher computational speed and a higher efficiency, the current method is verified to have advantages in the optimization computation of the tunnel project. This also suggests that the application of the immune genetic algorithm has a practical significance to the stability assessment and informationlzation design of the wall rock of the tunnel.
Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...
Zacarias, Albert S; Mills, Michael D
IMRT plans generated in Eclipse use a fast algorithm to evaluate dose for optimization and a more accurate algorithm for a final dose calculation, the Analytical Anisotropic Algorithm. The use of a fast optimization algorithm introduces optimization convergence errors into an IMRT plan. Eclipse has a feature where optimization may be performed on top of an existing base plan. This feature allows for the possibility of arriving at a recursive solution to optimization that relies on the accuracy of the final dose calculation algorithm and not the optimizer algorithm. When an IMRT plan is used as a base plan for a second optimization, the second optimization can compensate for heterogeneity and modulator errors in the original base plan. Plans with the same field arrangement as the initial base plan may be added together by adding the initial plan optimal fluence to the dose correcting plan optimal fluence.A simple procedure to correct for optimization errors is presented that may be implemented in the Eclipse treatment planning system, along with an Excel spreadsheet to add optimized fluence maps together.
Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majority of metaheuristic algorithms, while efficiency analysis is equally challenging. In this paper, we i...
We propose a general sequential Monte Carlo approach for optimization of pseudo-Boolean objective functions. There are three aspects we particularly address in this work. First, we give a unified approach to stochastic optimization based on sequential Monte Carlo techniques, including the cross-entropy method and simulated annealing as special cases. Secondly, we point out the need for auxiliary sampling distributions, that is parametric families on binary spaces, which are able to reproduce complex dependency structures. We discuss some known and novel binary parametric families and illustrate their usefulness in our numerical experiments. Finally, we provide numerical evidence that particle-driven optimization algorithms yield superior results on strongly multimodal optimization problems while local search heuristics outperform them on easier problems.
Full Text Available Most existing research on the job shop scheduling problem has been focused on the minimization of makespan (i.e., the completion time of the last job. However, in the fiercely competitive market nowadays, delivery punctuality is more important for maintaining a high service reputation. So in this paper, we aim at solving job shop scheduling problems with the total weighted tardiness objective. Several dispatching rules are adopted in the Giffler-Thompson algorithm for constructing active schedules. It is noticeable that the rule selections for scheduling consecutive operations are not mutually independent but actually interrelated. Under such circumstances, a probabilistic model-building genetic algorithm (PMBGA is proposed to optimize the sequence of selected rules. First, we use Bayesian networks to model the distribution characteristics of high-quality solutions in the population. Then, the new generation of individuals is produced by sampling the established Bayesian network. Finally, some elitist individuals are further improved by a special local search module based on parameter perturbation. The superiority of the proposed approach is verified by extensive computational experiments and comparisons.
李鹤; 李琦; 高军萍; 雷明然
具有良好非周期自相关特性二元序列在通信同步、雷达等领域具有广泛的应用。通过对遗传算法、粒子群算法与量子粒子群算法三种进化算法进行对比分析，设计了具有良好非周期自相关特性的二元序列的搜索算法。研究结果表明，粒子群算法的搜索能力优于遗传算法，而量子粒子群算法具有参数少，易于控制的优点，取得了较好的优化结果。%Binary sequences with good aperiodic autocorrelation features are widely used in the field of radar, communication synchronization. Genetic algorithm, particle swarm optimization and quantum particle swarm optimization algorithm are compared and analyzed in this paper. The new search algorithm of binary sequences with good aperiodic autocorrelation properties are designed based on three evolutionary algorithms. Research results show that the search ability of particle swarm algorithm is better than genetic algorithm. Quantum particle swarm optimization algorithm has less parameters, easy to control, and the better good optimization results were obtained.
Seung Hwan Lee; Hwa Cho Yi; Kyuyoung Han; Jin Ho Kim
In this research, the design of a new electromagnetic engine valve in the limited space of combustion engine is optimized by multidisciplinary simulation using MATLAB and Maxwell. An electromagnetic engine valve actuator using a permanent magnet is a new actuator concept for overcoming the inherent drawbacks of the conventional solenoid-driven electromagnetic engine valve actuator, such as high power consumption and so on. This study aims to maximize the vibration frequency of the armature to...
Full Text Available The application of metaheuristic algorithms to optimization problems has been very important during the last decades. The main advantage of these techniques is their flexibility and robustness, which allows them to be applied to a wide range of problems. In this work we concentrate on metaheuristics based on Simulated Annealing, Tabu Search and Variable Neighborhood Search trajectory whose main characteristic is that they start from a point and through the exploration of the neighborhood vary the current solution, forming a trajectory. By means of the instances of the selected combinatorial problems, a computational experimentation is carried out that illustrates the behavior of the algorithmic methods to solve them. The main objective of this work is to perform the study and comparison of the results obtained for the selected trajectories metaheuristics in its application for the resolution of a set of academic problems of combinatorial optimization.
Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem(an individual for genetic algorithm). An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.
胡志坤; 彭小奇; 桂卫华
An adaptive chaotic gradient descending optimization algorithm for single objective optimization was presented. A local minimum judged by two rules was obtained by an improved mutative-step gradient descending method. A new optimal minimum was obtained to replace the local minimum by mutative-scale chaotic search algorithm whose scales are magnified gradually from a small scale in order to escape local minima. The global optimal value was attained by repeatedly iterating. At last, a BP (back-propagation) neural network model for forecasting slag output in matte converting was established. The algorithm was used to train the weights of the BP neural network model. The simulation results with a training data set of 400 samples show that the training process can be finished within 300 steps to obtain the global optimal value, and escape local minima effectively. An optimization system for operation parameters, which includes the forecasting model, is achieved, in which the output of converter increases by 6.0%, and the amount of the treated cool materials rises by 7.8% in the matte converting process.
A globally optimal solution to vector quantization (VQ) index assignment on noisy channel, the evolutionary algorithm based index assignment algorithm (EAIAA), is presented. The algorithm yields a significant reduction in average distortion due to channel errors, over conventional arbitrary index assignment, as confirmed by experimental results over the memoryless binary symmetric channel (BSC) for any bit error.
Full Text Available Affected by various environmental factors, wind speed presents high fluctuation, nonlinear and non-stationary characteristics. To evaluate wind energy properly and efficiently, this paper proposes a modified fast ensemble empirical model decomposition (FEEMD-bat algorithm (BA-least support vector machines (LSSVM (FEEMD-BA-LSSVM model combined with input selected by deep quantitative analysis. The original wind speed series are first decomposed into a limited number of intrinsic mode functions (IMFs with one residual series. Then a LSSVM is built to forecast these sub-series. In order to select input from environment variables, Cointegration and Granger causality tests are proposed to check the influence of temperature with different leading lengths. Partial correlation is applied to analyze the inner relationships between the historical speeds thus to select the LSSVM input. The parameters in LSSVM are fine-tuned by BA to ensure the generalization of LSSVM. The forecasting results suggest the hybrid approach outperforms the compared models.
Alavi Fazel, S. Ali
A new optimized model which can predict the heat transfer in the nucleate boiling at isolated bubble regime is proposed for pool boiling on a horizontal rod heater. This model is developed based on the results of direct observations of the physical boiling phenomena. Boiling heat flux, wall temperature, bubble departing diameter, bubble generation frequency and bubble nucleation site density have been experimentally measured. Water and ethanol have been used as two different boiling fluids. Heating surface was made by several metals and various degrees of roughness. The mentioned model considers various mechanisms such as latent heat transfer due to micro-layer evaporation, transient conduction due to thermal boundary layer reformation, natural convection, heat transfer due to the sliding bubbles and bubble super-heating. The fractional contributions of individual mentioned heat transfer mechanisms have been calculated by genetic algorithm. The results show that at wall temperature difference more that about 3 K, bubble sliding transient conduction, non-sliding transient conduction, micro-layer evaporation, natural convection, radial forced convection and bubble super-heating have higher to lower fractional contributions respectively. The performance of the new optimized model has been verified by comparison of the existing experimental data.
Ezra, Elishai; Maor, Idan; Bavli, Danny; Shalom, Itai; Levy, Gahl; Prill, Sebastian; Jaeger, Magnus S; Nahmias, Yaakov
Microfluidic applications range from combinatorial synthesis to high throughput screening, with platforms integrating analog perfusion components, digitally controlled micro-valves and a range of sensors that demand a variety of communication protocols. Currently, discrete control units are used to regulate and monitor each component, resulting in scattered control interfaces that limit data integration and synchronization. Here, we present a microprocessor-based control unit, utilizing the MS Gadgeteer open framework that integrates all aspects of microfluidics through a high-current electronic circuit that supports and synchronizes digital and analog signals for perfusion components, pressure elements, and arbitrary sensor communication protocols using a plug-and-play interface. The control unit supports an integrated touch screen and TCP/IP interface that provides local and remote control of flow and data acquisition. To establish the ability of our control unit to integrate and synchronize complex microfluidic circuits we developed an equi-pressure combinatorial mixer. We demonstrate the generation of complex perfusion sequences, allowing the automated sampling, washing, and calibrating of an electrochemical lactate sensor continuously monitoring hepatocyte viability following exposure to the pesticide rotenone. Importantly, integration of an optical sensor allowed us to implement automated optimization protocols that require different computational challenges including: prioritized data structures in a genetic algorithm, distributed computational efforts in multiple-hill climbing searches and real-time realization of probabilistic models in simulated annealing. Our system offers a comprehensive solution for establishing optimization protocols and perfusion sequences in complex microfluidic circuits.
He, Qing [Arizona State Univ., Tempe, AZ (United States)
In this paper a new parallel iterative algorithm for unconstrained optimization using the idea of multisplitting is proposed. This algorithm uses the existing sequential algorithms without any parallelization. Some convergence and numerical results for this algorithm are presented. The experiments are performed on an Intel iPSC/860 Hyper Cube with 64 nodes. It is interesting that the sequential implementation on one node shows that if the problem is split properly, the algorithm converges much faster than one without splitting.
The DNA evolutionary algorithm(DNA-EA)and the DNA genetic algorithm(DNA-GA)based on a new DNA encoding method are propsed based on the structure and the genetic mechanism of biological DNA. The DNA-EA and the DNA-GA are applied into the optimal design of TS fuzzy control system. The simulation results show the effectiveness of the two DNA algorithms, excellent self-learning capability. However, the DNA-EA is superior to the DNA-GA in the simulation performance.
Seung Hwan Lee
Full Text Available In this research, the design of a new electromagnetic engine valve in the limited space of combustion engine is optimized by multidisciplinary simulation using MATLAB and Maxwell. An electromagnetic engine valve actuator using a permanent magnet is a new actuator concept for overcoming the inherent drawbacks of the conventional solenoid-driven electromagnetic engine valve actuator, such as high power consumption and so on. This study aims to maximize the vibration frequency of the armature to reduce the transition time of the engine valve. The higher performance of the new actuator is demonstrated by dynamic finite element analysis.
Zhang, Weishan; Ingstrup, Mads; Hansen, Klaus Marius
-protection in the Hydra middleware, and is integrated with a framework for enforcing the computed solution at run time using security obligations. The experiments with the prototype on configuring security strategies for a pervasive service middleware show that this approach has acceptable performance, and could be used......With increasingly complex and heterogeneous systems in pervasive service computing, it becomes more and more important to provide self-protected services to end users. In order to achieve self-protection, the corresponding security should be provided in an optimized manner considering...
Optimization of the operational route in the automated storage/retrieval system (AS/RS) is transformed into the traveling salesman problem. To make the moving distance of the storage/retrieval machine shortest, we carry out a group of tests where 20 goods locations are chosed. Using PSO for operational route of AS/RS, the operation time can be shortened by about 11%. The experiments indicate that under the same conditions, the more the goods locations are, the higher the operation efficiency of the storage/retrieval machine is.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.
Luo, Yong; Zhu, Bo; Tang, Yong
We investigate the problem of dynamic optimal capital growth of a portfolio. A general framework that one strives to maximize the expected logarithm utility of long term growth rate was developed. Exact optimization algorithms run into difficulties in this framework and this motivates the investigation of applying simulated annealing optimized algorithm to optimize the capital growth of a given portfolio. Empirical results with real financial data indicate that the approach is inspiring for capital growth portfolio.
Fister, Iztok; Fister, Iztok; Brest, Janez
Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.
曹腾飞; 符云清; 钟明洋
为了提高Web服务组合流程中服务选择技术的收敛性能,提出了一种基于遗传算法与蚁群算法相融合的多目标优化策略,用于解决基于QoS的Web服务组合问题.本文首先将Web服务组合的全局最优化问题转化为寻求一条QoS最优解的路径问题,并通过改进遗传算法得到蚁群算法中初始路径的信息素分布,再通过改进蚁群算法来求得最优解.仿真实验结果表明,该改进算法能在较少的进化代数下得到最优路径,提高了Web服务组合的快速全局搜索能力.%To improve the convergence ability of service selection technology in process of Web service composition, the paper presents a multi-objective optimization strategy based on genetic algorithm and ant colony algorithm to solve global optimization problem in QoS-based Web service composition. In the paper, global optimization problem in Web service composition is presented as a QoS optimal routing problem. And then, an improved genetic algorithm is proposed to get pheromone distribution in initial route of ant colony algorithm. At last, an improved ant colony algorithm is presented to get the optimal solution. Simulation result suggests that the improved algorithms can get the optimal routing in less evolutional generation than typical algorithms, and improve global research ability in Web Service compositioa
Full Text Available Historically, the assessment of credit risk has proved to be both highly important and extremely difficult. Currently, financial institutions rely on the use of computer-generated credit scores for risk assessment. However, automated risk evaluations are currently imperfect, and the loss of vast amounts of capital could be prevented by improving the performance of computerized credit assessments. A number of approaches have been developed for the computation of credit scores over the last several decades, but these methods have been considered too complex without good interpretability and have therefore not been widely adopted. Therefore, in this study, we provide the first comprehensive comparison of results regarding the assessment of credit risk obtained using 10 runs of 10-fold cross validation of the Re-RX algorithm family, including the Re-RX algorithm, the Re-RX algorithm with both discrete and continuous attributes (Continuous Re-RX, the Re-RX algorithm with J48graft, the Re-RX algorithm with a trained neural network (Sampling Re-RX, NeuroLinear, NeuroLinear+GRG, and three unique rule extraction techniques involving support vector machines and Minerva from four real-life, two-class mixed credit-risk datasets. We also discuss the roles of various newly-extended types of the Re-RX algorithm and high performance classifiers from a Pareto optimal perspective. Our findings suggest that Continuous Re-RX, Re-RX with J48graft, and Sampling Re-RX comprise a powerful management tool that allows the creation of advanced, accurate, concise and interpretable decision support systems for credit risk evaluation. In addition, from a Pareto optimal perspective, the Re-RX algorithm family has superior features in relation to the comprehensibility of extracted rules and the potential for credit scoring with Big Data.
Menghan Wang∗,Zongmin Yue; Lie Meng
In order to prevent cracking appeared in the work⁃piece during the hot stamping operation, this paper proposes a hybrid optimization method based on Hammersley sequence sampling ( HSS) , finite analysis, back⁃propagation ( BP ) neural network and genetic algorithm ( GA ) . The mechanical properties of high strength boron steel are characterized on the basis of uniaxial tensile test at elevated temperatures. The samples of process parameters are chosen via the HSS that encourages the exploration throughout the design space and hence achieves better discovery of possible global optimum in the solution space. Meanwhile, numerical simulation is carried out to predict the forming quality for the optimized design. A BP neural network model is developed to obtain the mathematical relationship between optimization goal and design variables, and genetic algorithm is used to optimize the process parameters. Finally, the results of numerical simulation are compared with those of production experiment to demonstrate that the optimization strategy proposed in the paper is feasible.
Paschalis, P. [Nuclear and Particle Physics Section, Physics Department, National and Kapodistrian University of Athens, Zografos 15783, Athens (Greece); Mavromichalaki, H., E-mail: email@example.com [Nuclear and Particle Physics Section, Physics Department, National and Kapodistrian University of Athens, Zografos 15783, Athens (Greece)
Nowadays, several neutron monitor stations worldwide, broadcast their cosmic ray data in real time, in order for the scientific community to be able to use these measurements immediately. In parallel, the development of the Neutron Monitor Database (NMDB; (http://www.nmdb.eu)) which collects all the high resolution real time measurements, allows the implementation of various applications and services by using these data instantly. Therefore, it is obvious that the need for high quality real time data is imperative. The quality of the data is handled by different correction algorithms that filter the real time measurements for undesired instrumental variations. In this work, an optimization of the Median Editor that is currently mainly applied to the neutron monitor data and the recently proposed ANN algorithm based on neural networks is presented. This optimization leads to the implementation of the Median Editor Plus and the ANN Plus algorithms. A direct comparison of these algorithms with the newly appeared Edge Editor is performed and the results are presented.
Holmes, Tim; Zanker, Johannes M
Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioral measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA), which has been demonstrated as a tool to identify aesthetic preferences (Holmes and Zanker, 2012). In the present study, the GDEA was used to investigate the preferred combination of color and shape which have been promoted in the Bauhaus arts school. We used the same three shapes (square, circle, triangle) used by Kandinsky (1923), with the three color palette from the original experiment (A), an extended seven color palette (B), and eight different shape orientation (C). Participants were instructed to look for their preferred circle, triangle or square in displays with eight stimuli of different shapes, colors and rotations, in an attempt to test for a strong preference for red squares, yellow triangles and blue circles in such an unbiased experimental design and with an extended set of possible combinations. We Tested six participants extensively on the different conditions and found consistent preferences for color-shape combinations for individuals, but little evidence at the group level for clear color/shape preference consistent with Kandinsky's claims, apart from some weak link between yellow and triangles. Our findings suggest substantial inter-individual differences in the presence of stable individual associations of color and shapes, but also that these associations are robust within a single individual. These individual differences go some way toward challenging the claims of the universal preference for color/shape combinations proposed by Kandinsky, but also indicate that a much larger sample size would be needed to confidently reject that hypothesis. Moreover, these experiments highlight the
Full Text Available Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioural measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA, which has been used as a tool to identify aesthetic preferences (Holmes & Zanker, 2012. In the present study, the GDEA was used to investigate the preferred combination of colour and shape which have been promoted in the Bauhaus arts school. We used the same 3 shapes (square, circle, triangle used by Kandinsky (1923, with the 3 colour palette from the original experiment (A, an extended 7 colour palette (B, and 8 different shape orientation (C. Participants were instructed to look for their preferred circle, triangle or square in displays with 8 stimuli of different shapes, colours and rotations, in an attempt to test for a strong preference for red squares, yellow triangles and blue circles in such an unbiased experimental design and with an extended set of possible combinations. We Tested 6 participants extensively on the different conditions and found consistent preferences for individuals, but little evidence at the group level for preference consistent with Kandinsky’s claims, apart from some weak link between yellow and triangles. Our findings suggest substantial inter-individual differences in the presence of stable individual associations of colour and shapes, but also that these associations are robust within a single individual. These individual differences go some way towards challenging the claims of the universal preference for colour/shape combinations proposed by Kandinsky, but also indicate that a much larger sample size would be needed to confidently reject that hypothesis. Moreover, these experiments highlight the vast potential of the GDEA in experimental aesthetics
赵成栋; 周宇; 康建初; 谭俊
SNMP-based data collecting is fundamental for IP network management. This paper presents a SNMP-based Data Collect Algorithm for common use. According to requirement of data collecting, the algorithm disassem-bles collect tasks into metadata, and then reconstructs the protocol packet. Also takes full advantage of GetBulk op-eration, and brings out a combination algorithm for discrete variables of a MIB table so that the operation is opti-mized. A collect engine is implemented using the algorithm, which supports dynamic collection of multiple MIB vari-ables, supports arithmetic computing and logical computing, supports threshold judgment and sends out abnormalevents notifications. Tests are given to prove that the algorithm reduces the traffic for network management and im-proves the efficiency.
Soltani-Mohammadi, Saeed; Safa, Mohammad
Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.
ARTEAGA SIERRA, FRANCISCO RODRIGO; Milián Enrique, Carles; I. Torres-Gómez; M. Torres-Cisneros; Moltó, Germán; Ferrando Cogollos, Albert
© 2014 Optical Society of America. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modifications of the content of this paper are prohibited We present a numerical strategy to design fiber based dual pulse light sources exhibiting two predefined spectral peaks in the anomalous group velocity dispersion regime. The frequency conversion is based on t...
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
SDD-1 algorithm is a distributed query optimization algorithm,the genetic algorithm has been successfully ap-plied in many fields .In the SDD-1 algorithm based on genetic algorithm,genetic algorithm has a problem of“premature con-vergence”.To solve this problem,SDD-1 distributed query optimization algorithm based on niche genetic algorithm is pro-posed in this research.This algorithm can solve the query plan of minimum communication cost in possible time.Experimental results show that this algorithm has a better performance than SDD-1 algorithm and SDD-1 algorithm based on genetic algo-rithm.%SDD-1算法是一种分布式数据库的查询优化算法，遗传算法已经在许多领域得到了成功的应用。针对基于遗传算法的 SDD-1算法中，遗传算法存在“早熟收敛”的问题，提出一种基于小生境遗传算法的 SDD-1分布式查询优化算法，该算法能在尽可能短的时间内求解通信费用最小的查询计划。实验结果表明，该算法比单独使用 SDD-1算法、基于遗传算法的 SDD-1算法均有更优的性能。
Full Text Available In this work, we have proposed modified PSO algorithm based optimizer for automatic circuit design. The performance of the modified PSO algorithm is compared with two other evolutionary algorithms namely ABC algorithm and standard PSO algorithm by designing two stage CMOS operational amplifier and bulk driven OTA in 130nm technology. The results show the robustness of the proposed algorithm. With modified PSO algorithm, the average design error for two stage op-amp is only 0.054% in contrast to 3.04% for standard PSO algorithm and 5.45% for ABC algorithm. For bulk driven OTA, average design error is 1.32% with MPSO compared to 4.70% with ABC algorithm and 5.63% with standard PSO algorithm.
Metcalfe, Travis S.
I have developed a procedure utilizing a genetic-algorithm (GA) based optimization scheme to fit the observed light curves of an eclipsing binary star with a model produced by the Wilson-Devinney (W-D) code. The principal advantages of this approach are the global search capability and the objectivity of the final result. Although this method can be more efficient than some other comparably global search techniques, the computational requirements of the code are still considerable. I have applied this fitting procedure to my observations of the W UMa type eclipsing binary BH Cassiopeiae. An analysis of V-band CCD data obtained in 1994-1995 from Steward Observatory and U- and B-band photoelectric data obtained in 1996 from McDonald Observatory provided three complete light curves to constrain the fit. In addition, radial velocity curves obtained in 1997 from McDonald Observatory provided a direct measurement of the system mass ratio to restrict the search. The results of the GA-based fit are in excellent agreement with the final orbital solution obtained with the standard differential corrections procedure in the W-D code.
Metcalfe, T S
I have developed a procedure utilizing a Genetic-Algorithm-based optimization scheme to fit the observed light curves of an eclipsing binary star with a model produced by the Wilson-Devinney code. The principal advantages of this approach are the global search capability and the objectivity of the final result. Although this method can be more efficient than some other comparably global search techniques, the computational requirements of the code are still considerable. I have applied this fitting procedure to my observations of the W UMa type eclipsing binary BH Cassiopeiae. An analysis of V-band CCD data obtained in 1994/95 from Steward Observatory and U- and B-band photoelectric data obtained in 1996 from McDonald Observatory provided three complete light curves to constrain the fit. In addition, radial velocity curves obtained in 1997 from McDonald Observatory provided a direct measurement of the system mass ratio to restrict the search. The results of the GA-based fit are in excellent agreement with the...
Sergio Gerardo de-los-Cobos-Silva
Full Text Available This paper presents an original and efficient PSO algorithm, which is divided into three phases: (1 stabilization, (2 breadth-first search, and (3 depth-first search. The proposed algorithm, called PSO-3P, was tested with 47 benchmark continuous unconstrained optimization problems, on a total of 82 instances. The numerical results show that the proposed algorithm is able to reach the global optimum. This work mainly focuses on unconstrained optimization problems from 2 to 1,000 variables.
Among the available clustering algorithms in data mining, the CLOPE algorithm attracts much more attention with its high speed and good performance. However, the proper choice of some parameters in the CLOPE algorithm directly affects the validity of the clustering results, which is still an open issue. For this purpose, this paper proposes a fuzzy CLOPE algorithm, and presents a method for the optimal parameter choice by defining a modified partition fuzzy degree as a clustering validity function. The experimental results with real data set illustrate the effectiveness of the proposed fuzzy CLOPE algorithm and optimal parameter choice method based on the modified partition fuzzy degree.
Yuan, Chunhua; Wang, Jiang; Yi, Guosheng
Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.
Fu-Kwun Wang; Yu-Yao Hsiao; Ku-Kuang Chang
It is important for executives to predict the future trends. Otherwise, their companies cannot make profitable decisions and investments. The Bass diffusion model can describe the empirical adoption curve for new products and technological innovations. The Grey model provides short-term forecasts using four data points. This study develops a combined model based on the rolling Grey model (RGM) and the Bass diffusion model to forecast motherboard shipments. In addition, we investigate evolutio...
Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel
The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the "server-relay-client" framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.
Full Text Available The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS. When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities, there is a possibility that some electronic components may fail randomly (due to radiation effects, which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.
Wang, Lui; Bayer, Steven
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
蔡晴红; 于舒娟; 张昀
Quantum genetic algorithm,as the combination of the quantum computing theory and the principle of genetic algorithm,is a new global optimization algorithm,which has stronger capability in the optimization,faster convergence and shorter time in computing. It has already been widely used in many areas. It proposes a blind equalization model which sends BPSK signal based on SIMO system. Use projection operator of complement to construct the cost function,based on which quantum genetic algorithm is applied to optimize to ob-tain the best sequence of estimation. The algorithm simulation results show that the proposed blind detection algorithm based on quantum genetic optimization has rapid convergence speed,is able to successfully achieve blind detection,has a certain value.% 量子遗传算法(Quantum Genetic Algorithm)作为量子计算理论和遗传算法原理相结合的一种新兴的全局优化算法，因算法具有寻优能力强、收敛速度快和计算时间短的特点，在许多领域都得到了广泛应用。文中首先给出基于SIMO系统的BPSK发送信号盲检测模型，利用补投影算子构造盲检测优化代价函数，在此基础上利用量子遗传算法进行优化，获得最佳估计序列。算法性能仿真表明，文中提出的基于量子遗传优化的盲检测算法收敛速度快、能够成功实现盲检测，具有一定的应用价值。
Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun
Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.
Three heuristic algorithms for optimal polygonal approximation of digital planar curves is presented.With Genetic Algorithm (GA), improved Genetic Algorithm (IGA) based on Pareto optimal solution and Tabu Search (TS), a near optimal polygonal approximation was obtained.Compared to the famous Teh-chin algorithm, our algorithms have obtained the approximated polygons with less number of vertices and less approximation error.Compared to the dynamic programming algorithm, the processing time of our algorithms are much less expensive.
Full Text Available The traditional MUltiple SIgnal Classification (MUSIC algorithm requires significant computational effort and can not be employed for the Direction Of Arrival (DOA estimation of targets in a low-altitude multipath environment. As such, a novel MUSIC approach is proposed on the basis of the algorithm of Adaptive Step Glowworm Swarm Optimization (ASGSO. The virtual spatial smoothing of the matrix formed by each snapshot is used to realize the decorrelation of the multipath signal and the establishment of a fullorder correlation matrix. ASGSO optimizes the function and estimates the elevation of the target. The simulation results suggest that the proposed method can overcome the low altitude multipath effect and estimate the DOA of target readily and precisely without radar effective aperture loss.
Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.
Full Text Available Multiobjective (MO optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE, genetic algorithm (GA, gravitational search algorithm (GSA, and particle swarm optimization (PSO have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two. In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization.
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
Multiobjective (MO) optimization is an emerging field which is increasingly being encountered in many fields globally. Various metaheuristic techniques such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) have been used in conjunction with scalarization techniques such as weighted sum approach and the normal-boundary intersection (NBI) method to solve MO problems. Nevertheless, many challenges still arise especially when dealing with problems with multiple objectives (especially in cases more than two). In addition, problems with extensive computational overhead emerge when dealing with hybrid algorithms. This paper discusses these issues by proposing an alternative framework that utilizes algorithmic concepts related to the problem structure for generating efficient and effective algorithms. This paper proposes a framework to generate new high-performance algorithms with minimal computational overhead for MO optimization. PMID:24470795
Mohammad Dehghani Soltani
Full Text Available In this study we propose an improved method for watermarking based on ML detector that in comparison with similar methods this scheme has more robustness against attacks, with the same embedded length of logo. Embedding the watermark will perform in the low frequency coefficients of wavelet transform of high entropy blocks (blocks which have more information. Then in the watermark extraction step by using PSO algorithm in a way that maximum quality in comparison with previous methods obtain, by optimizing the Lagrange factor in the Neyman-Peyrson test, we extract the watermark. Finally, performance of proposed scheme has been investigated and accuracy of results are shown by simulation.
A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed,which is called the immune genetic algorithm with the elitism (IGAE).In IGAE,the new methods for computing antibody similarity,expected reproduction probability,and clonal selection probability were given.IGAE has three features.The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage,which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively.The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem.The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter β,which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly.Two different complex multi-modal functions were selected to test the validity of IGAE.The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly.The experimental results also confirm that IGAE is of better performance in convergence speed,solution variation behavior,and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.
Full Text Available As the conventional cutting pattern recognition methods for shearer are huge in size, have low recognition reliability and an inconvenient contacting measurement method, a fast and reliable coal-rock cutting pattern recognition system is always a baffling problem worldwide. However, the recognition rate has a direct relation with the outputs of coal mining and the safety quality of staff. In this paper, a novel cutting pattern identification method through the cutting acoustic signal of the shearer is proposed. The signal is clustering by fuzzy C-means (FCM and a hybrid optimization algorithm, combining the fruit fly and genetic optimization algorithm (FGOA. Firstly, an industrial microphone is installed on the shearer and the acoustic signal is collected as the source signal due to its obvious advantages of compact size, non-contact measurement and ease of remote transmission. The original sound is decomposed by multi-resolution wavelet packet transform (WPT, and the normalized energy of each node is extracted as a feature vector. Then, FGOA, by introducing a genetic proportion coefficient into the basic fruit fly optimization algorithm (FOA, is applied to overcome the disadvantages of being time-consuming and sensitivity to initial centroids of the traditional FCM. A simulation example, with the accuracy of 95%, and some comparisons prove the effectiveness and superiority of the proposed scheme. Finally, an industrial test validates the practical effect.
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
. We propose distributed computing to accelerate time optimal reachability analysis. We develop five distributed state exploration algorithms, implement them in \\uppaal enabling it to exploit the compute resources of a dedicated model-checking cluster. We experimentally evaluate the implemented...... algorithms with four models in terms of their ability to compute near- or proven-optimal solutions, their scalability, time and memory consumption and communication overhead. Our results show that distributed algorithms work much faster than sequential algorithms and have good speedup in general....
WU Jigang; JI Yongchang; CHEN Guoliang
The intersection of N halfplanes is a basic problem in computational geometry and computer graphics. The optimal offiine algorithm for this problem runs in time O(N log N). In this paper, an optimal online algorithm which runs also in time O(N log N) for this problem is presented. The main idea of the algorithm is to give a new definition for the left side of a given line, to assign the order for the points of a convex polygon, and then to use binary search method in an ordered vertex set. The data structure used in the algorithm is no more complex than array.
YANG Shu-xia; LI Xiang; LI Ning; YANG Shang-dong
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
Gao, Yifeng; Zhao, Ge
In this paper, a novel mutation operator of differential evolution algorithm is proposed. A new algorithm called divergence differential evolution algorithm (DDEA) is developed by combining the new mutation operator with divergence operator and assimilation operator (divergence operator divides population, and, assimilation operator combines population), which can detect multiple solutions and robustness in noisy environment. The new algorithm is applied to optimize Michalewicz Function and to track changing of rain-induced-attenuation process. The results based on DDEA are compared with those based on Differential Evolution Algorithm (DEA). It shows that DDEA algorithm gets better results than DEA does in the same premise. The new algorithm is significant for optimizing and tracking the characteristics of MIMO (Multiple Input Multiple Output) channel at millimeter waves.