Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
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
Chemical optimization algorithm for fuzzy controller design
Astudillo, Leslie; Castillo, Oscar
2014-01-01
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
OPTIMAL CONTROL ALGORITHMS FOR SECOND ORDER SYSTEMS
Directory of Open Access Journals (Sweden)
Danilo Pelusi
2013-01-01
Full Text Available Proportional Integral Derivative (PID controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as Fuzzy Logic, Genetic Algorithms and Neural Networks. The combination of these theories can give good results in terms of settling time, rise time and overshoot. In this study, suitable controllers able of improving timing performance of second order plants are proposed. The results show that the PID controller has good overshoot values and shows optimal robustness. The genetic-fuzzy controller gives a good value of settling time and a very good overshoot value. The neural-fuzzy controller gives the best timing parameters improving the control performances of the others two approaches. Further improvements are achieved designing a real-time optimization algorithm which works on a genetic-neuro-fuzzy controller.
Optimization Algorithms for Nuclear Reactor Power Control
Energy Technology Data Exchange (ETDEWEB)
Kim, Yeong Min; Oh, Won Jong; Oh, Seung Jin; Chun, Won Gee; Lee, Yoon Joon [Jeju National University, Jeju (Korea, Republic of)
2010-10-15
One of the control techniques that could replace the present conventional PID controllers in nuclear plants is the linear quadratic regulator (LQR) method. The most attractive feature of the LQR method is that it can provide the systematic environments for the control design. However, the LQR approach heavily depends on the selection of cost function and the determination of the suitable weighting matrices of cost function is not an easy task, particularly when the system order is high. The purpose of this paper is to develop an efficient and reliable algorithm that could optimize the weighting matrices of the LQR system
A Feedback Optimal Control Algorithm with Optimal Measurement Time Points
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Felix Jost
2017-02-01
Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.
Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
Ünal, Muhammet; Topuz, Vedat; Erdal, Hasan
2013-01-01
Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.
Efficient algorithms for the laboratory discovery of optimal quantum controls.
Turinici, Gabriel; Le Bris, Claude; Rabitz, Herschel
2004-01-01
The laboratory closed-loop optimal control of quantum phenomena, expressed as minimizing a suitable cost functional, is currently implemented through an optimization algorithm coupled to the experimental apparatus. In practice, the most commonly used search algorithms are variants of genetic algorithms. As an alternative choice, a direct search deterministic algorithm is proposed in this paper. For the simple simulations studied here, it outperforms the existing approaches. An additional algorithm is introduced in order to reveal some properties of the cost functional landscape.
Optimal Pid Controller Design Using Adaptive Vurpso Algorithm
Zirkohi, Majid Moradi
2015-04-01
The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.
Efficient evolutionary algorithms for optimal control
López Cruz, I.L.
2002-01-01
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
A Hamiltonian Algorithm for Singular Optimal LQ Control Systems
Delgado-Tellez, M
2012-01-01
A Hamiltonian algorithm, both theoretical and numerical, to obtain the reduced equations implementing Pontryagine's Maximum Principle for singular linear-quadratic optimal control problems is presented. This algorithm is inspired on the well-known Rabier-Rheinhboldt constraints algorithm used to solve differential-algebraic equations. Its geometrical content is exploited fully by implementing a Hamiltonian extension of it which is closer to Gotay-Nester presymplectic constraint algorithm used to solve singular Hamiltonian systems. Thus, given an optimal control problem whose optimal feedback is given in implicit form, a consistent set of equations is obtained describing the first order differential conditions of Pontryaguine's Maximum Principle. Such equations are shown to be Hamiltonian and the set of first class constraints corresponding to controls that are not determined, are obtained explicitly. The strength of the algorithm is shown by exhibiting a numerical implementation with partial feedback on the c...
Theory, Design, and Algorithms for Optimal Control of wireless Networks
2010-06-09
significantly outperform existing protocols (such as AODV ) in terms of total network cost Furthermore, we have shown that even when components of our...achieved through distributed control algorithms that jointly optimize power control, routing , and congestion factors. A second stochastic model approach...updates the network queue state, node-transmission powers amongst others, allowing for power control, scheduling, and routing algorithms to maximize
A Decomposition Algorithm for Optimal Control of Distributed Energy System
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Standardi, Laura
2013-01-01
In economic model predictive control of distributed energy systems, the constrained optimal control problem can be expressed as a linear program with a block-angular structure. In this paper, we present an efficient Dantzig-Wolfe decomposition algorithm specifically tailored to problems...
Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
LI Jimin; SHANG Chaoxuan; ZOU Minghu
2007-01-01
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.
Genetic Algorithm Optimizes Q-LAW Control Parameters
Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard
2008-01-01
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
Constraint Algorithm for Extremals in Optimal Control Problems
Barbero-Linan, Maria
2007-01-01
A characterization of different kinds of extremals of optimal control problems is given if we take an open control set. A well known constraint algorithm for implicit differential equations is adapted to the study of such problems. Some necessary conditions of Pontryagin's Maximum Principle determine the primary constraint submanifold for the algorithm. Some examples in the control literature, such as subRiemannian geometry and control-affine systems, are revisited to give, in a clear geometric way, a subset where the abnormal, normal and strict abnormal extremals stand.
Online Optimal Controller Design using Evolutionary Algorithm with Convergence Properties
Directory of Open Access Journals (Sweden)
Yousef Alipouri
2014-06-01
Full Text Available Many real-world applications require minimization of a cost function. This function is the criterion that figures out optimally. In the control engineering, this criterion is used in the design of optimal controllers. Cost function optimization has difficulties including calculating gradient function and lack of information about the system and the control loop. In this article, for the first time, gradient memetic evolutionary programming is proposed for minimization of non-convex cost functions that have been defined in control engineering. Moreover, stability and convergence of the proposed algorithm are proved. Besides, it is modified to be used in online optimization. To achieve this, the sign of the gradient function is utilized. For calculating the sign of the gradient, there is no need to know the cost-function’s shape. The gradient functions are estimated by the algorithm. The proposed algorithm is used to design a PI controller for nonlinear benchmark system CSTR (Continuous Stirred Tank Reactor by online and off-line approaches.
Boumediene ALLAOUA; Laoufi, Abdellah; Brahim GASBAOUI; Nasri, Abdelfatah; Abdessalam ABDERRAHMANI
2008-01-01
In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...
Control optimization, stabilization and computer algorithms for aircraft applications
Athans, M. (Editor); Willsky, A. S. (Editor)
1982-01-01
The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory.
Study of sequential optimal control algorithm smart isolation structure based on Simulink-S function
Liu, Xiaohuan; Liu, Yanhui
2017-01-01
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.
Application of Improved Genetic Algorithm in PID Controller Parameters Optimization
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Ying Chen
2013-01-01
Full Text Available Ying Chen, Yong-jie Ma, Wen-xia Yun College of Physics and Electronic Engineering, Northwest Normal University, Anning Road no.967 ,Lanzhou,China,0931-7971503 e-mail:chenying1386685@126.com Abstract The setting and optimization of Proportion Integration Differentiation(PID parameters have been always the important study topics in the automatic control field. The current optimization design methods are often difficult to consider the system requirements for quickness ,reliability and robustness .So a method of PID controller parameters optimization based on Improved Genetic Algorithm(IGA is presented .Simulations with Matlab have proved that the control performance index based on IGA is better than that of the GA method and Z-N method, and is a method which has good practical value of the PID parameter setting and optimization .
Acceleration of quantum optimal control theory algorithms with mixing strategies.
Castro, Alberto; Gross, E K U
2009-05-01
We propose the use of mixing strategies to accelerate the convergence of the common iterative algorithms utilized in quantum optimal control theory (QOCT). We show how the nonlinear equations of QOCT can be viewed as a "fixed-point" nonlinear problem. The iterative algorithms for this class of problems may benefit from mixing strategies, as it happens, e.g., in the quest for the ground-state density in Kohn-Sham density-functional theory. We demonstrate, with some numerical examples, how the same mixing schemes utilized in this latter nonlinear problem may significantly accelerate the QOCT iterative procedures.
Directory of Open Access Journals (Sweden)
Sukanta Nama
2016-04-01
Full Text Available Differential evolution (DE is an effective and powerful approach and it has been widely used in different environments. However, the performance of DE is sensitive to the choice of control parameters. Thus, to obtain optimal performance, time-consuming parameter tuning is necessary. Backtracking Search Optimization Algorithm (BSA is a new evolutionary algorithm (EA for solving real-valued numerical optimization problems. An ensemble algorithm called E-BSADE is proposed which incorporates concepts from DE and BSA. The performance of E-BSADE is evaluated on several benchmark functions and is compared with basic DE, BSA and conventional DE mutation strategy.
Directory of Open Access Journals (Sweden)
Boumediene ALLAOUA
2008-12-01
Full Text Available In this paper, an intelligent controller of the DC (Direct current Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became very strong, gives a very good results and possesses good robustness.
Energy Optimal Control Strategy of PHEV Based on PMP Algorithm
Directory of Open Access Journals (Sweden)
Tiezhou Wu
2017-01-01
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.
Optimal control of switched linear systems based on Migrant Particle Swarm Optimization algorithm
Xie, Fuqiang; Wang, Yongji; Zheng, Zongzhun; Li, Chuanfeng
2009-10-01
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.
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
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…
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
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…
OPTIMAL-TUNING OF PID CONTROLLER GAINS USING GENETIC ALGORITHMS
Directory of Open Access Journals (Sweden)
Ömer GÜNDOĞDU
2005-01-01
Full Text Available This paper presents a method of optimum parameter tuning of a PID controller to be used in driving an inertial load by a dc motor thorough a gearbox. Specifically, the method uses genetic algorithms to determine the optimum controller parameters by minimizing the sum of the integral of the squared error and the squared controller output deviated from its steady state value. The paper suggests the use of Ziegler-Nichols settings to form the intervals for the controller parameters in which the population to be formed. The results obtained from the genetic algorithms are compared with the ones from Ziegler-Nichols in both figures and tabular form. Comparatively better results are obtained in the genetic algorithm case.
Genetic algorithms for optimal design and control of adaptive structures
Ribeiro, R; Dias-Rodrigues, J; Vaz, M
2000-01-01
Future High Energy Physics experiments require the use of light and stable structures to support their most precise radiation detection elements. These large structures must be light, highly stable, stiff and radiation tolerant in an environment where external vibrations, high radiation levels, material aging, temperature and humidity gradients are not negligible. Unforeseen factors and the unknown result of the coupling of environmental conditions, together with external vibrations, may affect the position stability of the detectors and their support structures compromising their physics performance. Careful optimization of static and dynamic behavior must be an essential part of the engineering design. Genetic Algorithms ( GA) belong to the group of probabilistic algorithms, combining elements of direct and stochastic search. They are more robust than existing directed search methods with the advantage of maintaining a population of potential solutions. There is a class of optimization problems for which Ge...
Scalable algorithms for optimal control of stochastic PDEs
Ghattas, Omar
2016-01-07
We present methods for the optimal control of systems governed by partial differential equations with infinite-dimensional uncertain parameters. We consider an objective function that involves the mean and variance of the control objective, leading to a risk-averse optimal control formulation. To make the optimal control problem computationally tractable, we employ a local quadratic approximation of the objective with respect to the uncertain parameter. This enables computation of the mean and variance of the control objective analytically. The resulting risk-averse optimization problem is formulated as a PDE-constrained optimization problem with constraints given by the forward and adjoint PDEs for the first and second-order derivatives of the quantity of interest with respect to the uncertain parameter, and with an objective that involves the trace of a covariance-preconditioned Hessian (of the objective with respect to the uncertain parameters) operator. A randomized trace estimator is used to make tractable the trace computation. Adjoint-based techniques are used to derive an expression for the infinite-dimensional gradient of the risk-averse objective function via the Lagrangian, leading to a quasi-Newton method for solution of the optimal control problem. A specific problem of optimal control of a linear elliptic PDE that describes flow of a fluid in a porous medium with uncertain permeability field is considered. We present numerical results to study the consequences of the local quadratic approximation and the efficiency of the method.
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Mostafa Lotfi Forushani
2012-04-01
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.
Rapid Optimal Generation Algorithm for Terrain Following Trajectory Based on Optimal Control
Institute of Scientific and Technical Information of China (English)
杨剑影; 张海; 谢邦荣; 尹健
2004-01-01
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.
Well Control Optimization using Derivative-Free Algorithms and a Multiscale Approach
Wang, Xiang; Feng, Qihong
2015-01-01
In this paper, we use numerical optimization algorithms and a multiscale approach in order to find an optimal well management strategy over the life of the reservoir. The large number of well rates for each control step make the optimization problem more difficult and at a high risk of achieving a suboptimal solution. Moreover, the optimal number of adjustments is not known a priori. Adjusting well controls too frequently will increase unnecessary well management and operation cost, and an excessively low number of control adjustments may not be enough to obtain a good yield. We investigate three derivative-free optimization algorithms, chosen for their robust and parallel nature, to determine optimal well control strategies. The algorithms chosen include generalized pattern search (GPS), particle swarm optimization (PSO) and covariance matrix adaptation evolution strategy (CMA-ES). These three algorithms encompass the breadth of available black-box optimization strategies: deterministic local search, stochas...
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Meysam Gheisarnezhad
2015-01-01
Full Text Available Fractional-order PID (FOPID controller is a generalization of standard PID controller using fractional calculus. Compared with the Standard PID controller, two adjustable variables “differential order” and “integral order” are added to the PID controller.Three tank system is a nonlinear multivariable process that is a good prototype of chemical industrial processes. Cuckoo Optimization Algorithm (COA, that was recently introduced has shown its good performance in optimization problems. In this study, Improved Cuckoo Optimization Algorithm (ICOA has been presented. The aim of the paper is to compare different controllers tuned with a Improved Cuckoo Optimization Algorithm (ICOA for Three Tank System. In order to compare the performance of the optimized FOPID controller with other controllers, Genetic Algorithm(GA, Particle swarm optimization (PSO, Cuckoo Optimization Algorithm (COA and Imperialist Competitive Algorithm (ICA.
A BPTT-like Min-Max Optimal Control Algorithm for Nonlinear Systems
Milić, Vladimir; Kasać, Josip; Majetić, Dubravko; Šitum, Željko
2010-09-01
This paper presents a conjugate gradient-based algorithm for feedback min-max optimal control of nonlinear systems. The algorithm has a backward-in-time recurrent structure similar to the back propagation through time (BPTT) algorithm. The control law is given as the output of the one-layer neural network. Main contribution of the paper includes the integration of BPTT techniques, conjugate gradient methods, Adams method for solving ODEs and automatic differentiation (AD), to provide an effective, novel algorithm for solving numerically optimally min-max control problems. The proposed algorithm is applied to the rotational/translational actuator (RTAC) nonlinear benchmark problem with control and state vector constraints.
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Po-Chen Cheng
2015-06-01
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.
Approximation of Reachable Sets using Optimal Control Algorithms
Baier, Robert; Gerdts, Matthias; Xausa, Ilaria
2013-01-01
To appear; International audience; Numerical experiences with a method for the approximation of reachable sets of nonlinear control systems are reported. The method is based on the formulation of suitable optimal control problems with varying objective functions, whose discretization by Euler's method lead to finite dimensional non-convex nonlinear programs. These are solved by a sequential quadratic programming method. An efficient adjoint method for gradient computation is used to reduce th...
Debbarma, Sanjoy; Saikia, Lalit Chandra; Sinha, Nidul
2014-03-01
Present work focused on automatic generation control (AGC) of a three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRC). A fractional order (FO) controller named as I(λ)D(µ) controller based on crone approximation is proposed for the first time as an appropriate technique to solve the multi-area AGC problem in power systems. A recently developed metaheuristic algorithm known as firefly algorithm (FA) is used for the simultaneous optimization of the gains and other parameters such as order of integrator (λ) and differentiator (μ) of I(λ)D(µ) controller and governor speed regulation parameters (R). The dynamic responses corresponding to optimized I(λ)D(µ) controller gains, λ, μ, and R are compared with that of classical integer order (IO) controllers such as I, PI and PID controllers. Simulation results show that the proposed I(λ)D(µ) controller provides more improved dynamic responses and outperforms the IO based classical controllers. Further, sensitivity analysis confirms the robustness of the so optimized I(λ)D(µ) controller to wide changes in system loading conditions and size and position of SLP. Proposed controller is also found to have performed well as compared to IO based controllers when SLP takes place simultaneously in any two areas or all the areas. Robustness of the proposed I(λ)D(µ) controller is also tested against system parameter variations.
Optimal Control Problem Governed by Semilinear Parabolic Equation and its Algorithm
Institute of Scientific and Technical Information of China (English)
Chun-fa Li; Xue Yang; En-min Feng
2008-01-01
In this paper, an optimal control problem governed by semilinear parabolic equation which involves the control variable acting on forcing term and coefficients appearing in the higher order derivative terms is formulated and analyzed. The strong variation method, due originally to Mayne et al to solve the optimal control problem of a lumped parameter system, is extended to solve an optimal control problem governed by semilinear parabolic equation, a necessary condition is obtained, the strong variation algorithm for this optimal control problem is presented, and the corresponding convergence result of the algorithm is verified.
Moini, A
2002-01-01
In this paper, genetic algorithms are used in the design and robustification various mo el-ba ed/non-model-based fuzzy-logic controllers for robotic manipulators. It is demonstrated that genetic algorithms provide effective means of designing the optimal set of fuzzy rules as well as the optimal domains of associated fuzzy sets in a new class of model-based-fuzzy-logic controllers. Furthermore, it is shown that genetic algorithms are very effective in the optimal design and robustification of non-model-based multivariable fuzzy-logic controllers for robotic manipulators.
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Huseyin Ceylan
2013-01-01
Full Text Available This study proposes a traffic congestion minimization model in which the traffic signal setting optimization is performed through a combined simulation-optimization model. In this model, the TRANSYT traffic simulation software is combined with Differential Evolution (DE optimization algorithm, which is based on the natural selection paradigm. In this context, the EQuilibrium Network Design (EQND problem is formulated as a bilevel programming problem in which the upper level is the minimization of the total network performance index. In the lower level, the traffic assignment problem, which represents the route choice behavior of the road users, is solved using the Path Flow Estimator (PFE as a stochastic user equilibrium assessment. The solution of the bilevel EQND problem is carried out by the proposed Differential Evolution and TRANSYT with PFE, the so-called DETRANSPFE model, on a well-known signal controlled test network. Performance of the proposed model is compared to that of two previous works where the EQND problem has been solved by Genetic-Algorithms- (GAs- and Harmony-Search- (HS- based models. Results show that the DETRANSPFE model outperforms the GA- and HS-based models in terms of the network performance index and the computational time required.
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Ru Wang
2017-01-01
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.
Levenberg – Marquardt’s Algorithm used for PID Controller Parameters Optimization
Ahmed S. Abd El-Hamid; Ahmed H. Eissa; Aly M. Radwan
2015-01-01
The determination of parameters of controllers is an important problem in automatic control systems. In this paper, the Levenberg Marquardt (LM) Algorithm is used to effectively solve this problem with reasonable computational effort. The Levenberg Marquardt (LM) Algorithm for optimization of three term (PID) controller parameters with dynamic model of pH neutralization process is presented. The main goal is to show the merits of Levenberg Marquardt algorithm optimizat...
Micro-Turbine Generation Control System Optimization Using Evolutionary algorithm
Directory of Open Access Journals (Sweden)
Mohanraj B S
2014-10-01
Full Text Available Distribution systems management is becoming an increasingly complicated issue due to the introduction of new technologies, new energy trading strategies, and new deregulated environment. In the new deregulated energy market and considering the incentives coming from the technical and economical fields, it is reasonable to consider Distributed Generation (DG as a viable option to solve the lacking electric power supply problem. This paper presents a mathematical distribution system planning model considering three planning options to system expansion and to meet the load growth requirements with a reasonable price as well as the system power quality problems. DG is introduced as an attractive planning option in competition with voltage regulator devices and Interruptible load. This paper presents a dynamic modelling and simulation of a high speed single shaft micro-turbine generation (MTG system for grid connected operation and shows genetic algorithm (GA role in improvement of control system operation. The model is developed with the consideration of the main parts including: compressor-turbine, permanent magnet (PM generator, three phase bridge rectifier and inverter. The simulation results show the capability of Genetic Algorithm for controlling MTG system. The model is developed in Mat lab / Simulink.
A Hybrid Bacterial Foraging - PSO Algorithm Based Tuning of Optimal FOPI Speed Controller
Directory of Open Access Journals (Sweden)
Rajasekhar Anguluri
2011-11-01
Full Text Available Bacterial Foraging Optimization Algorithm (BFOA has recently emerged as a very powerful technique for real parameteroptimization. In order to overcome the delay in optimization and to further enhance the performance of BFO, this paper proposeda new hybrid algorithm combining the features of BFOA and Particle Swarm Optimization (PSO for tuning a Fractional orderspeed controller in a Permanent Magnet Synchronous Motor (PMSM Drive. Computer simulations illustrate the effectiveness of theproposed approach compared to that of basic versions of PSO and BFO.
DEFF Research Database (Denmark)
Khoobi, Saeed; Halvaei, Abolfazl; Hajizadeh, Amin
2016-01-01
of EV relies too much on the expert experience and it may lead to sub-optimal performance. This paper develops an optimized fuzzy controller using genetic algorithm (GA) for an electric vehicle equipped with two power bank including battery and super-capacitor. The model of EV and optimized fuzzy...
A MATLAB GUI for a Legendre Pseudospectral algorithm for optimal control problems
Hall, Andrew O.
1999-01-01
Approved for public release; distribution is unlimited This implementation of a Legendre-Gauss-Lobatto Pseudospectral (LGLP) algorithm takes advantage of the MATLAB Graphical User Interface (GUI) and the Optimization Toolbox to allow an efficient implementation of a direct solution technique. Direct solutions techniques solve optimal control problems without solving for the optimality conditions. Using the LGLP method, an optimal control problem is discretized into a Nonlinear Program (NLP...
Optimization of PID Controller for Brushless DC Motor by using Bio-inspired Algorithms
National Research Council Canada - National Science Library
Sanjay Kr. Singh; Nitish Katal; S.G. Modani
2014-01-01
This study presents the use and comparison of various bio-inspired algorithms for optimizing the response of a PID controller for a Brushless DC Motor in contrast to the conventional methods of tuning...
Moore, J H
1995-06-01
A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.
An optimal consensus tracking control algorithm for autonomous underwater vehicles with disturbances
Zhang, Jian Yuan Wen-Xia
2012-01-01
The optimal disturbance rejection control problem is considered for consensus tracking systems affected by external persistent disturbances and noise. Optimal estimated values of system states are obtained by recursive filtering for the multiple autonomous underwater vehicles modeled to multi-agent systems with Kalman filter. Then the feedforward-feedback optimal control law is deduced by solving the Riccati equations and matrix equations. The existence and uniqueness condition of feedforward-feedback optimal control law is proposed and the optimal control law algorithm is carried out. Lastly, simulations show the result is effectiveness with respect to external persistent disturbances and noise.
Time-optimal monotonic convergent algorithms for the control of quantum systems
Lapert, M; Sugny, D
2012-01-01
We present a new formulation of monotonically convergent algorithms which allows to optimize both the control duration and the field fluence. A standard algorithm designs a control field of fixed duration which both brings the system close to the target state and minimizes its fluence, whereas here we include in addition the optimization of the duration in the cost functional. We apply this new algorithm to the control of spin systems in Nuclear Magnetic Resonance. We show how to implement CNOT gates in systems of two and four coupled spins.
Neural Network Control Optimization based on Improved Genetic Algorithm
Directory of Open Access Journals (Sweden)
Zhaoyin Zhang
2013-08-01
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.
Liu, Derong; Li, Hongliang; Wang, Ding
2015-06-01
In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.
Optimization of S-surface controller for autonomous underwater vehicle with immune-genetic algorithm
Institute of Scientific and Technical Information of China (English)
LI Ye; ZHANG Lei; WAN Lei; LIANG Xiao
2008-01-01
To deduce error and fussy work of manual adjustment of parameters for an S-surface controller in underwater vehicle motion control, the immune-genetic optimization of S-surface controller of an underwater vehicle was proposed. The ability of producing various antibodies for the immune algorithm, the self-adjustment of antibody density, and the antigen immune memory were used to realize the rapid convergence of S-surface controller parameters. It avoided loitering near the local peak value. Deduction of the S-surface controller was given. General process of the immune-genetic algorithm was described and immune-genetic optimization of S-surface controller parameters was discussed. Definitive results were obtained from many simulation experiments and lake experiments, which indicate that the algorithm can get good effect in optimizing the nonlinear motion controller parameters of an underwater vehicle.
Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor
Directory of Open Access Journals (Sweden)
K. Premkumar
2016-06-01
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.
Stability of the Newton-Like algorithm in optimization flow control
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
The stability of the Newton-like algorithm in optimization flow control is considered in this paper.This algorithm is proved to be globally stable under a general network topology by means of Lyapunov stability theory, without considering the round trip time of each source. While the stability of this algorithm with considering the round trip time is analyzed as well. The analysis shows that the algorithm with only one bottleneck link accessed by several sources is also globally stable, and all trajectories described by this algorithm ultimately converge to the equilibrium point.
Energy Technology Data Exchange (ETDEWEB)
Medeiros, Jose Antonio Carlos Canedo; Machado, Marcelo Dornellas; Lima, Alan Miranda M. de; Schirru, Roberto [Instituto Alberto Luiz Coimbra de Pos-Graduacao e Pesquisa de Engenharia (COPPE/UFRJ-RJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear. Lab. de Monitoracao de Processos
2007-07-01
Predictive control systems are control systems that use a model of the controlled system (plant), used to predict the future behavior of the plant allowing the establishment of an anticipative control based on a future condition of the plant, and an optimizer that, considering a future time horizon of the plant output and a recent horizon of the control action, determines the controller's outputs to optimize a performance index of the controlled plant. The predictive control system does not require analytical models of the plant; the model of predictor of the plant can be learned from historical data of operation of the plant. The optimizer of the predictive controller establishes the strategy of the control: the minimization of a performance index (objective function) is done so that the present and future control actions are computed in such a way to minimize the objective function. The control strategy, implemented by the optimizer, induces the formation of an optimal control mechanism whose effect is to reduce the stabilization time, the 'overshoot' and 'undershoot', minimize the control actuation so that a compromise among those objectives is attained. The optimizer of the predictive controller is usually implemented using gradient-based algorithms. In this work we use the Particle Swarm Optimization algorithm (PSO) in the optimizer component of a predictive controller applied in the control of the xenon oscillation of a pressurized water reactor (PWR). The PSO is a stochastic optimization technique applied in several disciplines, simple and capable of providing a global optimal for high complexity problems and difficult to be optimized, providing in many cases better results than those obtained by other conventional and/or other artificial optimization techniques. (author)
Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm
Directory of Open Access Journals (Sweden)
Feifei Dong
2014-01-01
Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.
Research on Optimal Control for the Vehicle Suspension Based on the Simulated Annealing Algorithm
Directory of Open Access Journals (Sweden)
Jie Meng
2014-01-01
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.
Directory of Open Access Journals (Sweden)
Saifullah Khalid
2016-09-01
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.
DEFF Research Database (Denmark)
Ursem, Rasmus Kjær
optimization. In addition to general investigations in these areas, I introduce a number of algorithms and demonstrate their potential on real-world problems in system identification and control. Furthermore, I investigate dynamic optimization problems in the context of the three fundamental areas as well...
Optimization of PID controller based on The Bees Algorithm for one leg of a quadruped robot
Directory of Open Access Journals (Sweden)
Bakırcıoğlu Veli
2016-01-01
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.
Chakraborty, Mithun; Konar, Amit
2008-01-01
The Proportional-Integral-Derivative Controller is widely used in industries for process control applications. Fractional-order PID controllers are known to outperform their integer-order counterparts. In this paper, we propose a new technique of fractional-order PID controller synthesis based on peak overshoot and rise-time specifications. Our approach is to construct an objective function, the optimization of which yields a possible solution to the design problem. This objective function is optimized using two popular bio-inspired stochastic search algorithms, namely Particle Swarm Optimization and Differential Evolution. With the help of a suitable example, the superiority of the designed fractional-order PID controller to an integer-order PID controller is affirmed and a comparative study of the efficacy of the two above algorithms in solving the optimization problem is also presented.
Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
SHEN Hong; WAN Jianru; ZHANG Zhichao; LIU Yingpei; LI Guangye
2009-01-01
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.
Optimal fuzzy PID controller with adjustable factors based on flexible polyhedron search algorithm
Institute of Scientific and Technical Information of China (English)
谭冠政; 肖宏峰; 王越超
2002-01-01
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.
Two neural network algorithms for designing optimal terminal controllers with open final time
Plumer, Edward S.
1992-01-01
Multilayer neural networks, trained by the backpropagation through time algorithm (BPTT), have been used successfully as state-feedback controllers for nonlinear terminal control problems. Current BPTT techniques, however, are not able to deal systematically with open final-time situations such as minimum-time problems. Two approaches which extend BPTT to open final-time problems are presented. In the first, a neural network learns a mapping from initial-state to time-to-go. In the second, the optimal number of steps for each trial run is found using a line-search. Both methods are derived using Lagrange multiplier techniques. This theoretical framework is used to demonstrate that the derived algorithms are direct extensions of forward/backward sweep methods used in N-stage optimal control. The two algorithms are tested on a Zermelo problem and the resulting trajectories compare favorably to optimal control results.
Directory of Open Access Journals (Sweden)
Georgios E. Stavroulakis
2013-10-01
Full Text Available This paper presents a numerical study on optimal voltages and optimal placement of piezoelectric actuators for shape control of beam structures. A finite element model, based on Timoshenko beam theory, is developed to characterize the behavior of the structure and the actuators. This model accounted for the electromechanical coupling in the entire beam structure, due to the fact that the piezoelectric layers are treated as constituent parts of the entire structural system. A hybrid scheme is presented based on great deluge and genetic algorithm. The hybrid algorithm is implemented to calculate the optimal locations and optimal values of voltages, applied to the piezoelectric actuators glued in the structure, which minimize the error between the achieved and the desired shape. Results from numerical simulations demonstrate the capabilities and efficiency of the developed optimization algorithm in both clamped−free and clamped−clamped beam problems are presented.
Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm
Directory of Open Access Journals (Sweden)
V. Rajinikanth
2012-01-01
Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.
Optimization of PID Controller for Brushless DC Motor by using Bio-inspired Algorithms
Directory of Open Access Journals (Sweden)
Sanjay Kr. Singh
2014-02-01
Full Text Available This study presents the use and comparison of various bio-inspired algorithms for optimizing the response of a PID controller for a Brushless DC Motor in contrast to the conventional methods of tuning. For the optimization of the PID controllers Genetic Algorithm, Multi-objective Genetic Algorithm and Simulated Annealing have been used. PID controller tuning with soft-computing algorithms comprises of obtaining the best possible outcome for the three PID parameters for improving the steady state characteristics and performance indices like overshoot percentage, rise time and settling time. For the calculation and simulation of the results the Brushless DC Motor model, Maxon EC 45 flat ф 45 mm with Hall Sensors Motor has been used. The results obtained the optimization using Genetic Algorithms, Multi-objective Genetic Algorithm and Simulated Annealing is compared with the ones derived from the Ziegler-Nichols method and the MATLAB SISO Tool. And it is observed that comparatively better results are obtained by optimization using Simulated Annealing offering better steady state response.
Optimal fuzzy PID control tuned with genetic algorithms
Santos, Carlos Miguel Almeida
2013-01-01
Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers tha...
Optimization of type-2 fuzzy controllers using the bee colony algorithm
Amador, Leticia
2017-01-01
This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering the fuzzy control area. The main idea is that this areas together can to solve various control problems and to find better results. In this book we test the proposed method using two benchmark problems; the problem for filling a water tank and the problem for controlling the trajectory in an autonomous mobile robot. When Interval Type-2 Fuzzy Logic System is implemented to model the behavior of systems, the results show a better stabilization, because the analysis of uncertainty is better. For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-01-01
An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers.
Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System
Bendjeghaba, Omar
2014-01-01
This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.
The Distribution Population-based Genetic Algorithm for Parameter Optimization PID Controller
Institute of Scientific and Technical Information of China (English)
CHENQing-Geng; WANGNing; HUANGShao-Feng
2005-01-01
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.
Energy Technology Data Exchange (ETDEWEB)
Sharma, Sitansh, E-mail: sitansh@research.iiit.ac.in [Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032 (India); Singh, Harjinder, E-mail: harjinder.singh@iiit.ac.in [Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032 (India)
2011-11-18
Graphical abstract: Application of genetic algorithm optimization to control dissociation process in the ground electronic state of HF molecule is demonstrated. Highlights: Black-Right-Pointing-Pointer Genetic algorithm optimization for the design of laser pulses. Black-Right-Pointing-Pointer Control of dissociation process in the ground electronic state of HF molecule. Black-Right-Pointing-Pointer Two types of pulses, one with fixed frequency components and the other having non-deterministic components. Black-Right-Pointing-Pointer Optimized laser fields possess simple time and frequency structures. - Abstract: We have applied genetic algorithm optimization for the design of laser pulses to control dissociation process in the ground electronic state of HF molecule, within the mathematical framework of optimal control theory. In order to design the experimentally feasible laser fields, we coded the small set of selected field parameters in the GA parameter space. Two types of pulses, one with fixed frequency components and the other having non-deterministic components have been designed. Optimized laser field obtained using this approach, possesses simple time and frequency structures. We show that the fields having non-deterministic frequency components lead to greater dissociation probability compared to the ones having deterministic frequency components.
Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information.The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.The results of function optimization show that the algorithm has good searching ability and high convergence speed.The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum.In order to avoid the combinatorial explosion of fuzzy.rules due to multivariable inputs,a state variable synthesis scheme is emploved to reduce the number of fuzzy rules greatly.The simulation results show that the designed controller can control the inverted pendulum successfully.
Molecular Quantum Computing by an Optimal Control Algorithm for Unitary Transformations
Palao, J P; Palao, Jose P.; Kosloff, Ronnie
2002-01-01
Quantum computation is based on implementing selected unitary transformations which represent algorithms. A generalized optimal control theory is used to find the driving field that generates a prespecified unitary transformation. The approach is illustrated in the implementation of one and two qubits gates in model molecular systems.
Galvan-Sosa, M.; Portilla, J.; Hernandez-Rueda, J.; Siegel, J.; Moreno, L.; Ruiz de la Cruz, A.; Solis, J.
2014-02-01
Femtosecond laser pulse temporal shaping techniques have led to important advances in different research fields like photochemistry, laser physics, non-linear optics, biology, or materials processing. This success is partly related to the use of optimal control algorithms. Due to the high dimensionality of the solution and control spaces, evolutionary algorithms are extensively applied and, among them, genetic ones have reached the status of a standard adaptive strategy. Still, their use is normally accompanied by a reduction of the problem complexity by different modalities of parameterization of the spectral phase. Exploiting Rabitz and co-authors' ideas about the topology of quantum landscapes, in this work we analyze the optimization of two different problems under a deterministic approach, using a multiple one-dimensional search (MODS) algorithm. In the first case we explore the determination of the optimal phase mask required for generating arbitrary temporal pulse shapes and compare the performance of the MODS algorithm to the standard iterative Gerchberg-Saxton algorithm. Based on the good performance achieved, the same method has been applied for optimizing two-photon absorption starting from temporally broadened laser pulses, or from laser pulses temporally and spectrally distorted by non-linear absorption in air, obtaining similarly good results which confirm the validity of the deterministic search approach.
Bhole, Gaurav; Anjusha, V. S.; Mahesh, T. S.
2016-04-01
A robust control over quantum dynamics is of paramount importance for quantum technologies. Many of the existing control techniques are based on smooth Hamiltonian modulations involving repeated calculations of basic unitaries resulting in time complexities scaling rapidly with the length of the control sequence. Here we show that bang-bang controls need one-time calculation of basic unitaries and hence scale much more efficiently. By employing a global optimization routine such as the genetic algorithm, it is possible to synthesize not only highly intricate unitaries, but also certain nonunitary operations. We demonstrate the unitary control through the implementation of the optimal fixed-point quantum search algorithm in a three-qubit nuclear magnetic resonance (NMR) system. Moreover, by combining the bang-bang pulses with the crusher gradients, we also demonstrate nonunitary transformations of thermal equilibrium states into effective pure states in three- as well as five-qubit NMR systems.
Directory of Open Access Journals (Sweden)
Russel J Stonier
2003-08-01
Full Text Available In this paper we examine the application of evolutionary algorithms to find open-loop control solutions of the optimal control problem arising from the semidiscretisation of a linear parabolic tracking problem with boundary control. The solution is compared with the solutions obtained by methods based upon the variational equations of the Minimum Principle and the finite element method.
Yazdi, Ebrahim
2010-01-01
In this paper, a simple Neural controller has been used to achieve stable walking in a NAO biped robot, with 22 degrees of freedom that implemented in a virtual physics-based simulation environment of Robocup soccer simulation environment. The algorithm uses a Matsuoka base neural oscillator to generate control signal for the biped robot. To find the best angular trajectory and optimize network parameters, a new population-based search algorithm, called the Harmony Search (HS) algorithm, has been used. The algorithm conceptualized a group of musicians together trying to search for better state of harmony. Simulation results demonstrate that the modification of the step period and the walking motion due to the sensory feedback signals improves the stability of the walking motion.
Optimization and Optimal Control
Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider
2010-01-01
During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou
Fu, Xingang; Li, Shuhui; Fairbank, Michael; Wunsch, Donald C; Alonso, Eduardo
2015-09-01
This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.
Genetic Optimization Algorithm of PID Decoupling Control for VAV Air-Conditioning System
Institute of Scientific and Technical Information of China (English)
WANG Jiangjiang; AN Dawei; ZHANG Chunfa; JING Youyin
2009-01-01
Variable-air-volume (VAV) air-conditioning system is a multi-variable system and has multi coupling control loops. While all of the control loops are working together, they interfere and influence each other. A multi-variable decoupling PID controller is designed for VAV air-conditioning system. Diagonal matrix decoupling method is employed to eliminate the coupling between the loop of supply air temperature and that of thermal-space air temperature. The PID controller parameters are optimized by means of an improved genetic algorithm in floating point representations to obtain better performance. The population in the improved genetic algorithm mutates before crossover, which is helpful for the convergence. Additionally the micro mutation algorithm is proposed and applied to improve the convergence during the later evolution. To search the best parameters, the optimized parameters ranges should be amplified l0 times the initial ideal parameters. The simulation and experiment results show that the decoupling control system is effective and feasible. The method can overcome the strong coupling feature of the system and has shorter governing time and less over-shoot than non-optimization PID control.
Directory of Open Access Journals (Sweden)
Sanjay Kr. Singh
2014-05-01
Full Text Available This study focuses on multi-objective optimization of the PID controllers for optimal speed control for an isolated steam turbine. In complex operations, optimal tuning plays an imperative role in maintaining the product quality and process safety. This study focuses on the comparison of the optimal PID tuning using Multi-objective Genetic Algorithm (NSGA-II against normal genetic algorithm and Ziegler Nichols methods for the speed control of an isolated steam turbine. Isolated steam turbine not being connected to the grid; hence is usually used in refineries as steam turbine, where a hydraulic governor is used for the speed control. The PID controller for the system has been designed and implemented using MATLAB and SIMULINK and the results of the design methods have been compared, analysed and conclusions indicates that the significant improvement of results have been obtained by the Multi-Objective GA based optimization of PID as much faster response is obtained as compared to the ordinary GA and Ziegler Nichols method.
Group Leaders Optimization Algorithm
Daskin, Anmer
2010-01-01
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.
Directory of Open Access Journals (Sweden)
S.Farook,
2011-05-01
Full Text Available In this paper an attempt is made to optimize the feedback controller to improve the dynamics of a restructured multiarea power system using Evolutionary Real coded Genetic Algorithm (RCGA.Optimization using state variables is a difficult task as the access to all variables is limited and also measuring all of them is impossible. To solve the problem Evolutionary Genetic algorithms wereproposed to optimize the feedback gains of the controller, having access to few of the AGC variables. The feasibility and robustness of the algorithm is investigated on a two area interconnected power system consisting of two identical thermal plants in each areas in restructured environment. The dynamics of frequency deviations and tie-line power deviations were investigated by considering a demand of 0.1pu MW contracted by GENCOs in each area of the restructured power system. The results obtained by the proposed method are found to be quite encouraging when compared with those achieved using optimal controllers derived using Linear Quadratic Regulator (LQR theory.
Optimization algorithms and applications
Arora, Rajesh Kumar
2015-01-01
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
Institute of Scientific and Technical Information of China (English)
ANDRES-TOROB.; GIRON-SIERRAJ.M.; FERNANDEZ-BLANCOP.; LOPEZ-OROZCOJ.A.; BESADA-PORTASE.
2004-01-01
This paper describes empirical research on the model, optimization and supervisory control of beer fermentation.Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results.The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs).Successful finding of optimal ways to drive these processes were reported.Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.
Institute of Scientific and Technical Information of China (English)
ANDR(E)S-TORO B.; GIR(O)N-SIERRA J.M.; FERN(A)NDEZ-BLANCO P.; L(O)PEZ-OROZCO J.A.; BESADA-PORTAS E.
2004-01-01
This paper describes empirical research on the model, optimization and supervisory control of beer fermentation. Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results. The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs). Successful finding of optimal ways to drive these processes were reported. Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.
Model reduction algorithms for optimal control and importance sampling of diffusions
Hartmann, Carsten; Schütte, Christof; Zhang, Wei
2016-08-01
We propose numerical algorithms for solving optimal control and importance sampling problems based on simplified models. The algorithms combine model reduction techniques for multiscale diffusions and stochastic optimization tools, with the aim of reducing the original, possibly high-dimensional problem to a lower dimensional representation of the dynamics, in which only a few relevant degrees of freedom are controlled or biased. Specifically, we study situations in which either a reaction coordinate onto which the dynamics can be projected is known, or situations in which the dynamics shows strongly localized behavior in the small noise regime. No explicit assumptions about small parameters or scale separation have to be made. We illustrate the approach with simple, but paradigmatic numerical examples.
Parallel implementations of gradient-based iterative algorithms for optimal control problems
Energy Technology Data Exchange (ETDEWEB)
Podrazik, L.J.
1988-01-01
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.
Robust state feedback controller design of STATCOM using chaotic optimization algorithm
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Safari Amin
2010-01-01
Full Text Available In this paper, a new design technique for the design of robust state feedback controller for static synchronous compensator (STATCOM using Chaotic Optimization Algorithm (COA is presented. The design is formulated as an optimization problem which is solved by the COA. Since chaotic planning enjoys reliability, ergodicity and stochastic feature, the proposed technique presents chaos mapping using Lozi map chaotic sequences which increases its convergence rate. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results reveal that the proposed controller has an excellent capability in damping power system low frequency oscillations and enhances greatly the dynamic stability of the power systems. Moreover, the system performance analysis under different operating conditions shows that the phase based controller is superior compare to the magnitude based controller.
Energy Technology Data Exchange (ETDEWEB)
Larbes, C.; Ait Cheikh, S.M.; Obeidi, T.; Zerguerras, A. [Laboratoire des Dispositifs de Communication et de Conversion Photovoltaique, Departement d' Electronique, Ecole Nationale Polytechnique, 10, Avenue Hassen Badi, El Harrach, Alger 16200 (Algeria)
2009-10-15
This paper presents an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and irradiance conditions. First, for the purpose of comparison and because of its proven and good performances, the perturbation and observation (P and O) technique is briefly introduced. A fuzzy logic controller based MPPT (FLC) is then proposed which has shown better performances compared to the P and O MPPT based approach. The proposed FLC has been also improved using genetic algorithms (GA) for optimisation. Different development stages are presented and the optimized fuzzy logic MPPT controller (OFLC) is then simulated and evaluated, which has shown better performances. (author)
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.
2014-01-01
. 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......In a DC microgrid, several paralleled conversion systems are installed in distributed substations for transferring power from external grid to a DC microgrid. Droop control is used for the distributed load sharing among all the DC/DC converters. Considering the typical efficiency feature of power...... electronic converters, optimization method can be implemented in tertiary level for improving the overall system efficiency. However, optimization purposes usually require centralized communication, data acquisition and computation which might be either impractical or costly for dispersed systems...
An ECN-based Optimal Flow Control Algorithm for the Internet
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
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.
PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems
Directory of Open Access Journals (Sweden)
Arturo Y. Jaen-Cuellar
2013-09-01
Full Text Available Performance improvement is the main goal of the study of PID control and much research has been conducted for this purpose. The PID filter is implemented in almost all industrial processes because of its well-known beneficial features. In general, the whole system's performance strongly depends on the controller's efficiency and hence the tuning process plays a key role in the system's behaviour. In this work, the servo systems will be analysed, specifically the positioning control systems. Among the existent tuning methods, the Gain-Phase Margin method based on Frequency Response analysis is the most adequate for controller tuning in positioning control systems. Nevertheless, this method can be improved by integrating an optimization technique. The novelty of this work is the development of a new methodology for PID control tuning by coupling the Gain-Phase Margin method with the Genetic Algorithms in which the micro-population concept and adaptive mutation probability are applied. Simulations using a positioning system model in MATLAB and experimental tests in two CNC machines and an industrial robot are carried out in order to show the effectiveness of the proposal. The obtained results are compared with both the classical Gain-Phase Margin tuning and with a recent PID controller optimization using Genetic Algorithms based on real codification. The three methodologies are implemented using software.
Parametric Optimization of Control Systems Pumping Station by the Genetic Algorithms
Directory of Open Access Journals (Sweden)
M. P. Dounaev
2014-01-01
Full Text Available Now the frequency regulation with its well-known advantages is becoming more popular for using in electric drives. One of these systems is the system of liquid flow stabilization for pumping station of the second flood, which is discussed at the article. There are functional elements of the system: the frequency converter, the induction motor, pump set, check valve, part of the pipeline between the flow sensors and PID controller. In this case are used PID controller with universal input and RS-485 OWEN TRM101.The article tries to solve the main problem, which is to set optimal controller settings. The article presents a mathematical model of the system, built on the basis of transfer functions. The specific values of the parameters for Bratsk pumping station of the second flood are found.To solve the problem of parametric optimization for control of pumping station authors use a genetic algorithm, which allows us to use different quality criteria. A set of parameters for the genetic algorithm is represented, as well as a method of selection and a method of the transition between generations. The problem of parametric optimization was solved by quadratic integral criterion and by the criterion, composed of direct measures of quality (regulation time and the overshoot. The PID controller optimal values of adjustable parameters were obtained both for considered criteria and for their corresponding transient processes. Comparative analysis of results was produced. When proving the optimality of the obtained settings for the quadratic criterion the proximity of the gradient components of the target functional to zero is demonstrated. In the case of a criterion based on a direct measure of quality, is presented a picture of convergence of adjustable parameters to the same value when run with different starting points.It is presented that the use of a criterion based on a direct measure of quality, can significantly reduce the overshoot with a
Directory of Open Access Journals (Sweden)
Mahesh Nagarkar
2016-04-01
Full Text Available In this paper, a genetic algorithm (GA based in an optimization approach is presented in order to search the optimum weighting matrix parameters of a linear quadratic regulator (LQR. A Macpherson strut quarter car suspension system is implemented for ride control application. Initially, the GA is implemented with the objective of minimizing root mean square (RMS controller force. For single objective optimization, RMS controller force is reduced by 20.42% with slight increase in RMS sprung mass acceleration. Trade-off is observed between controller force and sprung mass acceleration. Further, an analysis is extended to multi-objective optimization with objectives such as minimization of RMS controller force and RMS sprung mass acceleration and minimization of RMS controller force, RMS sprung mass acceleration and suspension space deflection. For multi-objective optimization, Pareto-front gives flexibility in order to choose the optimum solution as per designer’s need.
Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm
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Yu-Huei Cheng
2017-03-01
Full Text Available Hybrid electric vehicle (HEV control strategy is a management approach for generating, using, and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage hybrid electric vehicles. In order to realize the optimal control strategy, we use a robust evolutionary computation method called a “memetic algorithm (MA” to optimize the control parameters in parallel HEVs. The “local search” mechanism implemented in the MA greatly enhances its search capabilities. In the implementation of the method, the fitness function combines with the ADvanced VehIcle SimulatOR (ADVISOR and is set up according to an electric assist control strategy (EACS to minimize the fuel consumption (FC and emissions (HC, CO, and NOx of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, the new European driving cycle (NEDC, Federal Test Procedure (FTP, Economic Commission for Europe + Extra-Urban driving cycle (ECE + EUDC, and urban dynamometer driving schedule (UDDS are carried out using the proposed method to find their respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.
A smoothing monotonic convergent optimal control algorithm for NMR pulse sequence design
Maximov, Ivan I; Salomon, Julien; Turinici, Gabriel
2010-01-01
The past decade has demonstrated increasing interests in using optimal control based methods within coherent quantum controllable systems. The versatility of such methods has been demonstrated with particular elegance within nuclear magnetic resonance (NMR) where natural separation between coherent and dissipative spin dynamics processes has enabled coherent quantum control over long periods of time to shape the experiment to almost ideal adoption to the spin system and external manipulations. This has led to new design principles as well as powerful new experimental methods within magnetic resonance imaging, liquid-state and solid-state NMR spectroscopy. For this development to continue and expand, it is crucially important to constantly improve the underlying numerical algorithms to provide numerical solutions which are optimally compatible with implementation on current instrumentation and at same time are numerically stable and offer fast monotonic convergence towards the target. Addressing such aims, we ...
PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems
Directory of Open Access Journals (Sweden)
Arturo Y. Jaen-Cuellar
2013-09-01
Full Text Available Performance improvement is the main goal of the study of PID control and much research has been conducted for this purpose. The PID filter is implemented in almost all industrial processes because of its well‐ known beneficial features. In general, the whole system’s performance strongly depends on the controller’s efficiency and hence the tuning process plays a key role in the system’s behaviour. In this work, the servo systems will be analysed, specifically the positioning control systems. Among the existent tuning methods, the Gain‐ Phase Margin method based on Frequency Response analysis is the most adequate for controller tuning in positioning control systems. Nevertheless, this method can be improved by integrating an optimization technique. The novelty of this work is the development of a new methodology for PID control tuning by coupling the Gain‐Phase Margin method with the Genetic Algorithms in which the micro‐population concept and adaptive mutation probability are applied. Simulations using a positioning system model in MATLAB and experimental tests in two CNC machines and an industrial robot are carried out in order to show the effectiveness of the proposal. The obtained results are compared with both the classical Gain‐Phase Margin tuning and with a recent PID controller optimization using Genetic Algorithms based on real codification. The three methodologies are implemented using software.
Directory of Open Access Journals (Sweden)
S. J. Bassi
2011-10-01
Full Text Available The proportional-integral-derivative (PID controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI method of particle swarm optimization (PSO algorithm for tuning the optimal proportional-integral derivative (PID controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler- Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler- Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
Aranza, M. F.; Kustija, J.; Trisno, B.; Hakim, D. L.
2016-04-01
PID Controller (Proportional Integral Derivative) was invented since 1910, but till today still is used in industries, even though there are many kind of modern controllers like fuzz controller and neural network controller are being developed. Performance of PID controller is depend on on Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). These gains can be got by using method Ziegler-Nichols (ZN), gain-phase margin, Root Locus, Minimum Variance dan Gain Scheduling however these methods are not optimal to control systems that nonlinear and have high-orde, in addition, some methods relative hard. To solve those obstacles, particle swarm optimization (PSO) algorithm is proposed to get optimal Kp, Ki and Kd. PSO is proposed because PSO has convergent result and not require many iterations. On this research, PID controller is applied on AVR (Automatic Voltage Regulator). Based on result of analyzing transient, stability Root Locus and frequency response, performance of PID controller is better than Ziegler-Nichols.
Energy Technology Data Exchange (ETDEWEB)
Wang, Cheng-Der, E-mail: jdwang@iner.gov.tw [Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan, ROC (China); Lin, Chaung [National Tsing Hua University, Department of Engineering and System Science, 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan (China)
2013-02-15
Highlights: ► The PSO algorithm was adopted to automatically design a BWR CRP. ► The local search procedure was added to improve the result of PSO algorithm. ► The results show that the obtained CRP is the same good as that in the previous work. -- Abstract: This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained.
Energy Technology Data Exchange (ETDEWEB)
Redmond, J. [Sandia National Labs., Albuquerque, NM (United States); Parker, G. [State Univ. of New York, Buffalo, NY (United States)
1993-07-01
This paper examines the role of the control objective and the control time in determining fuel-optimal actuator placement for structural vibration suppression. A general theory is developed that can be easily extended to include alternative performance metrics such as energy and time-optimal control. The performance metric defines a convex admissible control set which leads to a max-min optimization problem expressing optimal location as a function of initial conditions and control time. A solution procedure based on a nested Genetic Algorithm is presented and applied to an example problem. Results indicate that the optimal locations vary widely as a function of control time and initial conditions.
Multi-objective optimization based on Genetic Algorithm for PID controller tuning
Institute of Scientific and Technical Information of China (English)
WANG Guo-liang; YAN Wei-wu; SHAO Hui-he
2009-01-01
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.
Design and Implementation of an Optimal Fuzzy Logic Controller Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
S. Khan
2008-01-01
Full Text Available All control systems suffer from problems related to undesirable overshoot, longer settling times and vibrations while going form one state to another state. Most of relevant techniques had been in the form of suggesting modification and improvement in the instrumentation or interfacing part of the control system and the results reported, remain suffering from shortcomings related to hardware parameter dependence and maintenance and operational complexities. Present study was based on a software approach which was focusing on an algorithmic approach for programming a PIC16F877A microcontroller, for eliminating altogether the parametric dependence issues while adding the benefits of easier modification to suit a given control system to changing operational conditions. Said approach was first simulated using MATLAB/SIMULINK using the techniques of Proportional Derivative Fuzzy Logic Controller (PD-FLC whose membership function, fuzzy logic rules and scaling gains were optimized by the genetic algorithm technique. Simulated results were verified by programming the PIC16F877A microcontroller with the algorithm and using it on a temperature control system where a fan was regulated in response to variations in the ambient system temperature. Resulting tabulated performance indices showed a considerable improvement in rising and settling time besides reducing overshoot and steady state error.
Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid
Ahn, Changsun; Li, Chiao-Ting; Peng, Huei
Electrified vehicles (EV) and renewable power sources are two important technologies for sustainable ground transportation. If left unmitigated, the additional electric load could over-burden the electric grid. Meanwhile, a challenge for integrating renewable power sources into the grid lies in the fact their intermittency requires more regulation services which makes them expensive to deploy. Fortunately, EVs are controllable loads and the charging process can be interrupted. This flexibility makes it possible to manipulate EV charging to reduce the additional electric load and accommodate the intermittency of renewable power sources. To illustrate this potential, a two-level optimal charging algorithm is designed, which achieves both load shifting and frequency regulation. Load shifting can be realized through coordination of power generation and vehicle charging while reducing power generation cost and carbon dioxide emissions. To ensure practicality, a decentralized charging algorithm for load shifting is formulated by emulating the charging pattern identified through linear programming optimization solutions. The frequency regulation is also designed based on frequency droop that can be implemented in a decentralized way. The two control objectives can be integrated because they are functionally separated by time scale. Simulation results are presented to demonstrate the performance of the proposed decentralized algorithm.
Institute of Scientific and Technical Information of China (English)
贺建军; 喻寿益; 钟掘
2003-01-01
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.
Controlling chaos with weak periodic signals optimized by a genetic algorithm.
Soong, C Y; Huang, W T; Lin, F P; Tzeng, P Y
2004-01-01
In the present study we develop a relatively novel and effective chaos control approach with a multimode periodic disturbance applied as a control signal and perform an in-depth analysis on this nonfeedback chaos control strategy. Different from previous chaos control schemes, the present method is of two characteristic features: (1) the parameters of the controlling signal are optimized by a genetic algorithm (GA) with the largest Lyapunov exponent used as an index of the stability, and (2) the optimization is justified by a fitness function defined with the target Lyapunov exponent and the controlling power. This novel method is then tested on the noted Rössler and Lorenz systems with and without the presence of noise. The results disclosed that, compared to the existing chaos control methods, the present GA-based control needs only significantly reduced signal power and a shorter transient stage to achieve the preset control goal. The switching control ability and the robustness of the proposed method for cases with sudden change in a system parameter and/or with the presence of noise environment are also demonstrated.
Nature-inspired optimization algorithms
Yang, Xin-She
2014-01-01
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
Directory of Open Access Journals (Sweden)
Xing Wu
2011-07-01
Full Text Available This paper presents a multi-objective genetic algorithm (MOGA with Pareto optimality and elitist tactics for the control system design of automated guided vehicle (AGV. The MOGA is used to identify AGV driving system model and optimize its servo control system sequentially. In system identification, the model identified by least square method is adopted as an evolution tutor who selects the individuals having balanced performances in all objectives as elitists. In controller optimization, the velocity regulating capability required by AGV path tracking is employed as decision-making preferences which select Pareto optimal solutions as elitists. According to different objectives and elitist tactics, several sub-populations are constructed and they evolve concurrently by using independent reproduction, neighborhood mutation and heuristic crossover. The lossless finite precision method and the multi-objective normalized increment distance are proposed to keep the population diversity with a low computational complexity. Experiment results show that the cascaded MOGA have the capability to make the system model consistent with AGV driving system both in amplitude and phase, and to make its servo control system satisfy the requirements on dynamic performance and steady-state accuracy in AGV path tracking.
Zhang Fengjiao; Wei Minxiang
2015-01-01
Optimization of the control strategy plays an important role in improving the performance of electric vehicles. In order to improve the braking stability and recover the braking energy, a multi-objective genetic algorithm is applied to optimize the key parameters in the control strategy of electric vehicle electro-hydraulic composite braking system. Various limitations are considered in the optimization process, and the optimization results are verified by a software simulation platform of el...
An Inner Convex Approximation Algorithm for BMI Optimization and Applications in Control
Dinh, Quoc Tran; Diehl, Moritz
2012-01-01
In this work, we propose a new local optimization method to solve a class of nonconvex semidefinite programming (SDP) problems. The basic idea is to approximate the feasible set of the nonconvex SDP problem by inner positive semidefinite convex approximations via a parameterization technique. This leads to an iterative procedure to search a local optimum of the nonconvex problem. The convergence of the algorithm is analyzed under mild assumptions. Applications in static output feedback control are benchmarked and numerical tests are implemented based on the data from the COMPLeib library.
Sarjaš, Andrej; Chowdhury, Amor; Svečko, Rajko
2016-09-01
This paper presents the synthesis of an optimal robust controller design using the polynomial pole placement technique and multi-criteria optimisation procedure via an evolutionary computation algorithm - differential evolution. The main idea of the design is to provide a reliable fixed-order robust controller structure and an efficient closed-loop performance with a preselected nominally characteristic polynomial. The multi-criteria objective functions have quasi-convex properties that significantly improve convergence and the regularity of the optimal/sub-optimal solution. The fundamental aim of the proposed design is to optimise those quasi-convex functions with fixed closed-loop characteristic polynomials, the properties of which are unrelated and hard to present within formal algebraic frameworks. The objective functions are derived from different closed-loop criteria, such as robustness with metric ?∞, time performance indexes, controller structures, stability properties, etc. Finally, the design results from the example verify the efficiency of the controller design and also indicate broader possibilities for different optimisation criteria and control structures.
Optimization of PID Parameter In Control System Tuning With Multi-Objective Genetic Algorithm.
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Md Amanullah
2014-05-01
Full Text Available Way of playing advancement is the out-standing design of the study of PID control and frequently research work has been guided for this aspiration. The Proportional plus Integral plus Derivative (PID, controllers are most sweepingly used in control theory as well as industrial plants owing to their ease of execution and sturdiness way of playing. The aspiration of this deed representation capable and apace tuning approach using Genetic Algorithm (GA to obtain the optimized criterion of the PID controller so as to acquire the essential appearance designation of the technique below meditation. The make perfect achievement about multiple plants have in relation to the established tuning approach, to consider the ability of intended approach. Mostly, the whole system’s performance powerfully depends on the controller’s proficiency and thus the tuning technique plays a key part in the system’s behavior.
APPLICATION OF NEWTON RAPHSON ALGORITHM FOR OPTIMIZING TRANSMISSION CONTROL PROTOCOL PERFORMANCE
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J. Viji Priya
2013-01-01
Full Text Available Wireless networks are growing rapidly. TCP is the most widely-used protocol on Internet and so optimizing TCP performance is very important for fast efficient data transfer. The different existing TCP variants and solutions they have not been analyzed together to identify the bottlenecks in wireless networks. TCP has a major problem in its congestion control algorithm which does not allow the flow to achieve the full available bandwidth on fast long-distance links. This problem has been studied in this study using a new high speed congestion control TCP protocol based on the Newton Raphson algorithm This study further analyses involving six TCP performance evaluation constraints namely, TCP full bandwidth utilization, throughput, packet loss rate, fairness in sharing bandwidth, friendliness in short-RTT and long-RTT and these constraints are used to evaluate the proposed Newton Raphson Congestion Control (NRC-TCP performance. This study shows that the proposed algorithm performs better compared with the other methods of application.
Directory of Open Access Journals (Sweden)
Xinhua Liu
2017-07-01
Full Text Available In order to improve the response performance of a proportion integration differentiation (PID controller for magnetorheological fluids (MRF brake and to reduce the braking fluctuation rate, an improved fruit fly optimization algorithm for PID controller parameters tuning of MRF brake is proposed. A data acquisition system for MRF brake is designed and the transfer function of MRF brake is identified. Moreover, an improved fruit fly optimization algorithm (IFOA through integration of PID control strategy and cloud model algorithm is proposed to design a PID controller for MRF brake. Finally, the simulation and experiment are carried out. The results show that IFOA, with a faster response output and no overshoot, is superior to the conventional PID and fruit fly optimization algorithm (FOA PID controller.
Balandin, DV; Kogan, MM
2004-01-01
An algorithm for checking feasibility of the robust H-infinity-control problem for systems with time-varying norm bounded uncertainty is suggested. This algorithm is an iterative procedure on each step of which an optimization problem for a linear function under convex constraints determined by LMIs
Directory of Open Access Journals (Sweden)
Wei Zhang
2012-01-01
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.
Optimization of LMS Algorithm for System Identification
Prasad, Saurabh R.; Godbole, Bhalchandra B.
2017-01-01
An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical scenario. So most feasible choice of the adaptive filtering algorithm is the LMS algorithm includin...
Optimal control of FES-assisted standing up in paraplegia using genetic algorithms.
Davoodi, R; Andrews, B J
1999-11-01
A practical system for Functional Electrical Stimulation (FES) assisted standing up in paraplegia should involve only a minimum of manual set up and tuning. An improved tuning method, using a genetic algorithm (GA) is proposed and demonstrated using computer simulation. Specifically, the GA adjusts the parameters of fuzzy logic (FL) and gain-scheduling proportional integral derivative (GS-PID) controllers that electrically stimulate the hip and knee musculature during the sit-stand maneuver. These new GA designed controllers were found to be effective in coordinating volitional and FES control according to formulated criteria. The latter was based on the deviations from a desired trajectory of the knee and hip joints and the magnitude of the voluntary upper body forces. The magnitude of the average arm forces were slightly higher when compared with the open-loop maximal stimulation of the hip and knee musculature; however, the terminal knee velocities were significantly reduced to less than 10 degrees /s. For practical implementation, the number of trials required to optimize the FL and GS-PID controllers can be reduced by a proposed pre-training procedure using a computer model scaled to the individual. The GA designed controllers remain near optimal provided the model-subject mismatch is small.
Directory of Open Access Journals (Sweden)
S. Chaine
2017-06-01
Full Text Available Controllers in some power system problems are required to satisfy different performance objectives, which could be conflicting with one another. Therefore in the process of their gain tuning, when the problem is formulated within an optimization framework, it becomes necessary to achieve multiple objectives with a method multi-objective optimization method. This work presents a new method of multi-objective optimization method to optimize several controller parameters. The problem deals with optimization of controllers of doubly fed induction generator modeled for frequency regulation in an interconnected two-area wind power integrated thermal power system. The gains of integral controller of automatic generation control loop and the proportional and derivative controllers of doubly fed induction generator inertial control loop are optimized in a coordinated manner by employing a multi-objective non-dominated sorting based Cuckoo search algorithm. The algorithm is formed by synthesizing the parallel searching abilities of Cuckoo search algorithm (CSA with the non dominated sorting methodology adopted in Non dominated sorting genetic algorithm (NSGA-II. Based on the set of selected instances, the algorithm termed as non dominated sorting Cuckoo search (NSCS, exhibits better efficiency of optimization compared to the NSGA-II, CSA, genetic algorithm, and particle swarm optimization. The performance of the designed controller is further compared with the performance obtained with a modified version of NSCS, which includes the method of archiving in it. The designed set of controllers perform robustly even with the variation in disturbances, parameter and operating conditions in the system.
Salcedo-Sanz, S.; Camacho-Gómez, C.; Magdaleno, A.; Pereira, E.; Lorenzana, A.
2017-04-01
In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.
Optimal congestion control algorithm for ad hoc networks: Penalty function-based approach
Institute of Scientific and Technical Information of China (English)
XU Wet-qiang; WU Tie-jun
2006-01-01
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.
Directory of Open Access Journals (Sweden)
Jianjun He
2015-01-01
Full Text Available A hybrid optimization strategy for integrated topological optimization design of piezoelectric cylindrical flat shell structure is proposed. The method combines the genetic algorithm (GA and linear-quadratic-regulator (LQR theory to optimize the performance of coupling structure/control system. The GA is used to choose the optimal structure topology and number and placements of actuators and control parameters; meanwhile, the LQR is used to design control system to suppress vibration of optimal structure under sinusoidal excitation, which is based on the couple-mode space control. In addition, the mathematical morphology operators are used for repairs of disconnected structure topology. The results of numerical simulation and computations show that the proposed method is effective and feasible, with good performance for the optimal and coupling piezoelectric cylindrical shell structure/control system.
ORACLS- OPTIMAL REGULATOR ALGORITHMS FOR THE CONTROL OF LINEAR SYSTEMS (CDC VERSION)
Armstrong, E. S.
1994-01-01
This control theory design package, called Optimal Regulator Algorithms for the Control of Linear Systems (ORACLS), was developed to aid in the design of controllers and optimal filters for systems which can be modeled by linear, time-invariant differential and difference equations. Optimal linear quadratic regulator theory, currently referred to as the Linear-Quadratic-Gaussian (LQG) problem, has become the most widely accepted method of determining optimal control policy. Within this theory, the infinite duration time-invariant problems, which lead to constant gain feedback control laws and constant Kalman-Bucy filter gains for reconstruction of the system state, exhibit high tractability and potential ease of implementation. A variety of new and efficient methods in the field of numerical linear algebra have been combined into the ORACLS program, which provides for the solution to time-invariant continuous or discrete LQG problems. The ORACLS package is particularly attractive to the control system designer because it provides a rigorous tool for dealing with multi-input and multi-output dynamic systems in both continuous and discrete form. The ORACLS programming system is a collection of subroutines which can be used to formulate, manipulate, and solve various LQG design problems. The ORACLS program is constructed in a manner which permits the user to maintain considerable flexibility at each operational state. This flexibility is accomplished by providing primary operations, analysis of linear time-invariant systems, and control synthesis based on LQG methodology. The input-output routines handle the reading and writing of numerical matrices, printing heading information, and accumulating output information. The basic vector-matrix operations include addition, subtraction, multiplication, equation, norm construction, tracing, transposition, scaling, juxtaposition, and construction of null and identity matrices. The analysis routines provide for the following
ORACLS- OPTIMAL REGULATOR ALGORITHMS FOR THE CONTROL OF LINEAR SYSTEMS (DEC VAX VERSION)
Frisch, H.
1994-01-01
This control theory design package, called Optimal Regulator Algorithms for the Control of Linear Systems (ORACLS), was developed to aid in the design of controllers and optimal filters for systems which can be modeled by linear, time-invariant differential and difference equations. Optimal linear quadratic regulator theory, currently referred to as the Linear-Quadratic-Gaussian (LQG) problem, has become the most widely accepted method of determining optimal control policy. Within this theory, the infinite duration time-invariant problems, which lead to constant gain feedback control laws and constant Kalman-Bucy filter gains for reconstruction of the system state, exhibit high tractability and potential ease of implementation. A variety of new and efficient methods in the field of numerical linear algebra have been combined into the ORACLS program, which provides for the solution to time-invariant continuous or discrete LQG problems. The ORACLS package is particularly attractive to the control system designer because it provides a rigorous tool for dealing with multi-input and multi-output dynamic systems in both continuous and discrete form. The ORACLS programming system is a collection of subroutines which can be used to formulate, manipulate, and solve various LQG design problems. The ORACLS program is constructed in a manner which permits the user to maintain considerable flexibility at each operational state. This flexibility is accomplished by providing primary operations, analysis of linear time-invariant systems, and control synthesis based on LQG methodology. The input-output routines handle the reading and writing of numerical matrices, printing heading information, and accumulating output information. The basic vector-matrix operations include addition, subtraction, multiplication, equation, norm construction, tracing, transposition, scaling, juxtaposition, and construction of null and identity matrices. The analysis routines provide for the following
Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour
2012-09-01
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.
Huang, Biqing; Li, Chenghai; Tao, Fei
2014-07-01
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).
Optimal Register Allocation by Augmented Left-Edge Algorithm on Arbitrary Control-Flow Structures
DEFF Research Database (Denmark)
Ruvald Pedersen, Mark; Madsen, Jan
2012-01-01
A new algorithm for optimal register allocation in context of high-level synthesis is presented. In this paper we show how the greedy left-edge algorithm can be leveraged to obtain a globally optimal allocation, that is computed in polynomial time. By splitting variables at block boundaries, allows...... for allocation to be done using only quasi-local and local allocation - avoiding the complexity of true global allocation. As local allocation is much simpler than global allocation, this approach emphasizes efficiency and ease of implementation - at a cost of an increased number of register transfers compared...
Energy Technology Data Exchange (ETDEWEB)
Cao, Cuiwen; Zhang, Yakun; Gu, Xingsheng [Ministry of Education, East China Univ. of Science and Technology, Shanghai (China). Key Lab. of Advanced Control and Optimization for Chemical Processes
2013-07-01
Optimizing operation parameters for Texaco coal-water slurry gasifier with the consideration of multiple objectives is a complicated nonlinear constrained problem concerning 3 BP neural networks. In this paper, multi-objective 3-layer mixed cultural differential evolution (MO-3LM-CDE) algorithms which comprise of 4 multi-objective strategies and a 3LM-CDE algorithm are firstly presented. Then they are tested in 6 benchmark functions. Finally, the MO-3LM-CDE algorithms are applied to optimize 3 control parameters of the Texaco coal-water slurry gasifier in methanol production of a real-world chemical plant. The simulation results show that multi-objective optimal results are better than the respective single-objective optimal operations.
Directory of Open Access Journals (Sweden)
M. Joorabian
2014-07-01
Full Text Available The Seeker Optimization Algorithm (SOA, as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes SOA based Load Frequency Control (LFC for the suppression of oscillations in power system. A two area system and four area system deregulated is considered to be equipped with proportional plus integral (PI controllers. SOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by Particle Swarm Optimization (PSO and Bacterial Foraging Algorithm (BFA in order to demonstrate the superior efficiency of the proposed SOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on SOA in compare to optimized PI controller based on PSO and BFA and conventional one over wide range of operating conditions, and system parameters variations.
Directory of Open Access Journals (Sweden)
Zhang Fengjiao
2015-03-01
Full Text Available Optimization of the control strategy plays an important role in improving the performance of electric vehicles. In order to improve the braking stability and recover the braking energy, a multi-objective genetic algorithm is applied to optimize the key parameters in the control strategy of electric vehicle electro-hydraulic composite braking system. Various limitations are considered in the optimization process, and the optimization results are verified by a software simulation platform of electric vehicle regenerative braking system in typical brake conditions. The results show that optimization objectives achieved a good astringency, and the optimized control strategy can increase the brake energy recovery effectively under the condition of ensuring the braking stability.
Directory of Open Access Journals (Sweden)
Rabindra Kumar Sahu
2016-03-01
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.
Ant colony system algorithm for the optimization of beer fermentation control
Institute of Scientific and Technical Information of China (English)
肖杰; 周泽魁; 张光新
2004-01-01
Beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. Optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.
An Optimal Flow Control Algorithm for Real-time Traffic over the Internet
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
According to the Wide Area Network model and queue dynamics in the router, the authors formulate the Internet flow control as a constrained convex programming problem, where the objective is to maximize the total utility and minimize transmission delay and delay jitter of all sources over their transmission rates. Based on this formulation, flow control can be solved by means of a gradient projection algorithm with properly rate iterations. The main difficulty facing the realization of the iteration algorithm is the distributed computation of the congestion measure. Fortunately, Explicit Congestion Notification (ECN) is likely to be used to improving the performance of TCP in the near future. By using ECN, it is possible to realize the iteration algorithm in IP networks. The algorithm is divided into two parts, algorithms in the router and the source. The main advantage of the scheme is its fast convergence ability and robustness, but small queue length fluctuation is unavoidable when the number of users increases.
Directory of Open Access Journals (Sweden)
L. I. Rozonoer
1999-01-01
Full Text Available Necessary and sufficient conditions for existence of optimal control for all initial data are proved for LQ-optimization problem. If these conditions are fulfilled, necessary and sufficient conditions of optimality are formulated. Basing on the results, some general hypotheses on optimal control in terms of Pontryagin's maximum condition and Bellman's equation are proposed.
Directory of Open Access Journals (Sweden)
DAHIYA, P.
2015-05-01
Full Text Available This paper presents the application of hybrid opposition based disruption operator in gravitational search algorithm (DOGSA to solve automatic generation control (AGC problem of four area hydro-thermal-gas interconnected power system. The proposed DOGSA approach combines the advantages of opposition based learning which enhances the speed of convergence and disruption operator which has the ability to further explore and exploit the search space of standard gravitational search algorithm (GSA. The addition of these two concepts to GSA increases its flexibility for solving the complex optimization problems. This paper addresses the design and performance analysis of DOGSA based proportional integral derivative (PID and fractional order proportional integral derivative (FOPID controllers for automatic generation control problem. The proposed approaches are demonstrated by comparing the results with the standard GSA, opposition learning based GSA (OGSA and disruption based GSA (DGSA. The sensitivity analysis is also carried out to study the robustness of DOGSA tuned controllers in order to accommodate variations in operating load conditions, tie-line synchronizing coefficient, time constants of governor and turbine. Further, the approaches are extended to a more realistic power system model by considering the physical constraints such as thermal turbine generation rate constraint, speed governor dead band and time delay.
Microgenetic optimization algorithm for optimal wavefront shaping
Anderson, Benjamin R; Gunawidjaja, Ray; Eilers, Hergen
2015-01-01
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.
New Optimization Algorithms in Physics
Hartmann, Alexander K
2004-01-01
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.
Directory of Open Access Journals (Sweden)
Sabita Chaine
2015-05-01
Full Text Available This work presents a methodology adopted in order to tune the controller parameters of superconducting magnetic energy storage (SMES system in the automatic generation control (AGC of a two-area thermal power system. The gains of integral controllers of AGC loop, proportional controller of SMES loop and gains of the current feedback loop of the inductor in SMES are optimized simultaneously in order to achieve a desired performance. Recently proposed intelligent technique based algorithm known as Cuckoo search algorithm (CSA is applied for optimization. Sensitivity and robustness of the tuned gains tested at different operating conditions prove the effectiveness of fast acting energy storage devices like SMES in damping out oscillations in power system when their controllers are properly tuned.
Antenna optimization using Particle Swarm Optimization algorithm
Directory of Open Access Journals (Sweden)
Golubović Ružica M.
2006-01-01
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.
Controller tuning based on optimization algorithms of a novel spherical rolling robot
Energy Technology Data Exchange (ETDEWEB)
Sadegjian, Rasou [Dept. of Electrical, Biomedical, and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, QazvinI (Iran, Islamic Republic of); Masouleh, Mehdi Tale [Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran (Iran, Islamic Republic of)
2016-11-15
This study presents the construction process of a novel spherical rolling robot and control strategies that are used to improve robot locomotion. The proposed robot drive mechanism is constructed based on a combination of the pendulum and wheel drive mechanisms. The control model of the proposed robot is developed, and the state space model is calculated based on the obtained control model. Two control strategies are defined to improve the synchronization performance of the proposed robot motors. The proportional-derivative and proportional-integral-derivative controllers are designed based on the pole placement method. The proportional-integral-derivative controller leads to a better step response than the proportional-derivative controller. The controller parameters are tuned with genetic and differential evaluation algorithms. The proportional-integral-derivative controller which is tuned based on the differential evaluation algorithm leads to a better step response than the proportional-integral-derivative controller that is tuned based on genetic algorithm. Fuzzy logics are used to reduce the robot drive mechanism motors synchronizing process time to the end of achieving a high-performance controller. The experimental implementation results of fuzzy-proportional-integral-derivative on the proposed spherical rolling robot resulted in a desirable synchronizing performance in a short time.
Ahirwal, M K; Kumar, Anil; Singh, G K
2013-01-01
This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
Research on optimal control, stabilization and computational algorithms for aerospace applications
Athans, M.
1985-01-01
The research carried out in the areas of optimal control and estimation theory and its applications under this grant is reviewed. A listing of the 257 publications that document the research results is presented.
Real-coded genetic algorithm for optimal vibration control of flexible structure
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Presents the study on the optimum location of actuators/sensors for active vibration control in aerospace flexible structures with the performance function first built by maximization of dissipation energy due to control action and a real-coded genetic algorithm then proposed to produce a global-optimum solution, and proves the feasibility and advantages of this algorithm with the example of a standard test function and a two-collocated actuators/sensors cantilever, and comparing the results with those given in the literatures.
A UAV routing and sensor control optimization algorithm for target search
Collins, Gaemus E.; Riehl, James R.; Vegdahl, Philip S.
2007-04-01
An important problem in unmanned air vehicle (UAV) and UAV-mounted sensor control is the target search problem: locating target(s) in minimum time. Current methods solve the optimization of UAV routing control and sensor management independently. While this decoupled approach makes the target search problem computationally tractable, it is suboptimal. In this paper, we explore the target search and classification problems by formulating and solving a joint UAV routing and sensor control optimization problem. The routing problem is solved on a graph using receding horizon optimal control. The graph is dynamically adjusted based on the target probability distribution function (PDF). The objective function for the routing optimization is the solution of a sensor control optimization problem. An optimal sensor schedule (in the sense of maximizing the viewed target probability mass) is constructed for each candidate flight path in the routing control problem. The PDF of the target state is represented with a particle filter and an "occupancy map" for any undiscovered targets. The tradeoff between searching for undiscovered targets and locating tracks is handled automatically and dynamically by the use of an appropriate objective function. In particular, the objective function is based on the expected amount of target probability mass to be viewed.
Directory of Open Access Journals (Sweden)
Jie-Sheng Wang
2014-02-01
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.
Directory of Open Access Journals (Sweden)
Fatemeh Masoudnia
2013-11-01
Full Text Available In this paper three optimum approaches to design PID controller for a Gryphon Robot are presented. The three applied approaches are Artificial Bee Colony, Shuffled Frog Leaping algorithms and nero-fuzzy system. The design goal is to minimize the integral absolute error and reduce transient response by minimizing overshoot, settling time and rise time of step response. An Objective function of these indexes is defined and minimized applying Shuffled Frog Leaping (SFL algorithm, Artificial Bee Colony (ABC algorithm and Nero-Fuzzy System (FNN. After optimization of the objective function, the optimal parameters for the PID controller are adjusted. Simulation results show that FNN has a remarkable effect on decreasing the amount of settling time and rise-time and eliminating of steady-state error while the SFL algorithm performs better on steady-state error and the ABC algorithm is better on decreasing of overshoot. In steady state manner, all of the methods react robustly to the disturbance, but FNN shows more stability in transient response.
Optimal Control of Population Transfer in Three-Level A System with Genetic Algorithms
Institute of Scientific and Technical Information of China (English)
ZHANG Xiang-Yun; SUN Zhen-Rong; CHEN Guo-Liang; WANG Zu-Geng; XU Zhi-Zhan; LI Ru-Xin
2004-01-01
Population transfer in a three-level A system is simulated numerically and optimized. Almost complete population transfer from |1〉 to |3〉 is achieved by a genetic algorithm while the population in state |2〉 reached minimum over the entire evolution at the same time. The result shows that the optimal pulse sequence is the well-known stimulated Raman adiabatic passage (STIRAP) scheme. The detuning of pump pulse and Stokes pulse △p and △s with the opposite sign and the chirps χp and χs with the same sign are in favour of the complete and robust population transfer for few-cycle laser pulse. Rabi frequencies Ωp and Ωs have insensitive effects on the complete population transfer during a large scope of their ratio when they are large enough.
Integrated controls design optimization
Lou, Xinsheng; Neuschaefer, Carl H.
2015-09-01
A control system (207) for optimizing a chemical looping process of a power plant includes an optimizer (420), an income algorithm (230) and a cost algorithm (225) and a chemical looping process models. The process models are used to predict the process outputs from process input variables. Some of the process in puts and output variables are related to the income of the plant; and some others are related to the cost of the plant operations. The income algorithm (230) provides an income input to the optimizer (420) based on a plurality of input parameters (215) of the power plant. The cost algorithm (225) provides a cost input to the optimizer (420) based on a plurality of output parameters (220) of the power plant. The optimizer (420) determines an optimized operating parameter solution based on at least one of the income input and the cost input, and supplies the optimized operating parameter solution to the power plant.
Directory of Open Access Journals (Sweden)
Shuai Zeng
2013-01-01
Full Text Available With the development of wireless technologies, mobile communication applies more and more extensively in the various walks of life. The social network of both fixed and mobile users can be seen as networked agent system. At present, kinds of devices and access network technology are widely used. Different users in this networked agent system may need different coding rates multimedia data due to their heterogeneous demand. This paper proposes a distributed flow rate control algorithm to optimize multimedia data transmission of the networked agent system with the coexisting various coding rates. In this proposed algorithm, transmission path and upload bandwidth of different coding rate data between source node, fixed and mobile nodes are appropriately arranged and controlled. On the one hand, this algorithm can provide user nodes with differentiated coding rate data and corresponding flow rate. On the other hand, it makes the different coding rate data and user nodes networked, which realizes the sharing of upload bandwidth of user nodes which require different coding rate data. The study conducts mathematical modeling on the proposed algorithm and compares the system that adopts the proposed algorithm with the existing system based on the simulation experiment and mathematical analysis. The results show that the system that adopts the proposed algorithm achieves higher upload bandwidth utilization of user nodes and lower upload bandwidth consumption of source node.
Optimal Mixing Evolutionary Algorithms
Thierens, D.; Bosman, P.A.N.; Krasnogor, N.
2011-01-01
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
A novel hybrid optimization algorithm for diferential-algebraic control problems
Directory of Open Access Journals (Sweden)
F. S. Lobato
2007-09-01
Full Text Available Dynamic optimization problems can be numerically solved by direct, indirect and Hamilton-Jacobi-Bellman methods. In this paper, the differential-algebraic approach is incorporated into a hybrid method, extending the concepts of structural and differential indexes, consistent initialization analysis, index reduction and dynamic degrees of freedom to the optimal control problem. The resultant differential-algebraic optimal control problem is solved in the following steps: transformation of the original problem into a standard nonlinear programming problem that provides control and state variables, switching time estimates and costate variables profiles with the DIRCOL code; definition of the switching function and the automatically generated sequence of index-1 differential-algebraic boundary value problems from Pontryagin’s minimum principle by using the developed Otima code; and finally, application of the COLDAE code with the results of the direct method as an initial guess. The proposed hybrid method is illustrated with a pressure-constrained batch reactor optimization problem associated with the slack variable method.
Optimizing models for production and inventory control using a genetic algorithm
Directory of Open Access Journals (Sweden)
Dragan S. Pamučar
2012-01-01
Full Text Available In order to make the Economic Production Quantity (EPQ model more applicable to real-world production and inventory control problems, in this paper we expand this model by assuming that some imperfect items of different product types being produced such as reworks are allowed. In addition, we may have more than one product and supplier along with warehouse space and budget limitation. We show that the model of the problem is a constrained non-linear integer program and propose a genetic algorithm to solve it. Moreover, a design of experiments is employed to calibrate the parameters of the algorithm for different problem sizes. In the end, a numerical example is presented to demonstrate the application of the proposed methodology.
Shan, Bonan; Wang, Jiang; Deng, Bin; Wei, Xile; Yu, Haitao; Zhang, Zhen; Li, Huiyan
2016-07-01
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.
Directory of Open Access Journals (Sweden)
Ruidan Su
2014-01-01
Full Text Available A parallel multipopulation genetic algorithm (PMPGA is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA; the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train.
Institute of Scientific and Technical Information of China (English)
SUN Fan; ZHONG Weimin; CHENG Hui; QIAN Feng
2013-01-01
Two general approaches are adopted in solving dynamic optimization problems in chemical processes,namely,the analytical and numerical methods.The numerical method,which is based on heuristic algorithms,has been widely used.An approach that combines differential evolution (DE) algorithm and control vector parameterization (CVP) is proposed in this paper.In the proposed CVP,control variables are approximated with polynomials based on state variables and time in the entire time interval.Region reduction strategy is used in DE to reduce the width of the search region,which improves the computing efficiency.The results of the case studies demonstrate the feasibility and efficiency of the proposed methods.
Genetic algorithm optimization of entanglement
Navarro-Munoz, J C; Rosu, H C; Navarro-Munoz, Jorge C.
2006-01-01
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
Institute of Scientific and Technical Information of China (English)
陈洲; 佟秋男; 张丛丛; 胡湛
2015-01-01
Identification of acetone and its two isomers, and the control of their ionization and dissociation processes are per-formed using a dual-mass-spectrometer scheme. The scheme employs two sets of time of flight mass spectrometers to simultaneously acquire the mass spectra of two different molecules under the irradiation of identically shaped femtosecond laser pulses. The optimal laser pulses are found using closed-loop learning method based on a genetic algorithm. Com-pared with the mass spectra of the two isomers that are obtained with the transform limited pulse, those obtained under the irradiation of the optimal laser pulse show large differences and the various reaction pathways of the two molecules are selectively controlled. The experimental results demonstrate that the scheme is quite effective and useful in studies of two molecules having common mass peaks, which makes a traditional single mass spectrometer unfeasible.
Aschepkov, Leonid T; Kim, Taekyun; Agarwal, Ravi P
2016-01-01
This book is based on lectures from a one-year course at the Far Eastern Federal University (Vladivostok, Russia) as well as on workshops on optimal control offered to students at various mathematical departments at the university level. The main themes of the theory of linear and nonlinear systems are considered, including the basic problem of establishing the necessary and sufficient conditions of optimal processes. In the first part of the course, the theory of linear control systems is constructed on the basis of the separation theorem and the concept of a reachability set. The authors prove the closure of a reachability set in the class of piecewise continuous controls, and the problems of controllability, observability, identification, performance and terminal control are also considered. The second part of the course is devoted to nonlinear control systems. Using the method of variations and the Lagrange multipliers rule of nonlinear problems, the authors prove the Pontryagin maximum principle for prob...
National Research Council Canada - National Science Library
Ting Hou; Liping Zhang; Yuchen Chen
2014-01-01
.... In this paper, a kind of fuzzy self-optimizing control based on differential evolution algorithm is proposed, which applied in the power plant boiler system, the boiler combustion efficiency has been...
Energy Technology Data Exchange (ETDEWEB)
Soong, C.Y. [Department of Aerospace and Systems Engineering, Feng Chia University, Seatwen, Taichung 40724, Taiwan (China)]. E-mail: cysoong@fcu.edu.tw; Huang, W.T. [Department of Marine Mechanical Engineering, Chinese Naval Academy, Kaohsiung 81300, Taiwan (China); Lin, F.P. [Grid Computing Division, National Center of High Performance Computing, Hsinchu 30012, Taiwan (China)
2007-12-15
Recently, we proposed a chaos control strategy with weak Fourier signals optimized by using a genetic algorithm (GA) and demonstrated its merits in controlling Lorenz and Roessler systems (Physical Review E, 2004). In this continuation work, performance of various types of signals, namely periodic continuous, periodic discrete, and constant bias (non-periodic), applied to an autonomous (Roessler) system and a non-autonomous (Murali-Lakshmanan-Chua, MLC) system are investigated. An index of relative robustness is proposed for measuring the noise-resisting ability of the control signals. The results reveal that the constant signal has the strongest noise-resisting ability, the periodic pulse signal has the weakest, and the Fourier signal falls in between. Phase modulation generally shortens the transient time period and is additionally beneficial to non-autonomous systems in minimizing significantly the signal power. By searching with the present GA-optimization, it is demonstrated that the minimum-power signal for controlling the non-autonomous (MLC) system is the signal with a frequency exactly the same as that of the system forcing but with phase modulation. The effectiveness of the GA-optimized signals of extremely low power employed in alternatively switching control of non-autonomous systems is also demonstrated.
Ezra, Elishai; Maor, Idan; Bavli, Danny; Shalom, Itai; Levy, Gahl; Prill, Sebastian; Jaeger, Magnus S; Nahmias, Yaakov
2015-08-01
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.
Model and Algorithm for the Optimal Controlled Partitioning of Power Systems
Institute of Scientific and Technical Information of China (English)
LIN Jikeng; LI Shengwen; WU Peng; WANG Xudong; SHAO Guanghui; XU Xingwei; MA Xin
2012-01-01
In China, with the development of projects such as ＂electricity transmission from the West to the East＂ and ＂power exchange between the South and the North＂, and with the UHV project being put into operation, a nation-wide interconnection system has been formed. For such big interconnection system, local faults or disturbances might lead to large-scale power blackouts and even system collapses, which will cause direct and indirect losses comparable to a big natural disaster. By taking proper and reasonable controlled partition measures, the risk of long-period and large-area power failure and even system collapse will be greatly reduced. However, with the system size increasing, the number of partition interface will grow geometrically, and therefore, it is a great challenge for a nation-wide interconnection system to achieve the optimal partition surface.
Zhang, Huaguang; Wei, Qinglai; Luo, Yanhong
2008-08-01
In this paper, we aim to solve the infinite-time optimal tracking control problem for a class of discrete-time nonlinear systems using the greedy heuristic dynamic programming (HDP) iteration algorithm. A new type of performance index is defined because the existing performance indexes are very difficult in solving this kind of tracking problem, if not impossible. Via system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then, the greedy HDP iteration algorithm is introduced to deal with the regulation problem with rigorous convergence analysis. Three neural networks are used to approximate the performance index, compute the optimal control policy, and model the nonlinear system for facilitating the implementation of the greedy HDP iteration algorithm. An example is given to demonstrate the validity of the proposed optimal tracking control scheme.
Genetic Searching Algorithm for Optimal Runlength—Limited Codes with Error Control
Institute of Scientific and Technical Information of China (English)
RenQingsheng; YeZhongxing
1997-01-01
A genetic searching algorithm is presented to construct arbitrarily concatenatable block code with runlength(d,k)constraints.The code also has the ability to correct error during decoding.A similar eliminating operator and an anti-symbiotic operator are suggested to improve the efficiency of the algorithm.
Saavedra, Juan Alejandro
Quality Control (QC) and Quality Assurance (QA) strategies vary significantly across industries in the manufacturing sector depending on the product being built. Such strategies range from simple statistical analysis and process controls, decision-making process of reworking, repairing, or scraping defective product. This study proposes an optimal QC methodology in order to include rework stations during the manufacturing process by identifying the amount and location of these workstations. The factors that are considered to optimize these stations are cost, cycle time, reworkability and rework benefit. The goal is to minimize the cost and cycle time of the process, but increase the reworkability and rework benefit. The specific objectives of this study are: (1) to propose a cost estimation model that includes energy consumption, and (2) to propose an optimal QC methodology to identify quantity and location of rework workstations. The cost estimation model includes energy consumption as part of the product direct cost. The cost estimation model developed allows the user to calculate product direct cost as the quality sigma level of the process changes. This provides a benefit because a complete cost estimation calculation does not need to be performed every time the processes yield changes. This cost estimation model is then used for the QC strategy optimization process. In order to propose a methodology that provides an optimal QC strategy, the possible factors that affect QC were evaluated. A screening Design of Experiments (DOE) was performed on seven initial factors and identified 3 significant factors. It reflected that one response variable was not required for the optimization process. A full factorial DOE was estimated in order to verify the significant factors obtained previously. The QC strategy optimization is performed through a Genetic Algorithm (GA) which allows the evaluation of several solutions in order to obtain feasible optimal solutions. The GA
Ant colony search algorithm for optimal reactive power optimization
Directory of Open Access Journals (Sweden)
Lenin K.
2006-01-01
Full Text Available The paper presents an (ACSA Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical test bus system. The proposed approach is tested and compared to genetic algorithm (GA, Adaptive Genetic Algorithm (AGA.
Combinatorial optimization theory and algorithms
Korte, Bernhard
2002-01-01
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.
Directory of Open Access Journals (Sweden)
Zhao Jing
2015-01-01
Full Text Available This paper proposes a strategy of a new optimal sliding-mode control for flight control system with state constraints so that the system guarantees the optimal performance index. Besides, the strategy ensures strong robustness to the internal parametric uncertainty and the external disturbances. In order to have fast transient response speed as well as good tracking accuracy, the integral of the time multiplied by the absolute displacement tracking error is introduced as the performance index. By analyzing the state constraints which are specifically the velocity tracking error constraint and the acceleration tracking error constraint, and the performance index, the parameters of sliding-mode surface and control law are obtained. Finally, the authors conduct the semi physical simulation on Qball-X4 quad-rotor helicopter, showing the effectiveness of the proposed strategy.
Constrained Multiobjective Biogeography Optimization Algorithm
Directory of Open Access Journals (Sweden)
Hongwei Mo
2014-01-01
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.
Constrained multiobjective biogeography optimization algorithm.
Mo, Hongwei; Xu, Zhidan; Xu, Lifang; Wu, Zhou; Ma, Haiping
2014-01-01
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.
Heterogeneous architecture to process swarm optimization algorithms
Directory of Open Access Journals (Sweden)
Maria A. Dávila-Guzmán
2014-01-01
Full Text Available Since few years ago, the parallel processing has been embedded in personal computers by including co-processing units as the graphics processing units resulting in a heterogeneous platform. This paper presents the implementation of swarm algorithms on this platform to solve several functions from optimization problems, where they highlight their inherent parallel processing and distributed control features. In the swarm algorithms, each individual and dimension problem are parallelized by the granularity of the processing system which also offer low communication latency between individuals through the embedded processing. To evaluate the potential of swarm algorithms on graphics processing units we have implemented two of them: the particle swarm optimization algorithm and the bacterial foraging optimization algorithm. The algorithms’ performance is measured using the acceleration where they are contrasted between a typical sequential processing platform and the NVIDIA GeForce GTX480 heterogeneous platform; the results show that the particle swarm algorithm obtained up to 36.82x and the bacterial foraging swarm algorithm obtained up to 9.26x. Finally, the effect to increase the size of the population is evaluated where we show both the dispersion and the quality of the solutions are decreased despite of high acceleration performance since the initial distribution of the individuals can converge to local optimal solution.
Optimization of flight control parameters of an aircraft using genetic algorithms
Garcia Aguilar, Sixto Ernesto
Ce travail presente de nouvelles methodes pour ameliorer la performance des algorithmes genetiques pour l'optimisation des gains du controleur d'un systeme de vol electrique des avions commerciaux. Nous avons combine les caracteristiques de deux operateurs de mutation, uniforme et non uniforme, au sein d'un nouvel operateur, de type periodique. Nous avons propose un nouveau modele avec contraintes et nous avons fait la conception d'un nouvel operateur de selection stochastique pour augmenter l'efficacite d'un algorithme genetique du codage reel (RGA) applique aux problemes d'optimisation avec contraintes. Pour la conception de l'operateur de selection sous contraintes, nous avons applique une methode qui peut manipuler la proportion d'individus faisables et non faisables sans negliger le comportement dynamique du GA. Finalement, nous avons reduit de 25% le temps d'execution original et ameliore l'efficacite des RGAs en combinant l'application d'un index de diversite d'une population d'un algorithme genetique ainsi qu'un reseau de Bayes (BN).
2012-03-01
Rao discusses the most common gradient search algorithms in [71] along with some heuristic ones that use some added randomness to direct the search... aircrews and mission planners who have interest in this type of problem. 98 Target T=Threat T x1 x2 B ou nd ar y B ou nd ar y T θ V (tf , x1(tf ), x2(tf...processing. The most valuable product to mission planners and aircrews is not the set of statistics describing expected values, variances, and covariances and
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture models
Genetic Algorithm for Optimization: Preprocessor and Algorithm
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
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.
Evolutionary algorithms for hard quantum control
Zahedinejad, Ehsan; Schirmer, Sophie; Sanders, Barry C.
2014-09-01
Although quantum control typically relies on greedy (local) optimization, traps (irregular critical points) in the control landscape can make optimization hard by foiling local search strategies. We demonstrate the failure of greedy algorithms as well as the (nongreedy) genetic-algorithm method to realize two fast quantum computing gates: a qutrit phase gate and a controlled-not gate. We show that our evolutionary algorithm circumvents the trap to deliver effective quantum control in both instances. Even when greedy algorithms succeed, our evolutionary algorithm can deliver a superior control procedure, for example, reducing the need for high time resolution.
DEFF Research Database (Denmark)
Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard
2015-01-01
compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...
Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen
2006-01-01
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.
Directory of Open Access Journals (Sweden)
Wiktor HUDY
2013-12-01
Full Text Available In this paper, the impact of regulators set and their types for the characteristic of rotational speed of induction motor was researched.. The evolutionary algorithm was used as optimization tool. Results were verified with using MATLAB/Simulink.
Discrete Variational Optimal Control
Jimenez, Fernando; de Diego, David Martin
2012-01-01
This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher-dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical and a practical examples, e.g. the control of an underwater vehicle, will illustrate the application of the proposed approach.
Discrete Variational Optimal Control
Jiménez, Fernando; Kobilarov, Marin; Martín de Diego, David
2013-06-01
This paper develops numerical methods for optimal control of mechanical systems in the Lagrangian setting. It extends the theory of discrete mechanics to enable the solutions of optimal control problems through the discretization of variational principles. The key point is to solve the optimal control problem as a variational integrator of a specially constructed higher dimensional system. The developed framework applies to systems on tangent bundles, Lie groups, and underactuated and nonholonomic systems with symmetries, and can approximate either smooth or discontinuous control inputs. The resulting methods inherit the preservation properties of variational integrators and result in numerically robust and easily implementable algorithms. Several theoretical examples and a practical one, the control of an underwater vehicle, illustrate the application of the proposed approach.
Clemmons, David R; Chihara, Kazuo; Freda, Pamela U; Ho, Ken K Y; Klibanski, Anne; Melmed, Shlomo; Shalet, Stephen M; Strasburger, Christian J; Trainer, Peter J; Thorner, Michael O
2003-10-01
Acromegaly is associated with significant morbidities and a 2- to 3-fold increase in mortality because of the excessive metabolic action of GH and IGF-I, a marker of GH output. Reductions in morbidity correspond with decreases in IGF-I, and mortality is lowered following normalization of IGF-I or GH levels. Therefore, this has become an important end point. Current guidelines for the treatment of acromegaly have not considered recent advances in medical therapy, in particular, the place of pegvisomant, a GH receptor antagonist. Treatment goals include normalizing biochemical markers, controlling tumor mass, preserving pituitary function, and relieving signs and symptoms. Surgery reduces tumor volume and is considered first-line therapy. Radiation reduces tumor volume and GH and IGF-I levels, but the onset of action is slow and hypopituitarism typically develops. Therefore, pharmacotherapy is often used following surgery or as first-line therapy for nonresectable tumors. Dopamine agonists can be considered in patients exhibiting minimal disease or those with GH-prolactin-cosecreting tumors but will not achieve hormone normalization in most patients. Somatostatin analogs effectively suppress GH and IGF-I in most patients, but intolerance (e.g. diarrhea, cramping, gallstones) can occur. Pegvisomant, the newest therapeutic option, blocks GH action at peripheral receptors, normalizes IGF-I levels, reduces signs and symptoms, and corrects metabolic defects. Pegvisomant does not appear to affect tumor size and has few adverse effects. Pegvisomant is the most effective drug treatment for acromegaly in normalizing IGF-I and producing a clinical response; it is the preferred agent in patients resistant to or intolerant of somatostatin analogs.
Directory of Open Access Journals (Sweden)
Pratap Chandra Pradhan
2016-03-01
Full Text Available In this paper, a Firefly Algorithm (FA optimized fuzzy PID controller is proposed for Automatic Generation Control (AGC of multi-area multi-source power system. Initially, a two area six units power system is used and the gains of the fuzzy PID controller are optimized employing FA optimization technique using an ITAE criterion. The superiority of the proposed FA optimized fuzzy PID controller has been demonstrated by comparing the results with some recently published approaches such as optimal control and Differential Evolution (DE optimized PID controller for the identical interconnected power system. Then, physical constraints such as Time Delay (TD, reheat turbine and Generation Rate Constraint (GRC are included in the system model and the superiority of FA is demonstrated by comparing the results over DE, Gravitational Search Algorithm (GSA and Genetic Algorithm (GA optimization techniques for the same interconnected power system. Additionally, a Unified Power Flow Controller (UPFC is placed in the tie-line and Superconducting Magnetic Energy Storage (SMES units are considered in both areas. Simulation results show that the system performances are improved significantly with the proposed UPFC and SMES units. Sensitivity analysis of the system is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed controller design is verified by considering different types of load patterns.
Optimal Design of TS Fuzzy Control System Based on DNA Evolutionary Algorithm%采用DNA进化算法优化设计TS模糊控制器
Institute of Scientific and Technical Information of China (English)
翁妙凤
2003-01-01
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.
NEW HMM ALGORITHM FOR TOPOLOGY OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Zuo Kongtian; Zhao Yudong; Chen Liping; Zhong Yifang; Huang Yuying
2005-01-01
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.
Directory of Open Access Journals (Sweden)
Seyed Mohsen Mousavi
2014-01-01
Full Text Available A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.
Mousavi, Seyed Mohsen; Niaki, S T A; Bahreininejad, Ardeshir; Musa, Siti Nurmaya
2014-01-01
A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.
Algorithms for optimizing drug therapy
Directory of Open Access Journals (Sweden)
Martin Lene
2004-07-01
Full Text Available Abstract Background Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. Methods One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface and JavaScript (program logic. Results Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs
Optimal Control of Mechanical Systems
Directory of Open Access Journals (Sweden)
Vadim Azhmyakov
2007-01-01
Full Text Available In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some consistent numerical approximations of a multiobjective optimization problem to the initial optimal control problem. For solving the auxiliary problem, we propose an implementable numerical algorithm.
Lu, Lin; Chang, Yunlong; Li, Yingmin; Lu, Ming
2013-05-01
An orthogonal experiment was conducted by the means of multivariate nonlinear regression equation to adjust the influence of external transverse magnetic field and Ar flow rate on welding quality in the process of welding condenser pipe by high-speed argon tungsten-arc welding (TIG for short). The magnetic induction and flow rate of Ar gas were used as optimum variables, and tensile strength of weld was set to objective function on the base of genetic algorithm theory, and then an optimal design was conducted. According to the request of physical production, the optimum variables were restrained. The genetic algorithm in the MATLAB was used for computing. A comparison between optimum results and experiment parameters was made. The results showed that the optimum technologic parameters could be chosen by the means of genetic algorithm with the conditions of excessive optimum variables in the process of high-speed welding. And optimum technologic parameters of welding coincided with experiment results.
Fire Evacuation using Ant Colony Optimization Algorithm
National Research Council Canada - National Science Library
Kanika Singhal; Shashank Sahu
2016-01-01
... planning.The objective of the algorithm is to minimizes the entire rescue time of all evacuees.The ant colony optimization algorithm is used to solve the complications of shortest route planning. Presented paper gives a comparative overview of various emergency scenarios using ant colony optimization algorithm.
Improved symbiotic organisms search algorithm for solving unconstrained function optimization
Directory of Open Access Journals (Sweden)
Sukanta Nama
2016-09-01
Full Text Available Recently, Symbiotic Organisms Search (SOS algorithm is being used for solving complex problems of optimization. This paper proposes an Improved Symbiotic Organisms Search (I-SOS algorithm for solving different complex unconstrained global optimization problems. In the improved algorithm, a random weighted reflective parameter and predation phase are suggested to enhance the performance of the algorithm. The performances of this algorithm are compared with the other state-of-the-art algorithms. The parametric study of the common control parameter has also been performed.
Optimized dynamical decoupling via genetic algorithms
Quiroz, Gregory; Lidar, Daniel A.
2013-11-01
We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
Optimized Dynamical Decoupling via Genetic Algorithms
Quiroz, Gregory
2013-01-01
We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
一种求解最优控制问题的混合WNN-PSO算法%Hybrid WNN-PSO Algorithm for Optimal Control Problems
Institute of Scientific and Technical Information of China (English)
李树荣; 雷阳; 张强; 张晓东
2013-01-01
To solve optimal control problems with numerical methods, a hybrid wavelet neural network -particle swarm optimization (WNN-PSO) algorithm was developed. The first step of WNN-PSO was to parameterize the optimal control trajectory based on the non-linear approximation capability of the wavelet neural network. Then the optimal control problem was transformed into a non-linear programming problem where the decision variables are the parameters of the wavelet neural network. Lastly, the parameters of the network were optimized by the particle swarm optimization (PSO) algorithm and the global optimal solution of the NLP was obtained. Simulation study on a Bang-Bang optimal control problem and a benchmark chemical process optimal control problem shows the feasibility and effectiveness of the proposed method.%针对最优控制问题的数值求解,提出了一种混合小波神经网络粒子群(WNN-PSO)算法,算法首先利用小波神经网络的非线性逼近能力参数化最优控制轨迹,将最优控制问题转化为非线性规划(NLP)问题,其决策变量为小波神经网络的参数,然后采用粒子群(PSO)算法优化小波神经网络参数,获得NLP问题的全局最优解.针对Bang-Bang最优控制问题和一个经典的化工过程最优控制问题进行仿真研究,验证了所提出算法的可行性和有效性.
A hybrid fly fruit algorithm for PID control parameters optimization%一种用于PID控制参数优化的混合果蝇算法
Institute of Scientific and Technical Information of China (English)
宋娟
2015-01-01
In order to improve optimization performance of fly fruit optimization algorithm( FOA),put forward an assemblage of subsection optimization of a hybrid fly fruit optimization algorithm( hybrid FOA,HFOA),which introduces steady particle swarm optimization( PSO )algorithm into FOA parameters optimization of individual flying distance and smell concentration judgement value,meanwhile,the hybrid method adopts ITAE as fitness function and is applied to a class of unstable systems in PID control. Matlab simulation verification show that HFOA algorithm has fast convergence,good stability and high precision,and it verifies that application of HFOA in PID control parameter optimization are feasible and effective.%针对果蝇优化算法（ FOA）收敛速度快但寻优精度低的缺点，为了改善果蝇算法的优化性能，提出一种混合果蝇优化算法（ HFOA）。HFOA采用分段优化的思想，在优化过程后期采用收敛稳定性较好的粒子群优化（ PSO）算法优化果蝇算法中果蝇个体飞行距离和味道浓度的判定值，采用误差性能指标积分准则ITAE作为适应度函数，并将优化方案应用于一类不稳定系统的PID控制。Matlab仿真验证表明：HFOA计算高效，具有良好的稳定性，收敛精度高，进而验证了HFOA应用于PID控制参数优化是可行而有效的。
Optimal Multistage Algorithm for Adjoint Computation
Energy Technology Data Exchange (ETDEWEB)
Aupy, Guillaume; Herrmann, Julien; Hovland, Paul; Robert, Yves
2016-01-01
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.
Genetic algorithms and fuzzy multiobjective optimization
Sakawa, Masatoshi
2002-01-01
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...
Metaheuristic Optimization: Algorithm Analysis and Open Problems
2012-01-01
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...
Directory of Open Access Journals (Sweden)
Ahmet Demir
2017-01-01
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.
Institute of Scientific and Technical Information of China (English)
Concha Bielza; Juan A.Fernández del Pozo; Pedro Larra(n)aga
2013-01-01
Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation.There are two main approaches to parameter setting:parameter tuning and parameter control.In this paper,we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation.The nodes of this Bayesian network are genetic algorithm parameters to be controlled.Its structure captures probabilistic conditional (in)dependence relationships between the parameters.They are learned from the best individuals,i.e.,the best configurations of the genetic algorithm.Individuals are evaluated by running the genetic algorithm for the respective parameter configuration.Since all these runs are time-consuming tasks,each genetic algorithm uses a small-sized population and is stopped before convergence.In this way promising individuals should not be lost.Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time.Moreover,our approach can cope with as yet unsolved high-dimensional problems.
Oil Reservoir Production Optimization using Optimal Control
DEFF Research Database (Denmark)
Völcker, Carsten; Jørgensen, John Bagterp; Stenby, Erling Halfdan
2011-01-01
Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using the adjo......Practical oil reservoir management involves solution of large-scale constrained optimal control problems. In this paper we present a numerical method for solution of large-scale constrained optimal control problems. The method is a single-shooting method that computes the gradients using...... the adjoint method. We use an Explicit Singly Diagonally Implicit Runge-Kutta (ESDIRK) method for the integration and a quasi-Newton Sequential Quadratic Programming (SQP) algorithm for the constrained optimization. We use this algorithm in a numerical case study to optimize the production of oil from an oil...... reservoir using water ooding and smart well technology. Compared to the uncontrolled case, the optimal operation increases the Net Present Value of the oil field by 10%....
Function Optimization Based on Quantum Genetic Algorithm
Ying Sun; Hegen Xiong
2014-01-01
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...
Parallel algorithms for unconstrained optimizations by multisplitting
Energy Technology Data Exchange (ETDEWEB)
He, Qing [Arizona State Univ., Tempe, AZ (United States)
1994-12-31
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.
Model based development of engine control algorithms
Dekker, H.J.; Sturm, W.L.
1996-01-01
Model based development of engine control systems has several advantages. The development time and costs are strongly reduced because much of the development and optimization work is carried out by simulating both engine and control system. After optimizing the control algorithm it can be executed b
Memetic firefly algorithm for combinatorial optimization
Fister, Iztok; Fister, Iztok; Brest, Janez
2012-01-01
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.
Simulated annealing algorithm for optimal capital growth
Luo, Yong; Zhu, Bo; Tang, Yong
2014-08-01
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.
Directory of Open Access Journals (Sweden)
Babak Yousefi Yegane
2012-01-01
Full Text Available This paper considers an inventory control policy for a two-echelon inventory control system with one supplier-one buyer. We consider the case of deteriorating items which lead to shortage in supply chain. Therefore, it is necessary to decrease the deterioration rate by adding some specification to the packaging of these items that is known as active packaging. Although this packaging can reduce the deteriorating rate of products, but may be increases the cost of both supplier and buyer. Because of the complexity of the mathematical model, a genetic algorithm has been developed to determine the best policy of this inventory control system.
OPTIMIZED STRAPDOWN CONING CORRECTION ALGORITHM
Institute of Scientific and Technical Information of China (English)
黄磊; 刘建业; 曾庆化
2013-01-01
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 .
Gas pipeline optimization using adaptive algorithms
Energy Technology Data Exchange (ETDEWEB)
Smati, A.; Zemmour, N. [INH, Boumerdes (Algeria)
1996-12-31
Transmission gas pipeline network consume significant amounts of energy. Then, minimizing the energy requirements is a challenging task. Due to the nonlinearity and poor knowledge of the system states, several results, based on the optimal control theory, are obtained only for simple configurations. In this paper an optimization scheme in the face of varying demand is carried out. It is based on the use of a dynamic simulation program as a plant model and the Pareto set technique to sell out useful experiments. Experiments are used for the identification of regression models based on an original class of functions. The nonlinear programming algorithm results. Its connection with regression models permits the definition off-line, and for a long time horizon, of the optimal discharge pressure trajectory for all the compressor stations. The use of adaptive algorithms, with high frequency, permits one to cancel the effect of unknown disturbances and errors in demand forecasts. In this way, an on-line optimization scheme using data of SCADA system is presented.
Guerrero, R. D.; Arango, C. A.; Reyes, A.
2016-07-01
We recently proposed a Quantum Optimal Control (QOC) method constrained to build pulses from analytical pulse shapes [R. D. Guerrero et al., J. Chem. Phys. 143(12), 124108 (2015)]. This approach was applied to control the dissociation channel yields of the diatomic molecule KH, considering three potential energy curves and one degree of freedom. In this work, we utilized this methodology to study the strong field control of the cis-trans photoisomerization of 11-cis retinal. This more complex system was modeled with a Hamiltonian comprising two potential energy surfaces and two degrees of freedom. The resulting optimal pulse, made of 6 linearly chirped pulses, was capable of controlling the population of the trans isomer on the ground electronic surface for nearly 200 fs. The simplicity of the pulse generated with our QOC approach offers two clear advantages: a direct analysis of the sequence of events occurring during the driven dynamics, and its reproducibility in the laboratory with current laser technologies.
Optimized QoS Routing Algorithm
Institute of Scientific and Technical Information of China (English)
石明洪; 王思兵; 白英彩
2004-01-01
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.
Tetris Agent Optimization Using Harmony Search Algorithm
Directory of Open Access Journals (Sweden)
Victor II M. Romero
2011-01-01
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.
An Efficient Algorithm for Unconstrained Optimization
Directory of Open Access Journals (Sweden)
Sergio Gerardo de-los-Cobos-Silva
2015-01-01
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.
Optimal control of quantum measurement
Energy Technology Data Exchange (ETDEWEB)
Egger, Daniel; Wilhelm, Frank [Theoretical Physics, Saarland University, 66123 Saarbruecken (Germany)
2015-07-01
Pulses to steer the time evolution of quantum systems can be designed with optimal control theory. In most cases it is the coherent processes that can be controlled and one optimizes the time evolution towards a target unitary process, sometimes also in the presence of non-controllable incoherent processes. Here we show how to extend the GRAPE algorithm in the case where the incoherent processes are controllable and the target time evolution is a non-unitary quantum channel. We perform a gradient search on a fidelity measure based on Choi matrices. We illustrate our algorithm by optimizing a measurement pulse for superconducting phase qubits. We show how this technique can lead to large measurement contrast close to 99%. We also show, within the validity of our model, that this algorithm can produce short 1.4 ns pulses with 98.2% contrast.
Optimal Fungal Space Searching Algorithms.
Asenova, Elitsa; Lin, Hsin-Yu; Fu, Eileen; Nicolau, Dan V; Nicolau, Dan V
2016-10-01
Previous experiments have shown that fungi use an efficient natural algorithm for searching the space available for their growth in micro-confined networks, e.g., mazes. This natural "master" algorithm, which comprises two "slave" sub-algorithms, i.e., collision-induced branching and directional memory, has been shown to be more efficient than alternatives, with one, or the other, or both sub-algorithms turned off. In contrast, the present contribution compares the performance of the fungal natural algorithm against several standard artificial homologues. It was found that the space-searching fungal algorithm consistently outperforms uninformed algorithms, such as Depth-First-Search (DFS). Furthermore, while the natural algorithm is inferior to informed ones, such as A*, this under-performance does not importantly increase with the increase of the size of the maze. These findings suggest that a systematic effort of harvesting the natural space searching algorithms used by microorganisms is warranted and possibly overdue. These natural algorithms, if efficient, can be reverse-engineered for graph and tree search strategies.
Drilling Path Optimization Based on Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Guangyu; ZHANG Weibo; DU Yuexiang
2006-01-01
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.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
An Algorithmic Framework for Multiobjective Optimization
Ganesan, T.; Elamvazuthi, I.; Shaari, Ku Zilati Ku; Vasant, P.
2013-01-01
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
An algorithmic framework for multiobjective optimization.
Ganesan, T; Elamvazuthi, I; Shaari, Ku Zilati Ku; Vasant, P
2013-01-01
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.
An Algorithmic Framework for Multiobjective Optimization
Directory of Open Access Journals (Sweden)
T. Ganesan
2013-01-01
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.
An optimization algorithm of collaborative indoor locating
Directory of Open Access Journals (Sweden)
SHI Ying
2014-06-01
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%.
Optimal control of sun tracking solar concentrators
Hughes, R. O.
1979-01-01
Application of the modern control theory to derive an optimal sun tracking control for a point focusing solar concentrator is presented. A standard tracking problem converted to regulator problem using a sun rate input achieves an almost zero steady state tracking error with the optimal control formulation. However, these control techniques are costly because optimal type algorithms require large computing systems, thus they will be used mainly as comparison standards for other types of control algorithms and help in their development.
Multiphase Return Trajectory Optimization Based on Hybrid Algorithm
Directory of Open Access Journals (Sweden)
Yi Yang
2016-01-01
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.
An Optimal Online Algorithm for Halfplane Intersection
Institute of Scientific and Technical Information of China (English)
WU Jigang; JI Yongchang; CHEN Guoliang
2000-01-01
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.
Distributed Algorithms for Time Optimal Reachability Analysis
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
. 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....
Optimizing neural network forecast by immune algorithm
Institute of Scientific and Technical Information of China (English)
YANG Shu-xia; LI Xiang; LI Ning; YANG Shang-dong
2006-01-01
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.
Research on particle swarm optimization algorithm based on optimal movement probability
Ma, Jianhong; Zhang, Han; He, Baofeng
2017-01-01
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.
On algorithm for building of optimal α-decision trees
Alkhalid, Abdulaziz
2010-01-01
The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.
Optimization of Shallow Foundation Using Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
Mohammad Khajehzadeh
2012-01-01
Full Text Available In this study an effective method for nonlinear constrained optimization of shallow foundation is presented. A newly developed heuristic global optimization algorithm called Gravitational Search Algorithm (GSA is introduced and applied for the optimization of foundation. The algorithm is classified as random search algorithm and does not require initial values and uses a random search instead of a gradient search, so derivative information is unnecessary. The optimization procedure controls all geotechnical and structural design constraints while reducing the overall cost of the foundation. To verify the efficiency of the proposed method, two design examples of spread footing are illustrated. To further validate the effectiveness and robustness of the GSA, these examples are solved using genetic algorithm. The results indicate that the proposed method could provide solutions of high quality, accuracy and efficiency for optimum design of foundation.
A Novel Particle Swarm Optimization Algorithm for Global Optimization.
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.
Directory of Open Access Journals (Sweden)
Leonardo W. T. Silva
2014-08-01
Full Text Available In launching operations, Rocket Tracking Systems (RTS process the trajectory data obtained by radar sensors. In order to improve functionality and maintenance, radars can be upgraded by replacing antennas with parabolic reflectors (PRs with phased arrays (PAs. These arrays enable the electronic control of the radiation pattern by adjusting the signal supplied to each radiating element. However, in projects of phased array radars (PARs, the modeling of the problem is subject to various combinations of excitation signals producing a complex optimization problem. In this case, it is possible to calculate the problem solutions with optimization methods such as genetic algorithms (GAs. For this, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, differentiated coding and a new crossover genetic operator. This operator has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, GA-MMC was successful in more than 90% of the tests for each application, increased the fitness of the final population by more than 20% and reduced the premature convergence.
Martinek, Radek; Kahankova, Radana; Nazeran, Homer; Konecny, Jaromir; Jezewski, Janusz; Janku, Petr; Bilik, Petr; Zidek, Jan; Nedoma, Jan; Fajkus, Marcel
2017-05-19
This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.
Spaceborne SAR Imaging Algorithm for Coherence Optimized.
Directory of Open Access Journals (Sweden)
Zhiwei Qiu
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.
Model-based multiobjective evolutionary algorithm optimization for HCCI engines
Ma, He; Xu, Hongming; Wang, Jihong; Schnier, Thorsten; Neaves, Ben; Tan, Cheng; Wang, Zhi
2014-01-01
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...
A Quasi Time Optimal Receding Horizon Control
Bania, Piotr
2007-01-01
This paper presents a quasi time optimal receding horizon control algorithm. The proposed algorithm generates near time optimal control when the state of the system is far from the target. When the state attains a certain neighbourhood of the aim, it begins the adaptation of the cost function. The purpose of this adaptation is to move from the time optimal control to the stabilizing control. Sufficient conditions for the stability of the closed loop system and the manner of the adaptation of ...
Algorithmic Differentiation for Calculus-based Optimization
Walther, Andrea
2010-10-01
For numerous applications, the computation and provision of exact derivative information plays an important role for optimizing the considered system but quite often also for its simulation. This presentation introduces the technique of Algorithmic Differentiation (AD), a method to compute derivatives of arbitrary order within working precision. Quite often an additional structure exploitation is indispensable for a successful coupling of these derivatives with state-of-the-art optimization algorithms. The talk will discuss two important situations where the problem-inherent structure allows a calculus-based optimization. Examples from aerodynamics and nano optics illustrate these advanced optimization approaches.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-10-01
The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI(λ)D(μ) controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications.
Investigation on evolutionary optimization of chaos control
Energy Technology Data Exchange (ETDEWEB)
Zelinka, Ivan [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: zelinka@fai.utb.cz; Senkerik, Roman [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: senkerik@fai.utb.cz; Navratil, Eduard [Faculty of Applied Informatics, Tomas Bata University in Zli' n, Nad Stranemi 4511, 762 72 Zli' n (Czech Republic)], E-mail: enavratil@fai.utb.cz
2009-04-15
This work deals with an investigation on optimization of the feedback control of chaos based on the use of evolutionary algorithms. The main objective is to show that evolutionary algorithms are capable of optimization of chaos control. As models of deterministic chaotic systems, one-dimensional Logistic equation and two-dimensional Henon map were used. The optimizations were realized in several ways, each one for another set of parameters of evolution algorithms or separate cost functions. The evolutionary algorithm SOMA (self-organizing migrating algorithm) was used in four versions. For each version simulations were repeated several times to show and check for robustness of the applied method.
Distributed Algorithms for Time Optimal Reachability Analysis
DEFF Research Database (Denmark)
Zhang, Zhengkui; Nielsen, Brian; Larsen, Kim Guldstrand
2016-01-01
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....
Schrodinger Equation As a General Optimization Algorithm
Huang, Xiaofei
2009-01-01
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.
Adaptive cuckoo search algorithm for unconstrained optimization.
Ong, Pauline
2014-01-01
Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.
Heuristic Algorithm in Optimal Discrete Structural Designs
Directory of Open Access Journals (Sweden)
Alongkorn Lamom
2008-01-01
Full Text Available This study proposes a Heuristic Algorithm for Material Size Selection (HAMSS. It is developed to handle discrete structural optimization problems. The proposed algorithm (HAMSS, Simulated Annealing Algorithm (SA and the conventional design algorithm obtained from a structural steel design software are studied with three selected examples. The HAMSS, in fact, is the adaptation from the traditional SA. Although the SA is one of the easiest optimization algorithms available, a huge number of function evaluations deter its use in structural optimizations. To obtain the optimum answers by the SA, possible answers are first generated randomly. Many of these possible answers are rejected because they do not pass the constraints. To effectively handle this problem, the behavior of optimal structural design problems is incorporated into the algorithm. The new proposed algorithm is called the HAMSS. The efficiency comparison between the SA and the HAMSS is illustrated in term of number of finite element analysis cycles. Results from the study show that HAMSS can significantly reduce the number of structural analysis cycles while the optimized efficiency is not different.
Global Optimality of the Successive Maxbet Algorithm.
Hanafi, Mohamed; ten Berge, Jos M. F.
2003-01-01
It is known that the Maxbet algorithm, which is an alternative to the method of generalized canonical correlation analysis and Procrustes analysis, may converge to local maxima. Discusses an eigenvalue criterion that is sufficient, but not necessary, for global optimality of the successive Maxbet algorithm. (SLD)
DYNAMIC LABELING BASED FPGA DELAY OPTIMIZATION ALGORITHM
Institute of Scientific and Technical Information of China (English)
吕宗伟; 林争辉; 张镭
2001-01-01
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.
Belief Propagation Algorithm for Portfolio Optimization Problems.
Shinzato, Takashi; Yasuda, Muneki
2015-01-01
The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.
Belief Propagation Algorithm for Portfolio Optimization Problems.
Directory of Open Access Journals (Sweden)
Takashi Shinzato
Full Text Available The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.
Asynchronous Parallel Evolutionary Algorithms for Constrained Optimizations
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Recently Guo Tao proposed a stochastic search algorithm in his PhD thesis for solving function op-timization problems. He combined the subspace search method (a general multi-parent recombination strategy) with the population hill-climbing method. The former keeps a global search for overall situation,and the latter keeps the convergence of the algorithm. Guo's algorithm has many advantages ,such as the sim-plicity of its structure ,the higher accuracy of its results, the wide range of its applications ,and the robustness of its use. In this paper a preliminary theoretical analysis of the algorithm is given and some numerical experiments has been done by using Guo's algorithm for demonstrating the theoretical results. Three asynchronous paral-lel evolutionary algorithms with different granularities for MIMD machines are designed by parallelizing Guo's Algorithm.
Wolf Pack Algorithm for Unconstrained Global Optimization
Directory of Open Access Journals (Sweden)
Hu-Sheng Wu
2014-01-01
Full Text Available The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA. Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.
Algorithms for optimal dyadic decision trees
Energy Technology Data Exchange (ETDEWEB)
Hush, Don [Los Alamos National Laboratory; Porter, Reid [Los Alamos National Laboratory
2009-01-01
A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data sets. This paper enhances and extends this algorithm by: introducing an adaptive grid search for the regularization parameter that guarantees optimal solutions for all relevant trees sizes, revising the core tree-building algorithm so that its run time is substantially smaller for most regularization parameter values on the grid, and incorporating new data structures and data pre-processing steps that provide significant run time enhancement in practice.
An algorithm for online optimization of accelerators
Energy Technology Data Exchange (ETDEWEB)
Huang, Xiaobiao [SLAC National Accelerator Lab., Menlo Park, CA (United States); Corbett, Jeff [SLAC National Accelerator Lab., Menlo Park, CA (United States); Safranek, James [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wu, Juhao [SLAC National Accelerator Lab., Menlo Park, CA (United States)
2013-10-01
We developed a general algorithm for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. Moreover, it is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.
Glowworm swarm optimization theory, algorithms, and applications
Kaipa, Krishnanand N
2017-01-01
This book provides a comprehensive account of the glowworm swarm optimization (GSO) algorithm, including details of the underlying ideas, theoretical foundations, algorithm development, various applications, and MATLAB programs for the basic GSO algorithm. It also discusses several research problems at different levels of sophistication that can be attempted by interested researchers. The generality of the GSO algorithm is evident in its application to diverse problems ranging from optimization to robotics. Examples include computation of multiple optima, annual crop planning, cooperative exploration, distributed search, multiple source localization, contaminant boundary mapping, wireless sensor networks, clustering, knapsack, numerical integration, solving fixed point equations, solving systems of nonlinear equations, and engineering design optimization. The book is a valuable resource for researchers as well as graduate and undergraduate students in the area of swarm intelligence and computational intellige...
Space mapping optimization algorithms for engineering design
DEFF Research Database (Denmark)
Koziel, Slawomir; Bandler, John W.; Madsen, Kaj
2006-01-01
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....
Modified evolutionary algorithm for global optimization
Institute of Scientific and Technical Information of China (English)
郭崇慧; 陆玉昌; 唐焕文
2004-01-01
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.
Novel multi-objective optimization algorithm
Institute of Scientific and Technical Information of China (English)
Jie Zeng; Wei Nie
2014-01-01
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.
Optimality Conditions for Inventory Control
Feinberg, Eugene A.
2016-01-01
This tutorial describes recently developed general optimality conditions for Markov Decision Processes that have significant applications to inventory control. In particular, these conditions imply the validity of optimality equations and inequalities. They also imply the convergence of value iteration algorithms. For total discounted-cost problems only two mild conditions on the continuity of transition probabilities and lower semi-continuity of one-step costs are needed. For average-cost pr...
Gems of combinatorial optimization and graph algorithms
Skutella, Martin; Stiller, Sebastian; Wagner, Dorothea
2015-01-01
Are you looking for new lectures for your course on algorithms, combinatorial optimization, or algorithmic game theory? Maybe you need a convenient source of relevant, current topics for a graduate student or advanced undergraduate student seminar? Or perhaps you just want an enjoyable look at some beautiful mathematical and algorithmic results, ideas, proofs, concepts, and techniques in discrete mathematics and theoretical computer science? Gems of Combinatorial Optimization and Graph Algorithms is a handpicked collection of up-to-date articles, carefully prepared by a select group of international experts, who have contributed some of their most mathematically or algorithmically elegant ideas. Topics include longest tours and Steiner trees in geometric spaces, cartograms, resource buying games, congestion games, selfish routing, revenue equivalence and shortest paths, scheduling, linear structures in graphs, contraction hierarchies, budgeted matching problems, and motifs in networks. This ...
Optimization in engineering models and algorithms
Sioshansi, Ramteen
2017-01-01
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...
Algorithms for worst-case tolerance optimization
DEFF Research Database (Denmark)
Schjær-Jacobsen, Hans; Madsen, Kaj
1979-01-01
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 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....
Optimal Hops-Based Adaptive Clustering Algorithm
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.
Institute of Scientific and Technical Information of China (English)
邓枫; 覃宁; 伍贻兆
2013-01-01
建立了并行EGO全局优化算法框架,并将其应用于二维和三维激波控制鼓包减阻优化设计当中.EGO全局优化算法是一类基于克里金(Kriging)代理模型的全局优化算法,具有自动平衡全局搜索与局部搜索的优点,适合于处理具有少量设计参数但是函数计算耗时很长的优化问题,例如基于计算流体力学的气动优化设计.为考察并行EGO全局优化算法的效率,选用了4个解析优化算例以及二维和三维激波控制鼓包减阻优化问题对克里金信任法、常数取代法以及罚点法等3种启发式并行策略进行了比较.数值实验表明,并行EGO全局优化算法每次取点个数可多达16个之多,加速将近16倍,其中在3种并行策略中又以罚点法效率最高,因而在计算耗时很长的气动外形优化问题当中具有很大的应用前景.%The parallel efficient global optimization (EGO) algorithm is applied to the 2-D/3-D shock control bump optimizations.The EGO algorithm is a global optimization method based on the Kriging model,which is especially designed for expensive black box functions,such as high-fidelity aerodynamic shape optimizations.Three heuristic parallel strategies including "Kriging believer","constant liar" and " imputed point" strategies are investigated based on four benchmark optimization problems and two shock control bump optimization problems.In shock control bump optimization problems,the parallel EGO algorithm is nearly 16 times faster than the serial EGO algorithm.And our research also shows that' Imputed point' strategy is the best strategy in these optimization problems.
Optimization Algorithms for Fully Automatic Optimizing Cross-cut Saw
Institute of Scientific and Technical Information of China (English)
LI Xiaochun; DING Qingxin; ZHAO Honglin; SUN Guangbin; XI Jiaxing
2010-01-01
The optimization of boards by grades plays an important role in the production for cross cutting boards, and the outturn rate and utilization of boards are directly affected by the optimization results of boards by grades. At present, the OptiCut series fully automatic optimizing cross-cut saw(FAOCCS) from Germany Weinig Group occupies the main markets in the world, but no report about the relative theories on the optimization technology and its algorithms is available. There exist some disadvantages in woodworking machinery and equipment used for cross cutting boards in China, for example, low sawing precision, outturn rate of boards and productivity, and difficulty in making statistics on the sawing results. Three optimization modes are presented for the optimization algorithms for FAOCCS, namely, optimization of fixed length, optimization of finger-jointed lumber and mixed optimization. Mathematical models are then established for these three optimization modes, and the corresponding software for realizing the optimization is prepared. Finally, Synthetic evaluation on the established mathematical models is presented through three practical examples. The results of synthetic evaluation indicate that FAOCCS using the optimization modes may raise the outturn rate of boards approximately 8% and the productivity obviously, and allows accurate statistics on the cross cut products of boards. The mathematical models of above three optimization modes are useful for increasing the outturn rate and utilization ratio of boards.
A cuckoo search algorithm for multimodal optimization.
Cuevas, Erik; Reyna-Orta, Adolfo
2014-01-01
Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration.
Optimal Algorithm for Algebraic Factoring
Institute of Scientific and Technical Information of China (English)
支丽红
1997-01-01
This paper presents on optimized method for factoring multivariate polynomials over algebraic extension fields defined by an irreducible ascending set. The basic idea is to convert multivariate polynomials to univariate polynomials and algebraic extension fields to algebraic number fields by suitable integer substituteions.Then factorize the univariate polynomials over the algebraic number fields.Finally,construct mulativariate factors of the original polynomial by Hensel lemma and TRUEFACTOR test.Some examples with timing are included.
An Efficient Chemical Reaction Optimization Algorithm for Multiobjective Optimization.
Bechikh, Slim; Chaabani, Abir; Ben Said, Lamjed
2015-10-01
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.
Optimal obstacle control problem
Institute of Scientific and Technical Information of China (English)
ZHU Li; LI Xiu-hua; GUO Xing-ming
2008-01-01
In the paper we discuss some properties of the state operators of the optimal obstacle control problem for elliptic variational inequality. Existence, uniqueness and regularity of the optimal control problem are established. In addition, the approximation of the optimal obstacle problem is also studied.
A new optimization algorithm based on chaos
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
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.
A Hybrid Evolutionary Algorithm for Discrete Optimization
Directory of Open Access Journals (Sweden)
J. Bhuvana
2015-03-01
Full Text Available Most of the real world multi-objective problems demand us to choose one Pareto optimal solution out of a finite set of choices. Flexible job shop scheduling problem is one such problem whose solutions are required to be selected from a discrete solution space. In this study we have designed a hybrid genetic algorithm to solve this scheduling problem. Hybrid genetic algorithms combine both the aspects of the search, exploration and exploitation of the search space. Proposed algorithm, Hybrid GA with Discrete Local Search, performs global search through the GA and exploits the locality through discrete local search. Proposed hybrid algorithm not only has the ability to generate Pareto optimal solutions and also identifies them with less computation. Five different benchmark test instances are used to evaluate the performance of the proposed algorithm. Results observed shown that the proposed algorithm has produced the known Pareto optimal solutions through exploration and exploitation of the search space with less number of functional evaluations.
Control and optimization system
Xinsheng, Lou
2013-02-12
A system for optimizing a power plant includes a chemical loop having an input for receiving an input parameter (270) and an output for outputting an output parameter (280), a control system operably connected to the chemical loop and having a multiple controller part (230) comprising a model-free controller. The control system receives the output parameter (280), optimizes the input parameter (270) based on the received output parameter (280), and outputs an optimized input parameter (270) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.
Immune Algorithm For Document Query Optimization
Institute of Scientific and Technical Information of China (English)
WangZiqiang; FengBoqin
2005-01-01
To efficiently retrieve relevant document from the rapid proliferation of large information collections, a novel immune algorithm for document query optimization is proposed. The essential ideal of the immune algorithm is that the crossover and mutation of operator are constructed according to its own characteristics of information retrieval. Immune operator is adopted to avoid degeneracy. Relevant documents retrieved am merged to a single document list according to rank formula. Experimental results show that the novel immune algorithm can lead to substantial improvements of relevant document retrieval effectiveness.
Improvements To Glowworm Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Piotr Oramus
2010-01-01
Full Text Available Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multipleoptima of multimodal functions. The algorithm uses an ensemble of agents, which scan thesearch space and exchange information concerning a fitness of their current position. Thefitness is represented by a level of a luminescent quantity called luciferin. An agent movesin direction of randomly chosen neighbour, which broadcasts higher value of the luciferin.Unfortunately, in the absence of neighbours, the agent does not move at all. This is anunwelcome feature, because it diminishes the performance of the algorithm. Additionally,in the case of parallel processing, this feature can lead to unbalanced loads. This paperpresents simple modifications of the original algorithm, which improve performance of thealgorithm by limiting situations, in which the agent cannot move. The paper provides resultsof comparison of an original and modified algorithms calculated for several multimodal testfunctions.
An Optimization Synchronization Algorithm for TDDM Signal
Directory of Open Access Journals (Sweden)
Fang Liu
2016-01-01
Full Text Available The time division data modulation (TDDM mechanism is recommended to improve the communications quality and enhance the antijamming capability of the spread spectrum communication system, which will be used in the next generation global navigation satellite (GNSS systems. According to the principle and the characteristics of TDDM signal, an optimization synchronization algorithm is proposed. In the new algorithm, the synchronization accuracy and environmental adaptability have been improved with the special local sequence structure, the multicorrelation processing, and the proportion threshold mechanism. Thus, the inversion estimation formula was established. The simulation results demonstrate that the new algorithm can eliminate the illegibility threat in the synchronization process and can adapt to a lower SNR. In addition, this algorithm is better than the traditional algorithms in terms of synchronization accuracy and adaptability.
Institute of Scientific and Technical Information of China (English)
王江峰; 伍贻兆; Périaux J
2004-01-01
对比了进化算法(基因算法)与确定性算法(共轭梯度法)在优化控制问题中的优化效率.两种方法都与分散式优化策略-Nash对策进行了结合,并成功地应用于优化控制问题.计算模型采用绕NACA0012翼型的位流流场.区域分裂技术的引用使得全局流场被分裂为多个带有重叠区的子流场,使用4种不同的方法进行当地流场解的耦合,这些算法可以通过当地的流场解求得全局流场解.数值计算结果的对比表明,进化算法可以得到与共轭梯度法相同的计算结果,并且进化算法的不依赖梯度信息的特性使其在复杂问题及非线性问题中具有广泛的应用前景.%The comparison for optimization efficiency between evolutionary algorithms (Genetic Algorithms, GAs) and deterministic algorithms (Conjugate Gradient, CG) is presented. Both two different methods are combined with Nash strategy-decentralized optimization strategy in Game Theory-and implemented into an optimal control problem using a technique DDM (Domain Decomposition Method). The problem consists in simulating the perfect potential flow field around a NACA0012 airfoil with the technique DDM, the global calculation domain is then split into sub-domains with overlaps, the accord of local solutions on interfaces is obtained using four different algorithms which permit the resolution of global problem via local sub-problems on sub-domains and their interfaces. Comparable numerical results are obtained by different algorithms and show that the property of independence of gradient makes GAs based algorithms serious and robust research tools for great dimension problems or non-linear problems.
Function Optimization Based on Quantum Genetic Algorithm
Directory of Open Access Journals (Sweden)
Ying Sun
2014-01-01
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.
Stochastic Recursive Algorithms for Optimization Simultaneous Perturbation Methods
Bhatnagar, S; Prashanth, L A
2013-01-01
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from sim...
Optimization of Submarine Hydrodynamic Coefficients Based on Immune Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
HU Kun; XU Yi-fan
2010-01-01
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.
Angelic Hierarchical Planning: Optimal and Online Algorithms
2008-12-06
restrict our attention to plans in I∗(Act, s0). Definition 2. ( Parr and Russell , 1998) A plan ah∗ is hierarchically optimal iff ah∗ = argmina∈I∗(Act,s0):T...Murdock, Dan Wu, and Fusun Yaman. SHOP2: An HTN planning system. JAIR, 20:379–404, 2003. Ronald Parr and Stuart Russell . Reinforcement Learning with...Angelic Hierarchical Planning: Optimal and Online Algorithms Bhaskara Marthi Stuart J. Russell Jason Wolfe Electrical Engineering and Computer
Online Algorithms for Adaptive Optimization in Heterogeneous Delay Tolerant Networks
Directory of Open Access Journals (Sweden)
Wissam Chahin
2013-12-01
Full Text Available Delay Tolerant Networks (DTNs are an emerging type of networks which do not need a predefined infrastructure. In fact, data forwarding in DTNs relies on the contacts among nodes which may possess different features, radio range, battery consumption and radio interfaces. On the other hand, efficient message delivery under limited resources, e.g., battery or storage, requires to optimize forwarding policies. We tackle optimal forwarding control for a DTN composed of nodes of different types, forming a so-called heterogeneous network. Using our model, we characterize the optimal policies and provide a suitable framework to design a new class of multi-dimensional stochastic approximation algorithms working for heterogeneous DTNs. Crucially, our proposed algorithms drive online the source node to the optimal operating point without requiring explicit estimation of network parameters. A thorough analysis of the convergence properties and stability of our algorithms is presented.
Adaptive backtracking search optimization algorithm with pattern search for numerical optimization
Institute of Scientific and Technical Information of China (English)
Shu Wang; Xinyu Da; Mudong Li; Tong Han
2016-01-01
Thebacktracking search optimization algorithm (BSA) is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capa-bility to find global optimal solutions. However, the algorithm is stil insufficient in balancing the exploration and the exploita-tion. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts: the BSA is used for the exploration phase and the modified pattern search method completes the ex-ploitation phase. In particular, a simple but effective strategy of adapting one of BSA’s important control parameters is intro-duced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Com-putation 2014 (IEEE CEC2014) over six widely-used bench-marks and 22 real-parameter single objective numerical opti-mization benchmarks in IEEE CEC2014. The results of ex-periment and statistical analysis demonstrate the effective-ness and efficiency of the proposed algorithm.
Optimal control studies for steamflooding
Energy Technology Data Exchange (ETDEWEB)
Liu, Wei.
1992-01-01
A system science approach using optimal control theory of distributed parameter systems has been developed to determine operating strategies that maximize the economic attractiveness of the steamflooding Enhanced Oil Recovery (EOR) process. Necessary conditions for optimization are established by using the calculus of variations and Pontryagin's Maximum Principle. The objective criterion is to maximize the difference between oil revenue and injected steam cost. A stable and efficient numerical algorithm, based on an iterative gradient method, is developed. The optimal control model is based on a three-dimensional, three-phase (oil, steam and water) steam injection numerical simulator. A discrete form of the model is formulated. The optimized operating variables are the optimal bottom-hole pressure, the optimal injection rate of steam and water, and the optimal steam quality policies. Another optimal control study is also conducted on a simplified one-dimensional model (the extended Neuman model) to provide quick and reliable preliminary information on the economic feasibility of steamflooding processes. The simplified control model only considers the injection rate of steam as the control variable. The performance of this system science approach is investigated through various one-, two- and three-dimensional steamflooding problems. The effects of reservoir properties and heterogeneity on optimal policies as well as the sensitivity of the control variables are also studied. Results show this approach yields significant insight into the steamflooding EOR process. Improvement of the economic objective is significant under optimal operation conditions. These optimization results are quite important in a successful application of the steamflooding EOR method.
Schaft, A.J. van der
1987-01-01
It is argued that the existence of symmetries may simplify, as in classical mechanics, the solution of optimal control problems. A procedure for obtaining symmetries for the optimal Hamiltonian resulting from the Maximum Principle is given; this avoids the actual calculation of the optimal
Worst-case Optimal Join Algorithms
Ngo, Hung Q; Ré, Christopher; Rudra, Atri
2012-01-01
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...
Combinatorial Multiobjective Optimization Using Genetic Algorithms
Crossley, William A.; Martin. Eric T.
2002-01-01
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.
Optimal Power Flow by Interior Point and Non Interior Point Modern Optimization Algorithms
Directory of Open Access Journals (Sweden)
Marcin Połomski
2013-03-01
Full Text Available The idea of optimal power flow (OPF is to determine the optimal settings for control variables while respecting various constraints, and in general it is related to power system operational and planning optimization problems. A vast number of optimization methods have been applied to solve the OPF problem, but their performance is highly dependent on the size of a power system being optimized. The development of the OPF recently has tracked significant progress both in numerical optimization techniques and computer techniques application. In recent years, application of interior point methods to solve OPF problem has been paid great attention. This is due to the fact that IP methods are among the fastest algorithms, well suited to solve large-scale nonlinear optimization problems. This paper presents the primal-dual interior point method based optimal power flow algorithm and new variant of the non interior point method algorithm with application to optimal power flow problem. Described algorithms were implemented in custom software. The experiments show the usefulness of computational software and implemented algorithms for solving the optimal power flow problem, including the system model sizes comparable to the size of the National Power System.
TWO ALGORITHMS FOR LC1 UNCONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Wen-yu Sun; R.J.B.de Sampaio; Jin-Yun Yuan
2000-01-01
In this paper we present two algorithms for LC1 unconstrained optimization problems which use the second order Dini upper directional derivative. These methods are simple and easy to perform. We discuss the related properties of the iteration function, and establish the global and superlinear convergence of our methods.
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2014-01-01
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 ...
Advances in metaheuristic algorithms for optimal design of structures
Kaveh, A
2017-01-01
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 ...
Algorithm of capacity expansion on networks optimization
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
The paper points out the relationship between the bottleneck and the minimum cutset of the network, and presents a capacity expansion algorithm of network optimization to solve the network bottleneck problem. The complexity of the algorithm is also analyzed. As required by the algorithm, some virtual sources are imported through the whole positive direction subsection in the network, in which a certain capacity value is given. Simultaneously, a corresponding capacity-expanded network is constructed to search all minimum cutsets. For a given maximum flow value of the network, the authors found an adjustment value of each minimum cutset arc's group with gradually reverse calculation and marked out the feasible flow on the capacity-extended networks again with the adjustment value increasing. All this has been done repeatedly until the original topology structure is resumed. So the algorithm can increase the capacity of networks effectively and solve the bottleneck problem of networks.
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
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.
Optimization Algorithms in Optimal Predictions of Atomistic Properties by Kriging.
Di Pasquale, Nicodemo; Davie, Stuart J; Popelier, Paul L A
2016-04-12
The machine learning method kriging is an attractive tool to construct next-generation force fields. Kriging can accurately predict atomistic properties, which involves optimization of the so-called concentrated log-likelihood function (i.e., fitness function). The difficulty of this optimization problem quickly escalates in response to an increase in either the number of dimensions of the system considered or the size of the training set. In this article, we demonstrate and compare the use of two search algorithms, namely, particle swarm optimization (PSO) and differential evolution (DE), to rapidly obtain the maximum of this fitness function. The ability of these two algorithms to find a stationary point is assessed by using the first derivative of the fitness function. Finally, the converged position obtained by PSO and DE is refined through the limited-memory Broyden-Fletcher-Goldfarb-Shanno bounded (L-BFGS-B) algorithm, which belongs to the class of quasi-Newton algorithms. We show that both PSO and DE are able to come close to the stationary point, even in high-dimensional problems. They do so in a reasonable amount of time, compared to that with the Newton and quasi-Newton algorithms, regardless of the starting position in the search space of kriging hyperparameters. The refinement through L-BFGS-B is able to give the position of the maximum with whichever precision is desired.
Optimization of Adaptive Transit Signal Priority Using Parallel Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
Guangwei Zhou; Albert Gan; L. David Shen
2007-01-01
Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority (TSP) strategy that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of TSP. The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. A VISSlM (VISual SIMulation) simulation testbed was developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP.The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases. The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles.
Generalized Weiszfeld Algorithms for Lq Optimization.
Aftab, Khurrum; Hartley, Richard; Trumpf, Jochen
2015-04-01
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.
Optimal control computer programs
Kuo, F.
1992-01-01
The solution of the optimal control problem, even with low order dynamical systems, can usually strain the analytical ability of most engineers. The understanding of this subject matter, therefore, would be greatly enhanced if a software package existed that could simulate simple generic problems. Surprisingly, despite a great abundance of commercially available control software, few, if any, address the part of optimal control in its most generic form. The purpose of this paper is, therefore, to present a simple computer program that will perform simulations of optimal control problems that arise from the first necessary condition and the Pontryagin's maximum principle.
Optimal Search Mechanism Analysis of Light Ray Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Jihong SHEN; Jialian LI; Bin WEI
2012-01-01
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.
Modified constriction particle swarm optimization algorithm
Institute of Scientific and Technical Information of China (English)
Zhe Zhang; Limin Jia; Yong Qin
2015-01-01
To deal with the demerits of constriction particle swarm optimization (CPSO), such as relapsing into local optima, slow convergence velocity, a modified CPSO algorithm is proposed by improving the velocity update formula of CPSO. The random ve-locity operator from local optima to global optima is added into the velocity update formula of CPSO to accelerate the convergence speed of the particles to the global optima and reduce the likeli-hood of being trapped into local optima. Final y the convergence of the algorithm is verified by calculation examples.
Configurable intelligent optimization algorithm design and practice in manufacturing
Tao, Fei; Laili, Yuanjun
2014-01-01
Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorit
Directory of Open Access Journals (Sweden)
Mukhtar Fatihu Hamza
2015-01-01
function. The performance characteristics considered for the controller are steady state error, settling time, rise time, maximum overshoot, and control energy. Experimental and simulation results indicated that the effectiveness and robustness of the proposed GA- and PSO-based controllers on the RIP with respect to load disturbances, parameter variation, and noise effects have been improved over state-of-the-art method. However, the comparative results for simulation and experiment based on cascade IT2F-PDC indicate that GA-based IT2F-PDC has lower steady state error while PSO-based IT2F-PDC has lower overshoot, settling time, and control energy, but both have almost the same rise time. The proposed control strategy can be regarded as a promising strategy for controlling different unstable and nonlinear systems.
Optimal Design of a Centrifugal Compressor Impeller Using Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Soo-Yong Cho
2012-01-01
Full Text Available An optimization study was conducted on a centrifugal compressor. Eight design variables were chosen from the control points for the Bezier curves which widely influenced the geometric variation; four design variables were selected to optimize the flow passage between the hub and the shroud, and other four design variables were used to improve the performance of the impeller blade. As an optimization algorithm, an artificial neural network (ANN was adopted. Initially, the design of experiments was applied to set up the initial data space of the ANN, which was improved during the optimization process using a genetic algorithm. If a result of the ANN reached a higher level, that result was re-calculated by computational fluid dynamics (CFD and was applied to develop a new ANN. The prediction difference between the ANN and CFD was consequently less than 1% after the 6th generation. Using this optimization technique, the computational time for the optimization was greatly reduced and the accuracy of the optimization algorithm was increased. The efficiency was improved by 1.4% without losing the pressure ratio, and Pareto-optimal solutions of the efficiency versus the pressure ratio were obtained through the 21st generation.
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Particle algorithms for optimization on binary spaces
Schäfer, Christian
2011-01-01
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.
An Optimal Algorithm for Linear Bandits
Cesa-Bianchi, Nicolò
2011-01-01
We provide the first algorithm for online bandit linear optimization whose regret after T rounds is of order sqrt{Td ln N} on any finite class X of N actions in d dimensions, and of order d*sqrt{T} (up to log factors) when X is infinite. These bounds are not improvable in general. The basic idea utilizes tools from convex geometry to construct what is essentially an optimal exploration basis. We also present an application to a model of linear bandits with expert advice. Interestingly, these results show that bandit linear optimization with expert advice in d dimensions is no more difficult (in terms of the achievable regret) than the online d-armed bandit problem with expert advice (where EXP4 is optimal).
Iterative learning control an optimization paradigm
Owens, David H
2016-01-01
This book develops a coherent theoretical approach to algorithm design for iterative learning control based on the use of optimization concepts. Concentrating initially on linear, discrete-time systems, the author gives the reader access to theories based on either signal or parameter optimization. Although the two approaches are shown to be related in a formal mathematical sense, the text presents them separately because their relevant algorithm design issues are distinct and give rise to different performance capabilities. Together with algorithm design, the text demonstrates that there are new algorithms that are capable of incorporating input and output constraints, enable the algorithm to reconfigure systematically in order to meet the requirements of different reference signals and also to support new algorithms for local convergence of nonlinear iterative control. Simulation and application studies are used to illustrate algorithm properties and performance in systems like gantry robots and other elect...
Song, Ting; Li, Nan; Zarepisheh, Masoud; Li, Yongbao; Gautier, Quentin; Zhou, Linghong; Mell, Loren; Jiang, Steve; Cerviño, Laura
2016-01-01
Intensity-modulated radiation therapy (IMRT) currently plays an important role in radiotherapy, but its treatment plan quality can vary significantly among institutions and planners. Treatment plan quality control (QC) is a necessary component for individual clinics to ensure that patients receive treatments with high therapeutic gain ratios. The voxel-weighting factor-based plan re-optimization mechanism has been proved able to explore a larger Pareto surface (solution domain) and therefore increase the possibility of finding an optimal treatment plan. In this study, we incorporated additional modules into an in-house developed voxel weighting factor-based re-optimization algorithm, which was enhanced as a highly automated and accurate IMRT plan QC tool (TPS-QC tool). After importing an under-assessment plan, the TPS-QC tool was able to generate a QC report within 2 minutes. This QC report contains the plan quality determination as well as information supporting the determination. Finally, the IMRT plan quality can be controlled by approving quality-passed plans and replacing quality-failed plans using the TPS-QC tool. The feasibility and accuracy of the proposed TPS-QC tool were evaluated using 25 clinically approved cervical cancer patient IMRT plans and 5 manually created poor-quality IMRT plans. The results showed high consistency between the QC report quality determinations and the actual plan quality. In the 25 clinically approved cases that the TPS-QC tool identified as passed, a greater difference could be observed for dosimetric endpoints for organs at risk (OAR) than for planning target volume (PTV), implying that better dose sparing could be achieved in OAR than in PTV. In addition, the dose-volume histogram (DVH) curves of the TPS-QC tool re-optimized plans satisfied the dosimetric criteria more frequently than did the under-assessment plans. In addition, the criteria for unsatisfied dosimetric endpoints in the 5 poor-quality plans could typically be
Algorithms for Optimally Arranging Multicore Memory Structures
Directory of Open Access Journals (Sweden)
Wei-Che Tseng
2010-01-01
Full Text Available As more processing cores are added to embedded systems processors, the relationships between cores and memories have more influence on the energy consumption of the processor. In this paper, we conduct fundamental research to explore the effects of memory sharing on energy in a multicore processor. We study the Memory Arrangement (MA Problem. We prove that the general case of MA is NP-complete. We present an optimal algorithm for solving linear MA and optimal and heuristic algorithms for solving rectangular MA. On average, we can produce arrangements that consume 49% less energy than an all shared memory arrangement and 14% less energy than an all private memory arrangement for randomly generated instances. For DSP benchmarks, we can produce arrangements that, on average, consume 20% less energy than an all shared memory arrangement and 27% less energy than an all private memory arrangement.
FOGSAA: Fast Optimal Global Sequence Alignment Algorithm
Chakraborty, Angana; Bandyopadhyay, Sanghamitra
2013-04-01
In this article we propose a Fast Optimal Global Sequence Alignment Algorithm, FOGSAA, which aligns a pair of nucleotide/protein sequences faster than any optimal global alignment method including the widely used Needleman-Wunsch (NW) algorithm. FOGSAA is applicable for all types of sequences, with any scoring scheme, and with or without affine gap penalty. Compared to NW, FOGSAA achieves a time gain of (70-90)% for highly similar nucleotide sequences (> 80% similarity), and (54-70)% for sequences having (30-80)% similarity. For other sequences, it terminates with an approximate score. For protein sequences, the average time gain is between (25-40)%. Compared to three heuristic global alignment methods, the quality of alignment is improved by about 23%-53%. FOGSAA is, in general, suitable for aligning any two sequences defined over a finite alphabet set, where the quality of the global alignment is of supreme importance.
Trajectory metaheuristic algorithms to optimize problems combinatorics
Directory of Open Access Journals (Sweden)
Natalia Alancay
2016-12-01
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.
Intelligent perturbation algorithms for space scheduling optimization
Kurtzman, Clifford R.
1990-01-01
The optimization of space operations is examined in the light of optimization heuristics for computer algorithms and iterative search techniques. Specific attention is given to the search concepts known collectively as intelligent perturbation algorithms (IPAs) and their application to crew/resource allocation problems. IPAs iteratively examine successive schedules which become progressively more efficient, and the characteristics of good perturbation operators are listed. IPAs can be applied to aerospace systems to efficiently utilize crews, payloads, and resources in the context of systems such as Space-Station scheduling. A program is presented called the MFIVE Space Station Scheduling Worksheet which generates task assignments and resource usage structures. The IPAs can be used to develop flexible manifesting and scheduling for the Industrial Space Facility.
Genetic algorithm optimization for finned channel performance
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
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.
Optimization of neutron monitor data correction algorithms
Energy Technology Data Exchange (ETDEWEB)
Paschalis, P. [Nuclear and Particle Physics Section, Physics Department, National and Kapodistrian University of Athens, Zografos 15783, Athens (Greece); Mavromichalaki, H., E-mail: emavromi@phys.uoa.gr [Nuclear and Particle Physics Section, Physics Department, National and Kapodistrian University of Athens, Zografos 15783, Athens (Greece)
2013-06-21
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.
Institute of Scientific and Technical Information of China (English)
刘显贵; 陈无畏; 罗善明; 钟铭恩
2015-01-01
interferences between the mechanical structure and control system of vehicle chassis system and to further enhance the overall performance of vehicle, the method of simultaneous optimization of mechanical structure and controller parameters of vehicle chassis system based on Particle Swarm Optimization Algorithm is presented in this paper. According to the fundamental principle of vehicle dynamics, a half car mode of active suspension system and anti-lock braking systems established at first. Then the linear quadratic gauss controller of the active suspension system and sliding mode controller of anti-lock braking system are designed. Taking controllers designed for active suspension system and anti-lock braking system as bottom controllers, the upper coordinated control logic of the systems is put forward and the upper PID coordinated controller is designed on the basis of analyzing the coupling conflict between active suspension system and anti-lock braking system. Finally, a Particle Swarm Optimization Algorithm was adopted for simultaneous optimization of mechanical structure and controller parameters of vehicle chassis integrated control system, because the traditional design method of a vehicle system is always to design control parameters following structure parameters and it can not obtain the global optimal performances for the system. In order to verify the effectiveness of the algorithm, the simultaneous optimization program is developed based on Particle Swarm Optimization Algorithm in MATLAB environment while the mechanical structure and control parameters of chassis control system are set as optimization variables and the overall vehicle dynamics performance is set as objective function. Simulation result shows that the pitching angular acceleration of vehicle is reduced and vehicle riding comfort performance is improved after optimized. Braking distance and dynamic load of front and rear wheels of vehicle are also reduced significantly which indicating vehicle
Graaf, de S.C.; Stigter, J.D.; Straten, van G.
2004-01-01
The adjustable control-variation weight (ACW)-gradient method proposed by Weinreb [Optimal Control with Multiple Bounded Inputs, Department of Electrical Engineering, Stanford University, Stanford, 1985, p. 148] is put to the test in finding optimal control laws for an optimisation problem with boun
Institute of Scientific and Technical Information of China (English)
陈庆更; 王宁
2005-01-01
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.
Optimization algorithm for the generation of ONCV pseudopotentials
Schlipf, Martin; Gygi, François
2015-11-01
We present an optimization algorithm to construct pseudopotentials and use it to generate a set of Optimized Norm-Conserving Vanderbilt (ONCV) pseudopotentials for elements up to Z = 83 (Bi) (excluding Lanthanides). We introduce a quality function that assesses the agreement of a pseudopotential calculation with all-electron FLAPW results, and the necessary plane-wave energy cutoff. This quality function allows us to use a Nelder-Mead optimization algorithm on a training set of materials to optimize the input parameters of the pseudopotential construction for most of the periodic table. We control the accuracy of the resulting pseudopotentials on a test set of materials independent of the training set. We find that the automatically constructed pseudopotentials
Jianwen Guo; Zhenzhong Sun; Hong Tang; Xuejun Jia; Song Wang; Xiaohui Yan; Guoliang Ye; Guohong Wu
2016-01-01
All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM) to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO) and cuckoo search (CS) algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test fun...
Focused Crawler Optimization Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Hartanto Kusuma Wardana
2011-12-01
Full Text Available As the size of the Web continues to grow, searching it for useful information has become more difficult. Focused crawler intends to explore the Web conform to a specific topic. This paper discusses the problems caused by local searching algorithms. Crawler can be trapped within a limited Web community and overlook suitable Web pages outside its track. A genetic algorithm as a global searching algorithm is modified to address the problems. The genetic algorithm is used to optimize Web crawling and to select more suitable Web pages to be fetched by the crawler. Several evaluation experiments are conducted to examine the effectiveness of the approach. The crawler delivers collections consist of 3396 Web pages from 5390 links which had been visited, or filtering rate of Roulette-Wheel selection at 63% and precision level at 93% in 5 different categories. The result showed that the utilization of genetic algorithm had empowered focused crawler to traverse the Web comprehensively, despite it relatively small collections. Furthermore, it brought up a great potential for building an exemplary collections compared to traditional focused crawling methods.
A GREEDY GENETIC ALGORITHM FOR UNCONSTRAINED GLOBAL OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
ZHAO Xinchao
2005-01-01
The greedy algorithm is a strong local searching algorithm. The genetica lgorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.
Bioinspired computation in combinatorial optimization: algorithms and their computational complexity
DEFF Research Database (Denmark)
Neumann, Frank; Witt, Carsten
2012-01-01
Bioinspired computation methods, such as evolutionary algorithms and ant colony optimization, are being applied successfully to complex engineering and combinatorial optimization problems, and it is very important that we understand the computational complexity of these algorithms. This tutorials...
An Approach In Optimization Of Ad-Hoc Routing Algorithms
Directory of Open Access Journals (Sweden)
Sarvesh Kumar Sharma
2012-06-01
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.
Colonius, Fritz
1988-01-01
This research monograph deals with optimal periodic control problems for systems governed by ordinary and functional differential equations of retarded type. Particular attention is given to the problem of local properness, i.e. whether system performance can be improved by introducing periodic motions. Using either Ekeland's Variational Principle or optimization theory in Banach spaces, necessary optimality conditions are proved. In particular, complete proofs of second-order conditions are included and the result is used for various versions of the optimal periodic control problem. Furthermore a scenario for local properness (related to Hopf bifurcation) is drawn up, giving hints as to where to look for optimal periodic solutions. The book provides mathematically rigorous proofs for results which are potentially of importance in chemical engineering and aerospace engineering.
Control parameter analysis of quantum-behaved particle swarm optimization algorithm%量子粒子群优化算法的控制参数分析
Institute of Scientific and Technical Information of China (English)
陶重阳; 杨新宇; 于翔深; 赵航
2014-01-01
In this paper, two parameter-control methods were proposed to remedy deficiencies of existed Quantum-behaved Particle Swarm Optimization ( QPSO) algorithm based on fixed contraction-expansion coefficientαor linear variation not being able to well address problems in complicated nonlinear optimization search. The first was dynamic nonlinear regressive strategy based on logistic function, in which S-type function was introduced to describe the dynamic nature of the value in its evolvement. The second was the adaptive parameter adjustment strategy, in which feedback regulation was introduced to control change in the value. In the case of complicated optimization, experimental results on several typical functions show that the proposed strategy significantly outperforms the existed ones ( those based on fixed value or linear variation) on both average optimal value and convergence rate.%针对现有的量子粒子群优化算法（ QPSO）中收缩扩张系数α取固定值或线性变化时，不能很好地适应复杂的多维非线性优化搜索问题，提出了两种参数α控制策略：基于Logistic函数的动态非线性递减策略和自适应参数调整策略。在第一种策略中引入S型函数来描述α值在进化过程中的动态变化特性，第二种策略中引入反馈调节方式来控制α值的变化。几个典型函数的实验测试结果表明，两种改进后的参数调整策略对于复杂优化问题在收敛速度和平均最优值上都有所改善，明显优于取固定值或线性变化策略。
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
A Clustering Genetic Algorithm for Cylinder Drag Optimization
Milano, Michele; Koumoutsakos, Petros
2002-01-01
A real coded genetic algorithm is implemented for the optimization of actuator parameters for cylinder drag minimization. We consider two types of idealized actuators that are allowed either to move steadily and tangentially to the cylinder surface (“belts”) or to steadily blow/suck with a zero net mass constraint. The genetic algorithm we implement has the property of identifying minima basins, rather than single optimum points. The knowledge of the shape of the minimum basin enables further insights into the system properties and provides a sensitivity analysis in a fully automated way. The drag minimization problem is formulated as an optimal regulation problem. By means of the clustering property of the present genetic algorithm, a set of solutions producing drag reduction of up to 50% is identified. A comparison between the two types of actuators, based on the clustering property of the algorithm, indicates that blowing/suction actuation parameters are associated with larger tolerances when compared to optimal parameters for the belt actuators. The possibility of using a few strategically placed actuators to obtain a significant drag reduction is explored using the clustering diagnostics of this method. The optimal belt-actuator parameters obtained by optimizing the two-dimensional case is employed in three-dimensional simulations, by extending the actuators across the span of the cylinder surface. The three-dimensional controlled flow exhibits a strong two-dimensional character near the cylinder surface, resulting in significant drag reduction.
Genetic algorithms in bidisciplinary (aerodynamics/electromagnetism) optimization
Institute of Scientific and Technical Information of China (English)
朱自强; 李海明; 李津; 于日新
2001-01-01
The genetic algorithm(GA) is a non-traditional, probability search and global optimization method similar to natural selection and evolution. The key points and control parameters of this method are briefly discussed. To apply it to a multiobjective and multidisciplinary optimization problem a kind of fitness function is suggested, in which the requirements of multiobjects and multiconstraints are considered and the nondimensional coefficients and panalty coefficients of the constraint function are also introduced. Numerical results of bidisciplinary optimization calculation show that the present method is effective, applicable, and robust.
Optimal control of hydroelectric facilities
Zhao, Guangzhi
This thesis considers a simple yet realistic model of pump-assisted hydroelectric facilities operating in a market with time-varying but deterministic power prices. Both deterministic and stochastic water inflows are considered. The fluid mechanical and engineering details of the facility are described by a model containing several parameters. We present a dynamic programming algorithm for optimizing either the total energy produced or the total cash generated by these plants. The algorithm allows us to give the optimal control strategy as a function of time and to see how this strategy, and the associated plant value, varies with water inflow and electricity price. We investigate various cases. For a single pumped storage facility experiencing deterministic power prices and water inflows, we investigate the varying behaviour for an oversimplified constant turbine- and pump-efficiency model with simple reservoir geometries. We then generalize this simple model to include more realistic turbine efficiencies, situations with more complicated reservoir geometry, and the introduction of dissipative switching costs between various control states. We find many results which reinforce our physical intuition about this complicated system as well as results which initially challenge, though later deepen, this intuition. One major lesson of this work is that the optimal control strategy does not differ much between two differing objectives of maximizing energy production and maximizing its cash value. We then turn our attention to the case of stochastic water inflows. We present a stochastic dynamic programming algorithm which can find an on-average optimal control in the face of this randomness. As the operator of a facility must be more cautious when inflows are random, the randomness destroys facility value. Following this insight we quantify exactly how much a perfect hydrological inflow forecast would be worth to a dam operator. In our final chapter we discuss the
Distributed Algorithms for Optimal Power Flow Problem
Lam, Albert Y S; Tse, David
2011-01-01
Optimal power flow (OPF) is an important problem for power generation and it is in general non-convex. With the employment of renewable energy, it will be desirable if OPF can be solved very efficiently so its solution can be used in real time. With some special network structure, e.g. trees, the problem has been shown to have a zero duality gap and the convex dual problem yields the optimal solution. In this paper, we propose a primal and a dual algorithm to coordinate the smaller subproblems decomposed from the convexified OPF. We can arrange the subproblems to be solved sequentially and cumulatively in a central node or solved in parallel in distributed nodes. We test the algorithms on IEEE radial distribution test feeders, some random tree-structured networks, and the IEEE transmission system benchmarks. Simulation results show that the computation time can be improved dramatically with our algorithms over the centralized approach of solving the problem without decomposition, especially in tree-structured...
Optimal control of motorsport differentials
Tremlett, A. J.; Massaro, M.; Purdy, D. J.; Velenis, E.; Assadian, F.; Moore, A. P.; Halley, M.
2015-12-01
Modern motorsport limited slip differentials (LSD) have evolved to become highly adjustable, allowing the torque bias that they generate to be tuned in the corner entry, apex and corner exit phases of typical on-track manoeuvres. The task of finding the optimal torque bias profile under such varied vehicle conditions is complex. This paper presents a nonlinear optimal control method which is used to find the minimum time optimal torque bias profile through a lane change manoeuvre. The results are compared to traditional open and fully locked differential strategies, in addition to considering related vehicle stability and agility metrics. An investigation into how the optimal torque bias profile changes with reduced track-tyre friction is also included in the analysis. The optimal LSD profile was shown to give a performance gain over its locked differential counterpart in key areas of the manoeuvre where a quick direction change is required. The methodology proposed can be used to find both optimal passive LSD characteristics and as the basis of a semi-active LSD control algorithm.
A Survey on Meta-Heuristic Global Optimization Algorithms
Directory of Open Access Journals (Sweden)
Mohammad Khajehzadeh
2011-06-01
Full Text Available Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Recently, metaheuristic global optimization algorithms have become a popular choice for solving complex and intricate problems, which are otherwise difficult to solve by traditional methods. In the present study, an attempt is made to review the most popular and well known metaheuristic global optimization algorithms introduced during the past decades.
Subcubic Control Flow Analysis Algorithms
DEFF Research Database (Denmark)
Midtgaard, Jan; Van Horn, David
We give the first direct subcubic algorithm for performing control flow analysis of higher-order functional programs. Despite the long held belief that inclusion-based flow analysis could not surpass the ``cubic bottleneck, '' we apply known set compression techniques to obtain an algorithm...... that runs in time O(n^3/log n) on a unit cost random-access memory model machine. Moreover, we refine the initial flow analysis into two more precise analyses incorporating notions of reachability. We give subcubic algorithms for these more precise analyses and relate them to an existing analysis from...
Clonal Selection Algorithm Based Iterative Learning Control with Random Disturbance
Directory of Open Access Journals (Sweden)
Yuanyuan Ju
2013-01-01
Full Text Available Clonal selection algorithm is improved and proposed as a method to solve optimization problems in iterative learning control. And a clonal selection algorithm based optimal iterative learning control algorithm with random disturbance is proposed. In the algorithm, at the same time, the size of the search space is decreased and the convergence speed of the algorithm is increased. In addition a model modifying device is used in the algorithm to cope with the uncertainty in the plant model. In addition a model is used in the algorithm cope with the uncertainty in the plant model. Simulations show that the convergence speed is satisfactory regardless of whether or not the plant model is precise nonlinear plants. The simulation test verify the controlled system with random disturbance can reached to stability by using improved iterative learning control law but not the traditional control law.
Intervals in evolutionary algorithms for global optimization
Energy Technology Data Exchange (ETDEWEB)
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
多目标控制电梯群控调度算法的优化%Optimization of elevator group scheduling algorithm of multi-objective control
Institute of Scientific and Technical Information of China (English)
李强; 郑永康; 王瀚韬
2013-01-01
针对模糊推理获取电梯群控指标可信度时缺乏学习性的缺点,引入了人工鱼群算法用于决策函数的多元线性回归曲线的优化.呼梯信号和电梯运行信息经采集与计算,得到了厅层召唤等待时间(HCWT)、厅层召唤最大等待时间(maxHCWT)、剩余响应能力(CV)和召唤集中程度(GD)这4个输入变量,经模糊推理获取了平均候梯时间(AWT)、乘客长候梯率(LWP)和能量消耗(RNC)3个群控指标的可信度值,并以此为样本训练人工鱼群,获得了决策函数.经决策函数计算得到的群控指标可信度值,再与由客流交通模式决定的指标加权系数进行线性平均,以此作为目标评价函数进行电梯群控调度,实现了对电梯运行效率和节能的多目标控制.经电梯群控仿真平台评测,实验结果表明优化后的群控调度算法能明显改善群控指标.%Aiming at the shortcoming of lack of learning ability for fuzzy inference to obtain the index reliabilities of elevator group control,the artificial fish-swarm algorithm was led into optimize the multiple linear regression curve of the decision function.Four input variables:hall call wait time(HCWT),maximum of HCWT (maxHCWT),capacity of validation (CV)and group density (GD)could be gotten through the collection and calculation of call signals and running signals of elevators,and the three index reliabilities of elevator group control:average wait time (AWT),long-time wait percentage (LWP)and energy consumption (ENC)could be gained by fuzzy inference with these input variables.The artificial fish-swarm was trained with these three index reliabilities as samples to gain the decision function,which could be used to calculate the index reliabilities,and then be taken to linear weighted mean with the weighting coefficients which was determined by passenger traffic mode.This formula was taken as the objective evaluation function of elevator group control to reach the multi
Directory of Open Access Journals (Sweden)
Jianwen Guo
2016-01-01
Full Text Available All equipment must be maintained during its lifetime to ensure normal operation. Maintenance is one of the critical roles in the success of manufacturing enterprises. This paper proposed a preventive maintenance period optimization model (PMPOM to find an optimal preventive maintenance period. By making use of the advantages of particle swarm optimization (PSO and cuckoo search (CS algorithm, a hybrid optimization algorithm of PSO and CS is proposed to solve the PMPOM problem. The test functions show that the proposed algorithm exhibits more outstanding performance than particle swarm optimization and cuckoo search. Experiment results show that the proposed algorithm has advantages of strong optimization ability and fast convergence speed to solve the PMPOM problem.
Approximation algorithms for planning and control
Boddy, Mark; Dean, Thomas
1989-01-01
A control system operating in a complex environment will encounter a variety of different situations, with varying amounts of time available to respond to critical events. Ideally, such a control system will do the best possible with the time available. In other words, its responses should approximate those that would result from having unlimited time for computation, where the degree of the approximation depends on the amount of time it actually has. There exist approximation algorithms for a wide variety of problems. Unfortunately, the solution to any reasonably complex control problem will require solving several computationally intensive problems. Algorithms for successive approximation are a subclass of the class of anytime algorithms, algorithms that return answers for any amount of computation time, where the answers improve as more time is allotted. An architecture is described for allocating computation time to a set of anytime algorithms, based on expectations regarding the value of the answers they return. The architecture described is quite general, producing optimal schedules for a set of algorithms under widely varying conditions.
遗传算法优化的 MFAC 对电机速度控制的研究%Motor Speed Control with MFAC Optimized by Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
费盛; 肖爱平
2016-01-01
将无模型自适应控制算法应用于直流伺服电机的控制，此算法结构简单，控制精度高。在建立了电机的数学模型后，使用MATLAB进行算法仿真，并通过遗传算法对无模型自适应控制算法进行参数优化。在MATLAB仿真的基础上，进行了实验验证。实验结果表明，无模型控制算法应用于电机速度控制能取得良好的控制效果，利用遗传算法优化参数后的控制效果更好。%In this paper ,the model‐free adaptive control algorithm is applied to a DC servo motor ,this algorithm has the advantages of simple structure ,high control precision .After the establishment of the mathematical model of the motor ,the algorithm is simulated with MATLAB ,and the model‐free adaptive control algorithm parameters are optimized by genetic algorithm .On the basis of MATLAB simulation ,the effect of the algorithm is verified through experiment .The experimental results show that applying model‐free control algorithm to the motor speed control can achieve good control effect .The effect will be better after optimized by genetic algorithm .
Watermark Extraction Optimization Using PSO Algorithm
Directory of Open Access Journals (Sweden)
Mohammad Dehghani Soltani
2013-04-01
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.
Application of imperialist competitive optimization algorithm in power industry
Directory of Open Access Journals (Sweden)
Mohammad Shahrazad
2015-01-01
Full Text Available In future electricity industry transferring high quality of power is essential. In this case, using Flexible AC Transmission System (FACTS devices is inevitable. FACTS devices are used for controlling the voltage, stability, power flow and security of transmission lines. Therefore, finding the optimal locations for these devices in power networks is necessary. There are several varieties of FACTS devices with different characteristics, deployed for different purposes. Imperialist Competitive (IC algorithm is a recently developed optimization technique, applied in power systems. IC algorithm is a new heuristic approach for global optimization searches based on the concept of imperialistic competition. In this paper, an IEEE 4-bus system is deployed as a case study in order to demonstrate the results of this novel approach using MATLAB.
Optimization of machining processes using pattern search algorithm
Directory of Open Access Journals (Sweden)
Miloš Madić
2014-04-01
Full Text Available Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers and practitioners. This paper introduces the use of pattern search (PS algorithm, as a deterministic direct search optimization method, for solving machining optimization problems. To analyze the applicability and performance of the PS algorithm, six case studies of machining optimization problems, both single and multi-objective, were considered. The PS algorithm was employed to determine optimal combinations of machining parameters for different machining processes such as abrasive waterjet machining, turning, turn-milling, drilling, electrical discharge machining and wire electrical discharge machining. In each case study the optimization solutions obtained by the PS algorithm were compared with the optimization solutions that had been determined by past researchers using meta-heuristic algorithms. Analysis of obtained optimization results indicates that the PS algorithm is very applicable for solving machining optimization problems showing good competitive potential against stochastic direct search methods such as meta-heuristic algorithms. Specific features and merits of the PS algorithm were also discussed.
Applications of metaheuristic optimization algorithms in civil engineering
Kaveh, A
2017-01-01
The book presents recently developed efficient metaheuristic optimization algorithms and their applications for solving various optimization problems in civil engineering. The concepts can also be used for optimizing problems in mechanical and electrical engineering.
Lunar Habitat Optimization Using Genetic Algorithms
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
OPC recipe optimization using genetic algorithm
Asthana, Abhishek; Wilkinson, Bill; Power, Dave
2016-03-01
Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer's intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex. The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.
Temperature controller optimization by computational intelligence
Directory of Open Access Journals (Sweden)
Ćojbašić Žarko M.
2016-01-01
Full Text Available In this paper a temperature control system for an automated educational classroom is optimized with several advanced computationally intelligent methods. Controller development and optimization has been based on developed and extensively tested mathematical and simulation model of the observed object. For the observed object cascade P-PI temperature controller has been designed and conventionally tuned. To improve performance and energy efficiency of the system, several metaheuristic optimizations of the controller have been attempted, namely genetic algorithm optimization, simulated annealing optimization, particle swarm optimization and ant colony optimization. Efficiency of the best results obtained with proposed computationally intelligent optimization methods has been compared with conventional controller tuning. Results presented in this paper demonstrate that heuristic optimization of advanced temperature controller can provide improved energy efficiency along with other performance improvements and improvements regarding equipment wear. Not only that presented methodology provides for determination and tuning of the core controller, but it also allows that advanced control concepts such as anti-windup controller gain are optimized simultaneously, which is of significant importance since interrelation of all control system parameters has important influence on the stability and performance of the system as a whole. Based on the results obtained, general conclusions are presented indicating that meta-heuristic computationally intelligent optimization of heating, ventilation, and air conditioning control systems is a feasible concept with strong potential in providing improved performance, comfort and energy efficiency. [Projekat Ministarstva nauke Republike Srbije, br. TR 33047 i br. TR 35016
Celik, Yuksel; Ulker, Erkan
2013-01-01
Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm's performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
Optimization of Temperature Controller for Electric Furnace
Institute of Scientific and Technical Information of China (English)
2000-01-01
Genetic algorithms are based on the principle of natural selection and the optimization of natural generation. We can select the number of the bit strings and mutation rate reasonably, the global optimal solution can be obtained. GAs adopt the binary code as optimizing parameter and this binary code can be used in computer controller easily. This paper studies the application of the GAs to the electric furnace temperature control. When the electric furnace mathematics model varies with the working condition, the parameter of controller can be optimized on line. So the system performance can be improved effectively.
Directory of Open Access Journals (Sweden)
Shi-Zhou Xu
2015-01-01
Full Text Available The high-power three-level explosion-proof inverters demand high thermal stability of power devices, and a set of theories and methods is needed to achieve an accurate power-loss calculation of power devices, to establish heat dissipation model, and ultimately to reduce the power loss to improve thermal stability of system. In this paper, the principle of neutral point clamped three-level (NPC3L inverter is elaborated firstly, and a fourth-order RC equivalent circuit of IGBT is derived, on which basis the power-loss model of IGBT and the optimized maternal power-loss thermal model, using an optimized power-loss algorithm, are established. Secondly, in accordance with the optimized maternal power-loss thermal model, the generic formulas of power-loss calculation are deduced to calculate the power-loss modification values of NPC3L and soft switching three-level (S3L inverters, which will be the thermal sources during thermal analysis for maternal power-loss thermal models. Finally, the experiment conducted on the 2.1 MW experimental platform shows that S3L inverter has the same excellent output characteristics with NPC3L inverter, reduces the power loss significantly by 213 W in each half-bridge, and decreases the temperature by 10°C, coinciding with the theoretical calculation, which verifies the accuracy of optimized power-loss algorithm and the effectiveness of the improvement.
On Symmetries in Optimal Control
van der Schaft, A. J.
1986-01-01
We discuss the use of symmetries in solving optimal control problems. In particular a procedure for obtaining symmetries is given which can be performed before the actual calculation of the optimal control and optimal Hamiltonian.
On Symmetries in Optimal Control
Schaft, A.J. van der
1986-01-01
We discuss the use of symmetries in solving optimal control problems. In particular a procedure for obtaining symmetries is given which can be performed before the actual calculation of the optimal control and optimal Hamiltonian.
Optimal Genetic View Selection Algorithm for Data Warehouse
Institute of Scientific and Technical Information of China (English)
Wang Ziqiang; Feng Boqin
2005-01-01
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.
Deterministic oscillatory search: a new meta-heuristic optimization algorithm
Indian Academy of Sciences (India)
N ARCHANA; R VIDHYAPRIYA; ANTONY BENEDICT; KARTHIK CHANDRAN
2017-06-01
The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical methods of optimization and global converging characteristics of meta-heuristic algorithms. Common traits of nature-inspired algorithms like randomness and tuning parameters (other than population size) are eliminated. The proposed algorithm is tested with mathematical benchmark functions and compared to other popular optimization algorithms. Theresults show that the proposed algorithm is superior in terms of robustness and problem solving capabilities to other algorithms. The paradigm is also applied to an engineering problem to prove its practicality. It is applied to find the optimal location of multi-type FACTS devices in a power system and tested in the IEEE 39 bus system and UPSEB 75 bus system. Results show better performance over other standard algorithms in terms of voltage stability, real power loss and sizing and cost of FACTS devices.
Efficient Genetic Algorithm sets for optimizing constrained building design problem
National Research Council Canada - National Science Library
Wright, Jonathan; Alajmi, Ali
2016-01-01
.... This requires trying large possible solutions which need heuristic optimization algorithms. A comparison between several heuristic optimization algorithms showed that Genetic Algorithm (GA) is robust on getting the optimum(s) simulation ( Wetter and Wright, 2004; Brownlee et al., 2011; Bichiou and Krarti, 2011; Sahu et al., 2012 ) while the building simulat...
A Hybrid Algorithm for Optimizing Multi- Modal Functions
Institute of Scientific and Technical Information of China (English)
Li Qinghua; Yang Shida; Ruan Youlin
2006-01-01
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.
Enhanced Bee Colony Algorithm for Complex Optimization Problems
Directory of Open Access Journals (Sweden)
S.Suriya
2012-01-01
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.
Optimal Control of Evolution Mixed Variational Inclusions
Energy Technology Data Exchange (ETDEWEB)
Alduncin, Gonzalo, E-mail: alduncin@geofisica.unam.mx [Universidad Nacional Autónoma de México, Departamento de Recursos Naturales, Instituto de Geofísica (Mexico)
2013-12-15
Optimal control problems of primal and dual evolution mixed variational inclusions, in reflexive Banach spaces, are studied. The solvability analysis of the mixed state systems is established via duality principles. The optimality analysis is performed in terms of perturbation conjugate duality methods, and proximation penalty-duality algorithms to mixed optimality conditions are further presented. Applications to nonlinear diffusion constrained problems as well as quasistatic elastoviscoplastic bilateral contact problems exemplify the theory.
Directory of Open Access Journals (Sweden)
Hyo Seon Park
2014-01-01
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.
Adaptive-feedback control algorithm.
Huang, Debin
2006-06-01
This paper is motivated by giving the detailed proofs and some interesting remarks on the results the author obtained in a series of papers [Phys. Rev. Lett. 93, 214101 (2004); Phys. Rev. E 71, 037203 (2005); 69, 067201 (2004)], where an adaptive-feedback algorithm was proposed to effectively stabilize and synchronize chaotic systems. This note proves in detail the strictness of this algorithm from the viewpoint of mathematics, and gives some interesting remarks for its potential applications to chaos control & synchronization. In addition, a significant comment on synchronization-based parameter estimation is given, which shows some techniques proposed in literature less strict and ineffective in some cases.
Optimized joystick controller.
Ding, D; Cooper, R A; Spaeth, D
2004-01-01
The purpose of the study was to develop an optimized joystick control interface for electric powered wheelchairs and thus provide safe and effective control of electric powered wheelchairs to people with severe physical disabilities. The interface enables clinicians to tune joystick parameters for each individual subject through selecting templates, dead zones, and bias axes. In terms of hand tremor usually associated with people with traumatic brain injury, cerebral palsy, and multiple sclerosis, fuzzy logic rules were applied to suppress erratic hand movements and extract the intended motion from the joystick. Simulation results were presented to show the graphical tuning interface as well as the performance of the fuzzy logic controller.
Numerical methods for control optimization in linear systems
Tyatyushkin, A. I.
2015-05-01
Numerical methods are considered for solving optimal control problems in linear systems, namely, terminal control problems with control and phase constraints and time-optimal control problems. Several algorithms with various computer storage requirements are proposed for solving these problems. The algorithms are intended for finding an optimal control in linear systems having certain features, for example, when the reachable set of a system has flat faces.
Directory of Open Access Journals (Sweden)
M. Kandasamy
2014-12-01
Full Text Available A comparison study of Firefly Algorithm (FA and Bacterial Foraging Algorithm (BFO optimization is carried out by applying them to a Non Linear pH neutralization process. In process control engineering, the Proportional, Derivative, Integral controller tuning parameters are deciding the performance of the controller to ensure the good performance of the plant. The FA and BFO algorithms are applied to obtain the optimum values of controller parameters. The performance indicators such as servo response and regulatory response tests are carried out to evaluate the efficiency of the heuristic algorithm based controllers. The error minimization criterion such as Integral Absolute Error (IAE, Integral Square Error (ISE, Integral Time Square Error (ITSE, Integral Time Absolute Error (ITAE and Time domain specifications – rise time, Peak Overshoot and settling time are considered for the study of the performance of the controllers. The study indicates that, FA tuned PID controller provides marginally better set point tracking, load disturbance rejection, time domain specifications and error minimization for the Non Linear pH neutralization process compared to BFO tuned PID controller.
Niching genetic algorithms for optimization in electromagnetics - I. Fundamentals
Sareni, Bruno; Krähenbühl, Laurent; Nicolas, Alain
1998-01-01
Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization.
A Hybrid Aggressive Space Mapping Algorithm for EM Optimization
DEFF Research Database (Denmark)
Bakr, M.; Bandler, J. W.; Georgieva, N.;
1999-01-01
We present a novel, Hybrid Aggressive Space Mapping (HASM) optimization algorithm. HASM is a hybrid approach exploiting both the Trust Region Aggressive Space Mapping (TRASM) algorithm and direct optimization. It does not assume that the final space-mapped design is the true optimal design and is...
A new multiobjective performance criterion used in PID tuning optimization algorithms.
Sahib, Mouayad A; Ahmed, Bestoun S
2016-01-01
In PID controller design, an optimization algorithm is commonly employed to search for the optimal controller parameters. The optimization algorithm is based on a specific performance criterion which is defined by an objective or cost function. To this end, different objective functions have been proposed in the literature to optimize the response of the controlled system. These functions include numerous weighted time and frequency domain variables. However, for an optimum desired response it is difficult to select the appropriate objective function or identify the best weight values required to optimize the PID controller design. This paper presents a new time domain performance criterion based on the multiobjective Pareto front solutions. The proposed objective function is tested in the PID controller design for an automatic voltage regulator system (AVR) application using particle swarm optimization algorithm. Simulation results show that the proposed performance criterion can highly improve the PID tuning optimization in comparison with traditional objective functions.
PDE Nozzle Optimization Using a Genetic Algorithm
Billings, Dana; Turner, James E. (Technical Monitor)
2000-01-01
Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.
Evolutionary Algorithm Geometry Optimization of Optical Antennas
Directory of Open Access Journals (Sweden)
Ramón Díaz de León-Zapata
2016-01-01
Full Text Available Printed circuit antennas have been used for the detection of electromagnetic radiation at a wide range of frequencies that go from radio frequencies (RF up to optical frequencies. The design of printed antennas at optical frequencies has been done by using design rules derived from the radio frequency domain which do not take into account the dispersion of material parameters at optical frequencies. This can make traditional RF antenna design not suitable for optical antenna design. This work presents the results of using a genetic algorithm (GA for obtaining an optimized geometry (unconventional geometries that may be used as optical regime antennas to capture electromagnetic waves. The radiation patterns and optical properties of the GA generated geometries were compared with the conventional dipole geometry. The characterizations were conducted via finite element method (FEM computational simulations.
Institute of Scientific and Technical Information of China (English)
冷雪锋
2012-01-01
The algorithm of CNC press brake servo controller is optimized in this paper. And this paper studied three acceleration and deceleration algorithms on trapezium algorithm,index algorithm and sshape algorithm, and draws velocity - time curve by examples with Matlab simulation. S -shape algorithm is the best solution by comparing, and we have a in - depth research for s - shape algorithm. This paper provides a good solution on the design of CNC press brake servo controller.%对数控折弯机伺服控制器的算法进行了优化设计,研究了梯形算法、指数算法和S形算法.并通过实例用Matlab仿真绘出速度-时间曲线,通过比较给出S形算法是最佳方案,并对S形算法深入研究.为数控折弯机伺服控制器提供了很好的设计方案.
Multiple Objective Optimization and Optimal Control of Fermentation Processes
Directory of Open Access Journals (Sweden)
Mitko Petrov
2008-10-01
Full Text Available A multiple objective optimization is applied for finding an optimum policy of fed-batch processes of whey fermentation and L-lysine production. The multiple objective optimization problems are transformed to a standard problem of optimization with single objective function by a general utility function with weight coefficients for each single utility coefficient criteria. A combined algorithm is applied when solving the maximizing decision problem. The algorithm includes a method for random search of finding an initial point and a method based on the fuzzy sets theory, combined in order to find the best solution of the optimization problem. The application of the combined algorithm eliminates the main disadvantage of the used fuzzy optimization method, namely it decreases the number of discrete values of the control variables. Thus, the algorithm allows problems with larger scale to be solved. After this multiple optimization, the useful product quality rises and the residual substrate concentration at the end of the process decreases. In this way, the process productivity is increased.
DEFF Research Database (Denmark)
Awasthi, Abhishek; Venkitusamy, Karthikeyan; Padmanaban, Sanjeevikumar
2017-01-01
, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site...... and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm...... optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality....
Artificial bee colony algorithm variants on constrained optimization
National Research Council Canada - National Science Library
Bahriye Akay; Dervis Karaboga
2017-01-01
.... In this study, the performance analysis of artificial bee colony algorithm (ABC), one of the intelligent optimization techniques, is examined on constrained problems and the effect of some modifications on the performance of the algorithm is examined...
Zhu, Jun; Yan, Xuefeng; Zhao, Weixiang
2013-10-01
To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process. The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem.
Efficient Algorithms for Optimal 4-Bit Reversible Logic System Synthesis
Directory of Open Access Journals (Sweden)
Zhiqiang Li
2013-01-01
Full Text Available Owing to the exponential nature of the memory and run-time complexity, many methods can only synthesize 3-bit reversible circuits and cannot synthesize 4-bit reversible circuits well. We mainly absorb the ideas of our 3-bit synthesis algorithms based on hash table and present the efficient algorithms which can construct almost all optimal 4-bit reversible logic circuits with many types of gates and at mini-length cost based on constructing the shortest coding and the specific topological compression; thus, the lossless compression ratio of the space of n-bit circuits reaches near 2×n!. This paper presents the first work to create all 3120218828 optimal 4-bit reversible circuits with up to 8 gates for the CNT (Controlled-NOT gate, NOT gate, and Toffoli gate library, and it can quickly achieve 16 steps through specific cascading created circuits.
Distributed Random Access Algorithm: Scheduling and Congesion Control
Jiang, Libin; Shin, Jinwoo; Walrand, Jean
2009-01-01
This paper provides proofs of the rate stability, Harris recurrence, and epsilon-optimality of CSMA algorithms where the backoff parameter of each node is based on its backlog. These algorithms require only local information and are easy to implement. The setup is a network of wireless nodes with a fixed conflict graph that identifies pairs of nodes whose simultaneous transmissions conflict. The paper studies two algorithms. The first algorithm schedules transmissions to keep up with given arrival rates of packets. The second algorithm controls the arrivals in addition to the scheduling and attempts to maximize the sum of the utilities of the flows of packets at the different nodes. For the first algorithm, the paper proves rate stability for strictly feasible arrival rates and also Harris recurrence of the queues. For the second algorithm, the paper proves the epsilon-optimality. Both algorithms operate with strictly local information in the case of decreasing step sizes, and operate with the additional info...
Genetic algorithm and particle swarm optimization combined with Powell method
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
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.
Optimal impulse control problems and linear programming.
Bauso, D.
2009-01-01
Optimal impulse control problems are, in general, difficult to solve. A current research goal is to isolate those problems that lead to tractable solutions. In this paper, we identify a special class of optimal impulse control problems which are easy to solve. Easy to solve means that solution algorithms are polynomial in time and therefore suitable to the on-line implementation in real-time problems. We do this by using a paradigm borrowed from the Operations Research field. As main result, ...
A Distributed Algorithm for Energy Optimization in Hydraulic Networks
DEFF Research Database (Denmark)
Kallesøe, Carsten; Wisniewski, Rafal; Jensen, Tom Nørgaard
2014-01-01
is distributed in the sense that all calculations are implemented where the necessary information is available, including both parameters and measurements. A communication network between the pumps is implemented for global optimization. The local implementation of the algorithm means that the system becomes......An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm...... a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation....
Practice Utilization of Algorithms for Analog Filter Group Delay Optimization
Directory of Open Access Journals (Sweden)
K. Hajek
2007-04-01
Full Text Available This contribution deals with an application of three different algorithms which optimize a group delay of analog filters. One of them is a part of the professional circuit simulator Micro Cap 7 and the others two original algorithms are developed in the MATLAB environment. An all-pass network is used to optimize the group delay of an arbitrary analog filter. Introduced algorithms look for an optimal order and optimal coefficients of an all-pass network transfer function. Theoretical foundations are introduced and all algorithms are tested using the optimization of testing analog filter. The optimization is always run three times because the second, third and fourth-order all-pass network is used. An equalization of the original group delay is a main objective of these optimizations. All outputs of all algorithms are critically evaluated and also described.
Adaptive Central Force Optimization Algorithm Based on the Stability Analysis
Directory of Open Access Journals (Sweden)
Weiyi Qian
2015-01-01
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.
Directory of Open Access Journals (Sweden)
Mohammad Marefati
2016-06-01
Full Text Available In this article, an optimized PID controller for a fuel cell is introduced. It should be noted that we did not compute the PID controller’s coefficients based on trial-and-error method; instead, imperialist competitive algorithms have been considered. At first, the problem will be formulated as an optimization problem and solved by the mentioned algorithm, and optimized results will be obtained for PID coefficients. Then one of the important kinds of fuel cells, called proton exchange membrane fuel cell, is introduced. In order to control the voltage of this fuel cell during the changes in the charges, an optimal controller is introduced, based on the imperialist competitive algorithm. In order to apply this algorithm, the problem is written as an optimization problem which includes objectives and constraints. To achieve the most desirable controller, this algorithm is used for problem solving. Simulations confirm the better performance of proposed PID controller.
Improved hybrid optimization algorithm for 3D protein structure prediction.
Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang
2014-07-01
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.
Accelerating ATM Optimization Algorithms Using High Performance Computing Hardware Project
National Aeronautics and Space Administration — NASA is developing algorithms and methodologies for efficient air-traffic management (ATM). Several researchers have adopted an optimization framework for solving...
An Effective Hybrid Optimization Algorithm for Capacitated Vehicle Routing Problem
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Capacitated vehicle routing problem (CVRP) is an important combinatorial optimization problem. However, it is quite difficult to achieve an optimal solution with the traditional optimization methods owing to the high computational complexity. A hybrid algorithm was developed to solve the problem, in which an artificial immune clonal algorithm (AICA) makes use of the global search ability to search the optimal results and simulated annealing (SA) algorithm employs certain probability to avoid becoming trapped in a local optimum. The results obtained from the computational study show that the proposed algorithm is a feasible and effective method for capacitated vehicle routing problem.
Accelerating ATM Optimization Algorithms Using High Performance Computing Hardware Project
National Aeronautics and Space Administration — NASA is developing algorithms and methodologies for efficient air-traffic management. Several researchers have adopted an optimization framework for solving problems...
Study on optimization control method based on artificial neural network
Institute of Scientific and Technical Information of China (English)
FU Hua; SUN Shao-guang; XU Zhen-Iiang
2005-01-01
In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
Optimal Control of Switched Systems based on Bezier Control Points
Directory of Open Access Journals (Sweden)
FatemeGhomanjani
2012-06-01
Full Text Available This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into k sub-intervals. Second, the trajectory and control functions are approximatedby Bezier curves in each subinterval. Bezier curves have been considered as piecewise polynomials of degree n, then they will be determined by n+1 control points on any subinterval. The optimal control problem is there by converted into a nonlinear programming problem (NLP, which can be solved by known algorithms. However in this paper the MATLAB optimization routine FMINCON is used for solving resulting NLP.
Composite multiobjective optimization beamforming based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
Shi Jing; Meng Weixiao; Zhang Naitong; Wang Zheng
2006-01-01
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.
Function Optimization Based on Quantum Genetic Algorithm
Ying Sun; Yuesheng Gu; Hegen Xiong
2013-01-01
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...
Multiple-Agent Task Allocation Algorithm Utilizing Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Kai Zhao
2013-11-01
Full Text Available Task allocation in multiple agent system has been widely applied many application fields, such as unmanned aerial vehicle, multi-robot system and manufacturing system et al. Therefore, it becomes one of the hot topics in distributed artificial intelligence research field for several years. Therefore, in this paper, we propose a novel task allocation algorithm in multiple agent systems utilizing ant colony optimization. Firstly, the basic structure of agent organization is described, which include context-aware module, information processing module, the executing module, decision-making and intelligent control module, knowledge base and task table. Based the above agent structure, these module utilize the knowledge in the external environment to process the information in agent communicating. Secondly, we point out that task allocation process in multiple agent systems can be implement by creating the space to the mapping of the multi-agent organization. Thirdly, a modified multiple agent system oriented ant colony optimization algorithm is given, which contain pre-processing steps and the task allocation results are obtained by executing the trust region sqp algorithm in local solver. Finally, performance evaluation is conducted by experiments comparing with Random strategy and Instant optimal strategy, and very positive results are obtained
Institute of Scientific and Technical Information of China (English)
刘朝华; 张英杰; 章兢; 吴建辉
2011-01-01
The active disturbance rejection control (ADRC) has the property of requiring no knowledge about the precise mathematical model, but the parameters of controller is difficult to be tuned. An active disturbance rejection control based on immune binary-state particle swarm optimization algorithm (IBPSO-ADRC) is proposed, which takes advantage of the combination of the artificial immune systems (AIS) and particle swarm optimization (PSO). The proposed algorithm is applied to optimize the parameters of ADRC and then to control chaotic system. Furthermore, a new ADRC for the chaotic system is constructed. The simulation experiments indicate that the scheme requires no knowledge about the mathematical model with fast response speed, while restraining the parameter perturbation of chaotic system effectively and is robust to noise.%利用人工免疫算法及粒子群优化算法融合的优点,提出了一种免疫双态微粒群算法(immune binarystate particle swarm optimization,IBPSO)的自抗扰控制器(IBPSO-ADRC),应用于混沌系统控制,构建一种混沌系统自抗扰控制系统.实验研究表明:该控制方法无需了解动态系统精确模型,具有响应速度快,有效抑制混沌系统参数摄动及较强抗干扰能力的特点.
Optimal Control of Active Recoil Mechanisms
1977-02-01
pressures in different chambers, rod pull are available and can be plotted. A linear state feedback control system is proposed to adapt this...desirable. A linear state feedback control system with variable gains is proposed in the report. A separate control law is designed for each...optimization algorithm to choose a feasible solution. 27 3.3 Results for M-37 Recoil Mechanism The linear state feedback control system and
Educational Tool for Optimal Controller Tuning Using Evolutionary Strategies
Carmona Morales, D.; Jimenez-Hornero, J. E.; Vazquez, F.; Morilla, F.
2012-01-01
In this paper, an optimal tuning tool is presented for control structures based on multivariable proportional-integral-derivative (PID) control, using genetic algorithms as an alternative to traditional optimization algorithms. From an educational point of view, this tool provides students with the necessary means to consolidate their knowledge on…
Nonlinear system identification and control using state transition algorithm
Yang, Chunhua; Gui, Weihua
2012-01-01
This paper presents a novel optimization method named state transition algorithm (STA) to solve the problem of identification and control for nonlinear system. In the proposed algorithm, a solution to optimization problem is considered as a state, and the updating of a solution equates to the process of state transition, which makes the STA easy to understand and convenient to be implemented. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results demonstrate the validity of the methodology, and comparison to STA with other optimization algorithms confirms that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence speed and more stable performance.
Algorithm for correcting optimization convergence errors in Eclipse.
Zacarias, Albert S; Mills, Michael D
2009-10-14
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.
基于优化启动控制算法的电机驱动电路设计%Motor Drive Circuit Design Based on Optimization Start Control Algorithm
Institute of Scientific and Technical Information of China (English)
张福强
2013-01-01
针对复杂电机电路的驱动过程较为复杂、系统集成度高的特点，提出基于优化启动控制算法的电机驱动电路设计思路。通过建立电机的工作模型，设计了一种优化启动控制算法。该算法可以克服电机启动过程中，结构复杂带来的启动较慢的弊端；运用角速度和加速度的优化控制，解决了电机启动中的各项问题，使电机性能达到最优。计算机仿真实验结果表明，该算法能够实现电机的精确控制，优化效果明显。% According to the complex motor circuit driver process is relatively complex, system integration high character-istic, based on optimization start control algorithm of motor drive circuit design ideas, through the establishment of the motor working model. Designed a kind of optimization start control algorithm, can overcome the motor starting process, complicated structure brings the disadvantages of slow start. Using the angular velocity and acceleration, and the opti-mization control, solve the problem of the motor start, which makes the motor to achieve optimal performance. The com-puter simulation results show that the algorithm can realize accurate control of the motor and the optimization effect is ob-vious.
Directory of Open Access Journals (Sweden)
D. Venugopal
2015-04-01
Full Text Available This paper proposes optimal location of FACTS devices in power system using Evolutionary algorithms. The location of FACTS controllers, their type and rated values are optimized simultaneously by using the proposed algorithm. From the FACTS devices family, shunt device Static Var Compensator (SVC is considered. The proposed BAT algorithm is a very effective method for the optimal choice and placement of SVC device to improve the Voltage profile of power systems. The proposed algorithm has been applied to IEEE 30 bus system.
NOVEL POWER CONTROL GAME VIA PRICING ALGORITHM FOR COGNITIVE RADIOS
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
To compensate the service providers who have paid billions of dollars to use spectrum and to satisfy secondary users’requirements in cognitive radios,a Non-cooperative Power Control Game and Pricing algorithm (NPGP) is proposed. Simulation results show that the proposed algorithm can regulate the secondary users’transmitter powers,optimally allocate radio resource and increase the total throughput effectively.
Algorithmic Aspects of Several Data Transfer Service Optimization Problems
Andreica, Mugurel Ionut; Ionescu, Florin; Andreica, Cristina Teodora
2009-01-01
Optimized data transfer services are highly demanded nowadays, due to the large amounts of data which are frequently being produced and accessed. In this paper we consider several data transfer service optimization problems (optimal server location in path networks, optimal packet sequencing and minimum makespan packet scheduling), for which we provide novel algorithmic solutions.
Optimal control for chemical engineers
Upreti, Simant Ranjan
2013-01-01
Optimal Control for Chemical Engineers gives a detailed treatment of optimal control theory that enables readers to formulate and solve optimal control problems. With a strong emphasis on problem solving, the book provides all the necessary mathematical analyses and derivations of important results, including multiplier theorems and Pontryagin's principle.The text begins by introducing various examples of optimal control, such as batch distillation and chemotherapy, and the basic concepts of optimal control, including functionals and differentials. It then analyzes the notion of optimality, de
Genetic Algorithm Based Proportional Integral Controller Design for Induction Motor
Directory of Open Access Journals (Sweden)
Mohanasundaram Kuppusamy
2011-01-01
Full Text Available Problem statement: This study has expounded the application of evolutionary computation method namely Genetic Algorithm (GA for estimation of feedback controller parameters for induction motor. GA offers certain advantages such as simple computational steps, derivative free optimization, reduced number of iterations and assured near global optima. The development of the method is well documented and computed and measured results are presented. Approach: The design of PI controller parameter for three phase induction motor drives was done using Genetic Algorithm. The objective function of motor current reduction, using PI controller, at starting is formulated as an optimization problem and solved with Genetic Algorithm. Results: The results showed the selected values of PI controller parameter using genetic algorithm approach, with objective of induction motor starting current reduction. Conclusions/Recommendation: The results proved the robustness and easy implementation of genetic algorithm selection of PI parameters for induction motor starting.
Optimization of machining processes using pattern search algorithm
Miloš Madić; Miroslav Radovanović
2014-01-01
Optimization of machining processes not only increases machining efficiency and economics, but also the end product quality. In recent years, among the traditional optimization methods, stochastic direct search optimization methods such as meta-heuristic algorithms are being increasingly applied for solving machining optimization problems. Their ability to deal with complex, multi-dimensional and ill-behaved optimization problems made them the preferred optimization tool by most researchers a...
Automatic Circuit Design and Optimization Using Modified PSO Algorithm
Directory of Open Access Journals (Sweden)
Subhash Patel
2016-04-01
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.
Solution of transient optimization problems by using an algorithm based on nonlinear programming
Teren, F.
1977-01-01
A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.
Solution of transient optimization problems by using an algorithm based on nonlinear programming
Teren, F.
1977-01-01
A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.
Directory of Open Access Journals (Sweden)
Yamina BOUGHARI
2017-06-01
Full Text Available In this paper the Cessna Citation X clearance criteria were evaluated for a new Flight Controller. The Flight Control Law were optimized and designed for the Cessna Citation X flight envelope by combining the Deferential Evolution algorithm, the Linear Quadratic Regulator method, and the Proportional Integral controller during a previous research presented in part 1. The optimal controllers were used to reach satisfactory aircraft’s dynamic and safe flight operations with respect to the augmentation systems’ handling qualities, and design requirements. Furthermore the number of controllers used to control the aircraft in its flight envelope was optimized using the Linear Fractional Representations features. To validate the controller over the whole aircraft flight envelope, the linear stability, eigenvalue, and handling qualities criteria in addition of the nonlinear analysis criteria were investigated during this research to assess the business aircraft for flight control clearance and certification. The optimized gains provide a very good stability margins as the eigenvalue analysis shows that the aircraft has a high stability, and a very good flying qualities of the linear aircraft models are ensured in its entire flight envelope, its robustness is demonstrated with respect to uncertainties due to its mass and center of gravity variations.
Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm
De, Bishnu P.; Kar, Rajib; Mandal, Durbadal; Ghoshal, Sakti P.
2015-07-01
The inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds' flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.
Constrained Multi-Level Algorithm for Trajectory Optimization
Adimurthy, V.; Tandon, S. R.; Jessy, Antony; Kumar, C. Ravi
The emphasis on low cost access to space inspired many recent developments in the methodology of trajectory optimization. Ref.1 uses a spectral patching method for optimization, where global orthogonal polynomials are used to describe the dynamical constraints. A two-tier approach of optimization is used in Ref.2 for a missile mid-course trajectory optimization. A hybrid analytical/numerical approach is described in Ref.3, where an initial analytical vacuum solution is taken and gradually atmospheric effects are introduced. Ref.4 emphasizes the fact that the nonlinear constraints which occur in the initial and middle portions of the trajectory behave very nonlinearly with respect the variables making the optimization very difficult to solve in the direct and indirect shooting methods. The problem is further made complex when different phases of the trajectory have different objectives of optimization and also have different path constraints. Such problems can be effectively addressed by multi-level optimization. In the multi-level methods reported so far, optimization is first done in identified sub-level problems, where some coordination variables are kept fixed for global iteration. After all the sub optimizations are completed, higher-level optimization iteration with all the coordination and main variables is done. This is followed by further sub system optimizations with new coordination variables. This process is continued until convergence. In this paper we use a multi-level constrained optimization algorithm which avoids the repeated local sub system optimizations and which also removes the problem of non-linear sensitivity inherent in the single step approaches. Fall-zone constraints, structural load constraints and thermal constraints are considered. In this algorithm, there is only a single multi-level sequence of state and multiplier updates in a framework of an augmented Lagrangian. Han Tapia multiplier updates are used in view of their special role in
Power, control and optimization
Vasant, Pandian; Barsoum, Nader
2013-01-01
The book consists of chapters based on selected papers of international conference „Power, Control and Optimization 2012”, held in Las Vegas, USA. Readers can find interesting chapters discussing various topics from the field of power control, its distribution and related fields. Book discusses topics like energy consumption impacted by climate, mathematical modeling of the influence of thermal power plant on the aquatic environment, investigation of cost reduction in residential electricity bill using electric vehicle at peak times or allocation and size evaluation of distributed generation using ANN model and others. Chapter authors are to the best of our knowledge the originators or closely related to the originators of presented ideas and its applications. Hence, this book certainly is one of the few books discussing the benefit from intersection of those modern and fruitful scientific fields of research with very tight and deep impact on real life and industry. This book is devoted to the studies o...
Institute of Scientific and Technical Information of China (English)
毛书军; 盛贤君
2014-01-01
In order to solve the challenging problem of optimization of five-dimensional parameters in fractional PID controller, based on the introduction of swarm intelligence algorithm and evolutionary computing, a hybrid computation intelligent learning algorithm was proposed, which combined Glowworm Swarm Optimization ( GSO) with Genetic Algorithm ( GA) . The hybrid algorithm was based on the swarm intelligence and individual evolution of creatures, which can greatly increase the accuracy of optimization and ensure that algorithm evolves to optimum. A series of experiments verify that the proposed hybrid algorithm can shorten the time of computing and increase the accuracy of simulation.%为解决分数阶PID控制器五维参数优化的难题，设计了一种把萤火虫算法和遗传算法相结合的混合计算智能算法，阐述了计算智能中的群智能算法和进化计算的基本原理和数学算法。该方法基于生物的群体智能和个体进化相结合的思想，能够有效地提高寻优精度，并使算法向最优方向不断进化。经过仿真验证，混合算法在分数阶PID参数整定方面具有运算时间短、仿真精度高等优点。
基于遗传算法的自抗扰炮控系统优化设计%Design of Optimized ADRC Gun Control System Based on Generic Algorithm
Institute of Scientific and Technical Information of China (English)
徐振辉
2012-01-01
针对自抗扰炮控系统调节参数较多,且依赖经验选取参数的局限性,提出一种基于遗传算法的自抗扰炮控系统优化设计方法.本文详细介绍炮控系统模型及遗传算法优化自抗扰(ADRC)炮控系统的步骤.最后,通过数值仿真结果表明:采用遗传算法优化后的自抗扰炮控系统具有良好的瞬态性能和稳态性能,响应时间短,超调量小,控制效果理想.%Aiming at the problem of numbers of adjust parameters which is selected depend on experience in ADRC gun control system, a method about the optimized design of ADRC gun control system based on generic algorithm is proposed. The paper introduces the mathematical model of gun control system and the steps of optimizing design parameters in ADRC gun control system based on generic algorithm. At last, the simulation result indicates that the optimized ADRC gun control system shows well dynamic and steady-state performances, short adjusting time and nearly have no excess adjustment.
Towards Automatic Controller Design using Multi-Objective Evolutionary Algorithms
DEFF Research Database (Denmark)
Pedersen, Gerulf
of evolutionary computation, a choice was made to use multi-objective algorithms for the purpose of aiding in automatic controller design. More specifically, the choice was made to use the Non-dominated Sorting Genetic Algorithm II (NSGAII), which is one of the most potent algorithms currently in use......, as the foundation for achieving the desired goal. While working with the algorithm, some issues arose which limited the use of the algorithm for unknown problems. These issues included the relative scale of the used fitness functions and the distribution of solutions on the optimal Pareto front. Some work has...
Application of Bees Algorithm in Multi-Join Query Optimization
Directory of Open Access Journals (Sweden)
Mohammad Alamery
2012-09-01
Full Text Available Multi-join query optimization is an important technique for designing and implementing database management system. It is a crucial factor that affects the capability of database. This paper proposes a Bees algorithm that simulates the foraging behavior of honey bee swarm to solve Multi-join query optimization problem. The performance of the Bees algorithm and Ant Colony Optimization algorithm are compared with respect to computational time and the simulation result indicates that Bees algorithm is more effective and efficient.
General Object-oriented Framework for Iterative Optimization Algorithms
Mornar, Vedran; Vanjak, Zvonimir
2001-01-01
It is usually impossible to exactly solve the hard optimization problems. One is thus directed to iterative algorithms. In implementation of these iterative algorithms, some common characteristics can be observed, which can be generalized in an object-oriented framework. This can significantly reduce the time needed for implementation of an iterative algorithm.
Fuzzy Control of Chaotic System with Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang
2002-01-01
A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.
Biology-Derived Algorithms in Engineering Optimization
Yang, Xin-She
2010-01-01
Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.
Optimized algorithm for balancing clusters in wireless sensor networks
Institute of Scientific and Technical Information of China (English)
Mucheol KIM; Sun-hong KIM; Hyungjin BYUN; Sang-yong HAN
2009-01-01
Wireless sensor networks consist of hundreds or thousands of sensor nodes that involve numerous restrictions including computation capability and battery capacity. Topology control is an important issue for achieving a balanced placement of sensor nodes. The clustering scheme is a widely known and efficient means of topology control for transmitting information to the base station in two hops. The automatic routing scheme of the self-organizing technique is another critical element of wireless sensor networks. In this paper we propose an optimal algorithm with cluster balance taken into consideration, and compare it with three well known and widely used approaches, I.e., LEACH, MEER, and VAP-E, in performance evaluation. Experimental results show that the proposed approach increases the overall network lifetime, indicating that the amount of energy required for communication to the base station will be reduced for locating an optimal cluster.
Hybrid Algorithm for Optimal Load Sharing in Grid Computing
Directory of Open Access Journals (Sweden)
A. Krishnan
2012-01-01
Full Text Available Problem statement: Grid Computing is the fast growing industry, which shares the resources in the organization in an effective manner. Resource sharing requires more optimized algorithmic structure, otherwise the waiting time and response time are increased and the resource utilization is reduced. Approach: In order to avoid such reduction in the performances of the grid system, an optimal resource sharing algorithm is required. In recent days, many load sharing technique are proposed, which provides feasibility but there are many critical issues are still present in these algorithms. Results: In this study a hybrid algorithm for optimization of load sharing is proposed. The hybrid algorithm contains two components which are Hash Table (HT and Distributed Hash Table (DHT. Conclusion: The results of the proposed study show that the hybrid algorithm will optimize the task than existing systems.
Teaching learning based optimization algorithm and its engineering applications
Rao, R Venkata
2016-01-01
Describing a new optimization algorithm, the “Teaching-Learning-Based Optimization (TLBO),” in a clear and lucid style, this book maximizes reader insights into how the TLBO algorithm can be used to solve continuous and discrete optimization problems involving single or multiple objectives. As the algorithm operates on the principle of teaching and learning, where teachers influence the quality of learners’ results, the elitist version of TLBO algorithm (ETLBO) is described along with applications of the TLBO algorithm in the fields of electrical engineering, mechanical design, thermal engineering, manufacturing engineering, civil engineering, structural engineering, computer engineering, electronics engineering, physics and biotechnology. The book offers a valuable resource for scientists, engineers and practitioners involved in the development and usage of advanced optimization algorithms.
A FUZZY CLOPE ALGORITHM AND ITS OPTIMAL PARAMETER CHOICE
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
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.
A NEW FAMILY OF TRUST REGION ALGORITHMS FOR UNCONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Yuhong Dai; Dachuan Xu
2003-01-01
Trust region (TR) algorithms are a class of recently developed algorithms for nonlinear optimization. A new family of TR algorithms for unconstrained optimization, which is the extension of the usual TR method, is presented in this paper. When the objective function is bounded below and continuously. differentiable, and the norm of the Hesse approximations increases at most linearly with the iteration number, we prove the global convergence of the algorithms. Limited numerical results are reported, which indicate that our new TR algorithm is competitive.
Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem
Rahmalia, Dinita
2017-08-01
Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.
An optimal scheduling algorithm based on task duplication
Institute of Scientific and Technical Information of China (English)
Ruan Youlin; Liu Gan; Zhu Guangxi; Lu Xiaofeng
2005-01-01
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.
Coil optimization for electromagnetic levitation using a genetic like algorithm
Royer, Z. L.; Tackes, C.; LeSar, R.; Napolitano, R. E.
2013-06-01
The technique of electromagnetic levitation (EML) provides a means for thermally processing an electrically conductive specimen in a containerless manner. For the investigation of metallic liquids and related melting or freezing transformations, the elimination of substrate-induced nucleation affords access to much higher undercooling than otherwise attainable. With heating and levitation both arising from the currents induced by the coil, the performance of any EML system depends on controlling the balance between lifting forces and heating effects, as influenced by the levitation coil geometry. In this work, a genetic algorithm is developed and utilized to optimize the design of electromagnetic levitation coils. The optimization is targeted specifically to reduce the steady-state temperature of the stably levitated metallic specimen. Reductions in temperature of nominally 70 K relative to that obtained with the initial design are achieved through coil optimization, and the results are compared with experiments for aluminum. Additionally, the optimization method is shown to be robust, generating a small range of converged results from a variety of initial starting conditions. While our optimization criterion was set to achieve the lowest possible sample temperature, the method is general and can be used to optimize for other criteria as well.
Optimal Reactive Power Dispatch using Improved Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Hamid Falaghi
2014-12-01
Full Text Available Reactive power dispatch plays a key role in secure and economic operation of power systems. Optimal reactive power dispatch (ORPD is a non-linear optimization problem which includes both continues and discrete variables. Due to complex characteristics, heuristic and evolutionary based optimization approaches have become effective tools to solve the ORPD problem. In this paper, a new optimization approach based on improved differential evolution (IDE has been proposed to solve the ORPD problem. IDE is an improved version of differential evolution optimization algorithm in which new solutions are produced in respect to global best solution. In the proposed approach, IDE determines the optimal combination of control variables including generator voltages, transformer taps and setting of VAR compensation devices to obtain minimum real power losses. In order to demonstrate the applicability and efficiency of the proposed IDE based approach, it has been tested on the IEEE 14 and 57-bus test systems and obtained results are compared with those obtained using other existing methods. Simulation results show that the proposed approach is superior to the other existing methods.
Earth Observation Satellites Scheduling Based on Decomposition Optimization Algorithm
Directory of Open Access Journals (Sweden)
Feng Yao
2010-11-01
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.
Transonic Wing Shape Optimization Using a Genetic Algorithm
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
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.
Optimal control of radiator systems; Optimal reglering av radiatorsystem
Energy Technology Data Exchange (ETDEWEB)
Wollerstrand, J.; Ljunggren, P.; Johansson, P.O.
2007-07-01
This report presents results from a study aiming to considerably improve the development towards minimizing the primary return temperature from a district heating (DH) substation by optimizing the control algorithm for the space heating system. The investigation of this research field started about 20 years ago in Sweden when low flow operation of space heating systems was introduced. Following a couple of years of partly confused discussions, the method was accepted by many, but was rejected by others. Our thesis is that further improvement of cooling of DH water is possible when advanced, but robust, control algorithms are used for the space heating system. A space heating system is traditionally designed for a specific constant circulation flow combined with a suitable control curve for the space heating supply temperature as a function of the outdoor temperature. Optimal choice of the control curve varies from case to case and is an issue both we and others have dealt with in previous work. A large step was to derive theoretical control curves for optimal control of the space heating system, with an analysis of how temperature and circulation flow varies with heat load. The estimated gain varies strongly depending on the conditions, however, with realistic conditions it can be as much as 5 deg C decreased DH return temperature on yearly average. To be able to work properly under varying physical circumstances, a control algorithm must be able to combine variation of space heating supply temperature and circulation flow as a function of the heat load. By regulating the rotation speed of the circulation pump this can be achieved. Such regulation can be adjusted for each and every building by regulating a few parameters in a regulator. The results from this work are, that important theoretical knowledge has been completed, to show results systematically and to find support from practical experiments. A hands-on description of the method for optimizing DH water
A novel hybrid algorithm of GSA with Kepler algorithm for numerical optimization
Directory of Open Access Journals (Sweden)
Soroor Sarafrazi
2015-07-01
Full Text Available It is now well recognized that pure algorithms can be promisingly improved by hybridization with other techniques. One of the relatively new metaheuristic algorithms is Gravitational Search Algorithm (GSA which is based on the Newton laws. In this paper, to enhance the performance of GSA, a novel algorithm called “Kepler”, inspired by the astrophysics, is introduced. The Kepler algorithm is based on the principle of the first Kepler law. The hybridization of GSA and Kepler algorithm is an efficient approach to provide much stronger specialization in intensification and/or diversification. The performance of GSA–Kepler is evaluated by applying it to 14 benchmark functions with 20–1000 dimensions and the optimal approximation of linear system as a practical optimization problem. The results obtained reveal that the proposed hybrid algorithm is robust enough to optimize the benchmark functions and practical optimization problems.
An Algorithm for the Optimal Matching Speeds of Passenger and Freight Trains in Mixed Operations
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
This paper models the calculation of the optimal matching speeds of passenger and freight trains with various stage control methods for speed in mixed operations, presents a algorithm for the solution and justifies it with a practical example.
MDP Optimal Control under Temporal Logic Constraints
Ding, Xu Chu; Belta, Calin; Rus, Daniela
2011-01-01
In this paper, we develop a method to automatically generate a control policy for a dynamical system modeled as a Markov Decision Process (MDP). The control specification is given as a Linear Temporal Logic (LTL) formula over a set of propositions defined on the states of the MDP. We synthesize a control policy such that the MDP satisfies the given specification almost surely, if such a policy exists. In addition, we designate an "optimizing proposition" to be repeatedly satisfied, and we formulate a novel optimization criterion in terms of minimizing the expected cost in between satisfactions of this proposition. We propose a sufficient condition for a policy to be optimal, and develop a dynamic programming algorithm that synthesizes a policy that is optimal under some conditions, and sub-optimal otherwise. This problem is motivated by robotic applications requiring persistent tasks, such as environmental monitoring or data gathering, to be performed.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
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.
Resource Allocation in Public Cluster with Extended Optimization Algorithm
Akbar, Z.; Handoko, L. T.
2007-01-01
We introduce an optimization algorithm for resource allocation in the LIPI Public Cluster to optimize its usage according to incoming requests from users. The tool is an extended and modified genetic algorithm developed to match specific natures of public cluster. We present a detail analysis of optimization, and compare the results with the exact calculation. We show that it would be very useful and could realize an automatic decision making system for public clusters.
Genetic-Algorithm Tool For Search And Optimization
Wang, Lui; Bayer, Steven
1995-01-01
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.
TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization
2016-11-28
magnitude in computational experiments on portfolio optimization problems. The research on this topic has been published as [CG15a], where details can...AFRL-AFOSR-UK-TR-2017-0001 TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization Horst Hamacher Technische Universität...To) 15 May 2013 to 12 May 2016 4. TITLE AND SUBTITLE TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization 5a. CONTRACT
SNMP Based Network Optimization Technique Using Genetic Algorithms
Directory of Open Access Journals (Sweden)
M. Mohamed Surputheen
2012-03-01
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.
Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm
Directory of Open Access Journals (Sweden)
Kerim Guney
2015-01-01
Full Text Available An evolutionary method based on backtracking search optimization algorithm (BSA is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO, genetic algorithm (GA, modified touring ant colony algorithm (MTACO, quadratic programming method (QPM, bacterial foraging algorithm (BFA, bees algorithm (BA, clonal selection algorithm (CLONALG, plant growth simulation algorithm (PGSA, tabu search algorithm (TSA, memetic algorithm (MA, nondominated sorting GA-2 (NSGA-2, multiobjective differential evolution (MODE, decomposition with differential evolution (MOEA/D-DE, comprehensive learning PSO (CLPSO, harmony search algorithm (HSA, seeker optimization algorithm (SOA, and mean variance mapping optimization (MVMO. The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
to check if the network is controllable. Afterward the pressure control problem in water supply systems is formulated as an optimal control problem. The goal is to minimize the power consumption in pumps and also to regulate the pressure drop at the end-users to a desired value. The formulated optimal...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
Institute of Scientific and Technical Information of China (English)
吴剑威; 孔慧芳; 唐立新
2012-01-01
Most of the industrial process control still uses PID (Proportion Integration Differentiation) controller.However,because the traditional PID controller is time-varying nonlinear system and can't acquire accurate model easily,it is difficult to obtain good performance and can't meet the precise control requirements.An improved ant colony algorithm (IACA) is proposed for PID parameters optimization in order to attain ideal control effect in this paper.The proposed algorithm uses an adaptive tuning mechanism and a dynamic updating strategy of pheromone intensity and pheromone evaporation coefficient to accelerate algorithm convergence.The control system has better dynamic performance,robustness and more advantages in many aspects such as settling time (ts),rise time (tr) and peak time (tp) by using IACA.Moreover,the feasibility and superiority of the algorithm is further validated.IACA is simple and easy to find the optimal solution.Secondly,it can improve optimization efficiency and stability.Simulation results show it can reduce the overshoot,ts,tr and tp of the PID control systems,compared with other tuning algorithms.%绝大多数工业工程控制仍然使用PID控制器,但由于它不易获得精确的数学模型和其非线性时变系统的性质,传统PID控制难以获得良好的控制品质、难以满足精确的控制要求.为了使PID控制器达到理想的控制效果,提出了一种基于改进蚁群算法的PID参数优化整定算法.该算法采用了信息素挥发系数和信息素强度自适应调整机制和动态更新策略,用以加速优化算法的收敛.该算法简单易行,更容易找到全局最优解,优化效率和性能明显提高.仿真实验结果表明,同现有的优化算法整定的结果比较,被控系统的超调量、调整时间等明显减少,动态特性、鲁棒性和稳定性等明显提高,进而验证了所设计算法的可行性和优越性.
On cooperative patrolling: optimal trajectories, complexity analysis, and approximation algorithms
Pasqualetti, Fabio; Bullo, Francesco
2011-01-01
The subject of this work is the patrolling of an environment with the aid of a team of autonomous agents. We consider both the design of open-loop trajectories with optimal properties, and of distributed control laws converging to optimal trajectories. As performance criteria, the refresh time and the latency are considered, i.e., respectively, time gap between any two visits of the same region, and the time necessary to inform every agent about an event occurred in the environment. We associate a graph with the environment, and we study separately the case of a chain, tree, and cyclic graph. For the case of chain graph, we first describe a minimum refresh time and latency team trajectory, and we propose a polynomial time algorithm for its computation. Then, we describe a distributed procedure that steers the robots toward an optimal trajectory. For the case of tree graph, a polynomial time algorithm is developed for the minimum refresh time problem, under the technical assumption of a constant number of robo...
A danger-theory-based immune network optimization algorithm.
Zhang, Ruirui; Li, Tao; Xiao, Xin; Shi, Yuanquan
2013-01-01
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.
A New Algorithm for Generalized Optimal Discriminant Vectors
Institute of Scientific and Technical Information of China (English)
吴小俊; 杨静宇; 王士同; 郭跃飞; 曹奇英
2002-01-01
A study has been conducted on the algorithm of solving generalized optimal set of discriminant vectors in this paper. This paper proposes an analytical algorithm of solving generalized optimal set of discriminant vectors theoretically for the first time. A lot of computation time can be saved because all the generalized optimal sets of discriminant vectors can be obtained simultaneously with the proposed algorithm, while it needs no iterative operations. The proposed algorithm can yield a much higher recognition rate. Furthermore,the proposed algorithm overcomes the shortcomings of conventional human face recognition algorithms which were effective for small sample size problems only. These statements are supported by the numerical simulation experiments on facial database of ORL.
Decoherence in optimized quantum random-walk search algorithm
Zhang, Yu-Chao; Bao, Wan-Su; Wang, Xiang; Fu, Xiang-Qun
2015-08-01
This paper investigates the effects of decoherence generated by broken-link-type noise in the hypercube on an optimized quantum random-walk search algorithm. When the hypercube occurs with random broken links, the optimized quantum random-walk search algorithm with decoherence is depicted through defining the shift operator which includes the possibility of broken links. For a given database size, we obtain the maximum success rate of the algorithm and the required number of iterations through numerical simulations and analysis when the algorithm is in the presence of decoherence. Then the computational complexity of the algorithm with decoherence is obtained. The results show that the ultimate effect of broken-link-type decoherence on the optimized quantum random-walk search algorithm is negative. Project supported by the National Basic Research Program of China (Grant No. 2013CB338002).
A Multiobjective Optimization Algorithm Based on Discrete Bacterial Colony Chemotaxis
Directory of Open Access Journals (Sweden)
Zhigang Lu
2014-01-01
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.
Analog Circuit Design Optimization Based on Evolutionary Algorithms
Directory of Open Access Journals (Sweden)
Mansour Barari
2014-01-01
Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.
MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm
Directory of Open Access Journals (Sweden)
Ahmed M.E. Khalil
2015-06-01
Full Text Available The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA and the Krill Herd Algorithm (KHA. The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.
When do evolutionary algorithms optimize separable functions in parallel?
DEFF Research Database (Denmark)
Doerr, Benjamin; Sudholt, Dirk; Witt, Carsten
2013-01-01
is that evolutionary algorithms make progress on all subfunctions in parallel, so that optimizing a separable function does not take not much longer than optimizing the hardest subfunction-subfunctions are optimized "in parallel." We show that this is only partially true, already for the simple (1+1) evolutionary...... algorithm ((1+1) EA). For separable functions composed of k Boolean functions indeed the optimization time is the maximum optimization time of these functions times a small O(log k) overhead. More generally, for sums of weighted subfunctions that each attain non-negative integer values less than r = o(log1...
Optimal design of steel portal frames based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
Yue CHEN; Kai HU
2008-01-01
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.
Accuracy Analysis of Attitude Computation Based on Optimal Coning Algorithm
Directory of Open Access Journals (Sweden)
Jianfeng Chen
2012-09-01
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.
Accuracy Analysis of Attitude Computation Based on Optimal Coning Algorithm
Directory of Open Access Journals (Sweden)
Xiyuan Chen
2012-11-01
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
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
Holst, Terry L.
2005-01-01
A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Optimization Algorithms in School Scheduling Programs: Study, Analysis and Results
Directory of Open Access Journals (Sweden)
Lina PUPEIKIENE
2009-04-01
Full Text Available To create good and optimal school schedule is very important and practical task. Currently in Lithuania schools are using two programs for making the school schedule at the moment. But none of these programs is very effective. Optimization Department of Lithuanian Institute of Mathematics and Informatics (IMI has created ``School schedule optimization program''. It has three optimization algorithms for making best school schedule. A user can choose not only few optimization options and get few optimal schedules, but some subjective and objectives parameters. The making of initial data file is advanced in this program. XML format is used for creating initial data file and getting all optimal results files. The purpose of this study is to analyze used optimization algorithms used in ``School schedule optimization program'' and to compare results with two most popular commercial school scheduling programs in Lithuania.
Tuning of active vibration controllers for ACTEX by genetic algorithm
Kwak, Moon K.; Denoyer, Keith K.
1999-06-01
This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.
Modelling and optimization of computer network traffic controllers
Directory of Open Access Journals (Sweden)
N. U. Ahmed
2005-01-01
operation of the controller and evaluate the benefits of using a genetic algorithm approach to speed up the optimization process. Our results show that the use of the genetic algorithm proves particularly useful in reducing the computation time required to optimize the operation of a system consisting of multiple token-bucket-regulated sources.
A Novel and Robust Evolution Algorithm for Optimizing Complicated Functions
Gao, Yifeng; Zhao, Ge
2011-01-01
In this paper, a novel mutation operator of differential evolution algorithm is proposed. A new algorithm called divergence differential evolution algorithm (DDEA) is developed by combining the new mutation operator with divergence operator and assimilation operator (divergence operator divides population, and, assimilation operator combines population), which can detect multiple solutions and robustness in noisy environment. The new algorithm is applied to optimize Michalewicz Function and to track changing of rain-induced-attenuation process. The results based on DDEA are compared with those based on Differential Evolution Algorithm (DEA). It shows that DDEA algorithm gets better results than DEA does in the same premise. The new algorithm is significant for optimizing and tracking the characteristics of MIMO (Multiple Input Multiple Output) channel at millimeter waves.
Two New PRP Conjugate Gradient Algorithms for Minimization Optimization Models.
Directory of Open Access Journals (Sweden)
Gonglin Yuan
Full Text Available Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1 βk ≥ 0 2 the search direction has the trust region property without the use of any line search method 3 the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations.
Optimal Parallel Algorithm for the Knapsack Problem Without Memory Conflicts
Institute of Scientific and Technical Information of China (English)
Ken-Li Li; Ren-Fa Li; Qing-Hua Li
2004-01-01
The knapsack problem is well known to be NP-complete. Due to its importance in cryptosystem and in number theory, in the past two decades, much effort has been made in order to find techniques that could lead to practical algorithms with reasonable running time. This paper proposes a new parallel algorithm for the knapsack problem where the optimal merging algorithm is adopted. The proposed algorithm is based on an EREW-SIMD machine with shared memory. It is proved that the proposed algorithm is both optimal and the first without memory conflicts algorithm for the knapsack problem. The comparisons of algorithm performance show that it is an improvement over the past researches.
Optimal Control of Evolutionary Dynamics
Chakrabarti, Raj; McLendon, George
2008-01-01
Elucidating the fitness measures optimized during the evolution of complex biological systems is a major challenge in evolutionary theory. We present experimental evidence and an analytical framework demonstrating how biochemical networks exploit optimal control strategies in their evolutionary dynamics. Optimal control theory explains a striking pattern of extremization in the redox potentials of electron transport proteins, assuming only that their fitness measure is a control objective functional with bounded controls.
Optimal Control of Mechanical Systems
Vadim Azhmyakov
2007-01-01
In the present work, we consider a class of nonlinear optimal control problems, which can be called “optimal control problems in mechanics.” We deal with control systems whose dynamics can be described by a system of Euler-Lagrange or Hamilton equations. Using the variational structure of the solution of the corresponding boundary-value problems, we reduce the initial optimal control problem to an auxiliary problem of multiobjective programming. This technique makes it possible to apply some ...
A New Class of Hybrid Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
Da-Qing Guo; Yong-Jin Zhao; Hui Xiong; Xiao Li
2007-01-01
A new class of hybrid particle swarm optimization (PSO) algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique (LDIW) and the mutative scale chaos optimization algorithm (MSCOA) are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.
Flower pollination algorithm: A novel approach for multiobjective optimization
Yang, Xin-She; Karamanoglu, Mehmet; He, Xingshi
2014-09-01
Multiobjective design optimization problems require multiobjective optimization techniques to solve, and it is often very challenging to obtain high-quality Pareto fronts accurately. In this article, the recently developed flower pollination algorithm (FPA) is extended to solve multiobjective optimization problems. The proposed method is used to solve a set of multiobjective test functions and two bi-objective design benchmarks, and a comparison of the proposed algorithm with other algorithms has been made, which shows that the FPA is efficient with a good convergence rate. Finally, the importance for further parametric studies and theoretical analysis is highlighted and discussed.
Support Vector Machine Optimized by Improved Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiang Chang Sheng
2013-07-01
Full Text Available Parameters of support vector machines (SVM which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm.
The Role of Vertex Consistency in Sampling-based Algorithms for Optimal Motion Planning
Arslan, Oktay
2012-01-01
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...
Advanced CHP Control Algorithms: Scope Specification
Energy Technology Data Exchange (ETDEWEB)
Katipamula, Srinivas; Brambley, Michael R.
2006-04-28
The primary objective of this multiyear project is to develop algorithms for combined heat and power systems to ensure optimal performance, increase reliability, and lead to the goal of clean, efficient, reliable and affordable next generation energy systems.
Genetic algorithms for multicriteria shape optimization of induction furnace
Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo
2012-09-01
In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.
A Cooperative Harmony Search Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Gang Li
2014-01-01
Full Text Available Harmony search algorithm (HS is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs and particle swarm optimizers (PSOs. It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.
Low-Thrust Trajectory Optimization with Simplified SQP Algorithm
Parrish, Nathan L.; Scheeres, Daniel J.
2017-01-01
The problem of low-thrust trajectory optimization in highly perturbed dynamics is a stressing case for many optimization tools. Highly nonlinear dynamics and continuous thrust are each, separately, non-trivial problems in the field of optimal control, and when combined, the problem is even more difficult. This paper de-scribes a fast, robust method to design a trajectory in the CRTBP (circular restricted three body problem), beginning with no or very little knowledge of the system. The approach is inspired by the SQP (sequential quadratic programming) algorithm, in which a general nonlinear programming problem is solved via a sequence of quadratic problems. A few key simplifications make the algorithm presented fast and robust to initial guess: a quadratic cost function, neglecting the line search step when the solution is known to be far away, judicious use of end-point constraints, and mesh refinement on multiple shooting with fixed-step integration.In comparison to the traditional approach of plugging the problem into a “black-box” NLP solver, the methods shown converge even when given no knowledge of the solution at all. It was found that the only piece of information that the user needs to provide is a rough guess for the time of flight, as the transfer time guess will dictate which set of local solutions the algorithm could converge on. This robustness to initial guess is a compelling feature, as three-body orbit transfers are challenging to design with intuition alone. Of course, if a high-quality initial guess is available, the methods shown are still valid.We have shown that endpoints can be efficiently constrained to lie on 3-body repeating orbits, and that time of flight can be optimized as well. When optimizing the endpoints, we must make a trade between converging quickly on sub-optimal endpoints or converging more slowly on end-points that are arbitrarily close to optimal. It is easy for the mission design engineer to adjust this trade based on
Directory of Open Access Journals (Sweden)
Kumare Vinodh
2016-03-01
Full Text Available This paper presents an analytical approach for solving the weighting matrices selection problem of a linear quadratic regulator (LQR for the trajectory tracking application of a magnetic levitation system. One of the challenging problems in the design of LQR for tracking applications is the choice of Q and R matrices. Conventionally, the weights of a LQR controller are chosen based on a trial and error approach to determine the optimum state feedback controller gains. However, it is often time consuming and tedious to tune the controller gains via a trial and error method. To address this problem, by utilizing the relation between the algebraic Riccati equation (ARE and the Lagrangian optimization principle, an analytical methodology for selecting the elements of Q and R matrices has been formulated. The novelty of the methodology is the emphasis on the synthesis of time domain design specifications for the formulation of the cost function of LQR, which directly translates the system requirement into a cost function so that the optimal performance can be obtained via a systematic approach. The efficacy of the proposed methodology is tested on the benchmark Quanser magnetic levitation system and a detailed simulation and experimental results are presented. Experimental results prove that the proposed methodology not only provides a systematic way of selecting the weighting matrices but also significantly improves the tracking performance of the system.
Kidney-inspired algorithm for optimization problems
Jaddi, Najmeh Sadat; Alvankarian, Jafar; Abdullah, Salwani
2017-01-01
In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results.
Davidon's optimally conditioned algorithms for unconstrained optimization
Energy Technology Data Exchange (ETDEWEB)
Nazareth, L.
1976-01-01
Recently, Davidon (Math. Prog., 9, 1-30) has published some new and very promising algorithms for minimizing unconstrained functionals. A particular perspective on these algorithms is presented, and extensions of some of the theory underlying them are developed in this paper.
An Improved Particle Swarm Optimization Algorithm Based on Ensemble Technique
Institute of Scientific and Technical Information of China (English)
SHI Yan; HUANG Cong-ming
2006-01-01
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.
A Unified Differential Evolution Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
Qiang, Ji; Mitchell, Chad
2014-06-24
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
Institute of Scientific and Technical Information of China (English)
浦黄忠; 甄子洋; 王道波; 胡勇
2009-01-01
提出了基于改进微粒群算法的无人机姿态控制器参数智能整定方法.标准微粒群算法在搜索后期由于群体缺乏多样性而容易出现收敛停滞现象,为此提出了一种改进的微粒群算法.标准微粒群算法中的微粒速度是根据惯性运动、群体历史最优位置和自身历史最优位置来调节的.改进微粒群算法中的微粒除了保持惯性运动外,仅向当前群体中任意更优个体的状态学习,而且惯性权重系数是随机数.改进方案减少了算法不确定参数,简化了微粒学习机制,且增强了群体多样性.本文构建了无人机姿态控制系统,将改进微粒群算法用于四个控制参数的寻优整定.仿真结果表明,改进微粒群算法比一般微粒群算法具有更强的全局搜索能力,故获得更优的无人机姿态控制参数.%An improved particle swarm optimization (PSO) algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle (UAV). Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global
遗传算法优化的非线性钢结构模糊控制研究%Genetic algorithm optimized fuzzy control of nonlinear steel structure
Institute of Scientific and Technical Information of China (English)
张永兵; 梁星云; 唐滢; 陈立星
2016-01-01
Membership functions and scaling factors of fuzzy logic controller wereoptimized by the genetic algorithm. The piezoelectric friction damper wasregarded as the control device, and an opti-mized fuzzy control algorithm was proposed to reduce the seismic response of nonlinear steel struc-tures. Fuzzy controller based on dual inputs and single output, absolute value of interstory drifts and interstory velocities wereselected as input variables;voltages wereselected as output variables;trian-gular shapes wereselected as input and output variables of membership functions. Membership func-tions and scaling factors of fuzzy logic controller wereoptimized by thegenetic algorithm. A 3-story nonlinear steel structure wasanalyzed to simulate numerically the seismic responses under an opti-mized fuzzy logic controller. Numerical analysis results show that the fuzzy logic controllers optimized by genetic algorithm are effective in reduction of both acceleration and displacement responses of nonlinear steel structures.%采用遗传算法优化模糊控制算法的隶属函数及比例因子，通过压电变摩擦阻尼器实现减少非线性钢结构的地震响应。采用双输入单输出的模糊控制器，选取结构的层间位移的绝对值、层间速度的绝对值作为模糊控制器的输入变量，输出变量为作用电压，选取三角形函数作为输入、输出变量的隶属度函数，使用遗传算法对隶属函数及比例因子做出优化。以3层非线性钢结构地震响应为例，分别对模糊控制算法及遗传算法优化后模糊控制算法进行数值分析，结果表明：经遗传算法优化后的模糊控制，进一步降低了非线性钢结构的加速度和位移等响应。
Wolf Search Algorithm for Solving Optimal Reactive Power Dispatch Problem
Directory of Open Access Journals (Sweden)
Kanagasabai Lenin
2015-03-01
Full Text Available This paper presents a new bio-inspired heuristic optimization algorithm called the Wolf Search Algorithm (WSA for solving the multi-objective reactive power dispatch problem. Wolf Search algorithm is a new bio – inspired heuristic algorithm which based on wolf preying behaviour. The way wolves search for food and survive by avoiding their enemies has been imitated to formulate the algorithm for solving the reactive power dispatches. And the speciality of wolf is possessing both individual local searching ability and autonomous flocking movement and this special property has been utilized to formulate the search algorithm .The proposed (WSA algorithm has been tested on standard IEEE 30 bus test system and simulation results shows clearly about the good performance of the proposed algorithm .
Directory of Open Access Journals (Sweden)
Hang Zhou
2015-01-01
Full Text Available Reasonable airport runway scheduling is an effective measure to alleviate air traffic congestion. This paper proposes a new model and algorithm for flight scheduling. Considering the factors such as operating conditions and flight safety interval, the runway throughput, flight delays cost, and controller workload composes a multiobjective optimization model. The genetic algorithm combined with sliding time window algorithm is used to solve the model proposed in this paper. Simulation results show that the algorithm presented in this paper gets the optimal results, the runway throughput is increased by 12.87%, the delay cost is reduced by 61.46%, and the controller workload is also significantly reduced compared with FCFS (first come first served. Meanwhile, compared with the general genetic algorithm, it also reduces the time complexity and improves real-time and work efficiency significantly. The analysis results can provide guidance for air traffic controllers to make better air traffic control.
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
Chen, Wei
2015-07-01
In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.
Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.
2014-01-01
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860
Directory of Open Access Journals (Sweden)
A. Hajnoori
2013-07-01
Full Text Available Portfolio optimization problem follows the calculation of investment income per share, based on return and risk criteria. Since stock risk is achieved by calculating its return, which is itself computed based on stock price, it is essential to forecast the stock price, efficiently. In this paper, in order to predict the stock price, grey fuzzy technique with high efficiency is employed. The proposed study of this paper calculates the return and risk of each asset and portfolio optimization model is developed based on cardinality constraint and investment income per share. To solve the resulted model, Invasive Weed Optimization (IWO algorithm is applied. In an example this algorithm is compared with other metaheuristic algorithms such as Imperialist Competitive Algorithm (ICA, Genetic Algorithm (GA and Particle Swarm Optimization (PSO. The results show that the applied algorithm performs significantly better than other algorithms.
Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A
2014-01-01
This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.
Reactive Power Optimization with SVC & TCSC using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Biplab Bhattacharyya
2014-01-01
Full Text Available In this paper Genetic Algorithm (GA is used as an evolutionary tecthniques for the optimal placement of flexible AC transmission systems (FACTS devices in an interconnected power system. Here two types of FACTS devices has been discussed nemely, Thyristor Controlled Series Capacitor (TCSC and Static Var Compensator (SVC for the economic operation and to reduce the transmission loss. Reactively loading of the system is taken from base to 200% of base loading and the system performance is observed without and with FACTS devices. Optimal placement of FACTS devices in the system is determined by calculating active and reactive power flow in lines. FACTS devices along with reactive generation of generators and transformer tap setting are used for the power transfer capacity using GA. The proposed approach is applied on IEEE 14 and IEEE 30-bus test systems. Finally the effectiveness of the proposed GA based method of placement of FACTS devices is established by comparing the results with another standard method of optimization like Particle Swarm Optimization (PSO technique.
Hybrid Algorithm for the Optimization of Training Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Hayder M. Albeahdili
2015-10-01
Full Text Available The training optimization processes and efficient fast classification are vital elements in the development of a convolution neural network (CNN. Although stochastic gradient descend (SGD is a Prevalence algorithm used by many researchers for the optimization of training CNNs, it has vast limitations. In this paper, it is endeavor to diminish and tackle drawbacks inherited from SGD by proposing an alternate algorithm for CNN training optimization. A hybrid of genetic algorithm (GA and particle swarm optimization (PSO is deployed in this work. In addition to SGD, PSO and genetic algorithm (PSO-GA are also incorporated as a combined and efficient mechanism in achieving non trivial solutions. The proposed unified method achieves state-of-the-art classification results on the different challenge benchmark datasets such as MNIST, CIFAR-10, and SVHN. Experimental results showed that the results outperform and achieve superior results to most contemporary approaches.
PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm
Directory of Open Access Journals (Sweden)
Wei Chen Esmonde Lim
2014-01-01
Full Text Available Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB, the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
A Hierachical Evolutionary Algorithm for Multiobjective Optimization in IMRT
Holdsworth, Clay; Liao, Jay; Phillips, Mark H
2012-01-01
Purpose: Current inverse planning methods for IMRT are limited because they are not designed to explore the trade-offs between the competing objectives between the tumor and normal tissues. Our goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: We developed a hierarchical evolutionary multiobjective algorithm designed to quickly generate a diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the trade-offs in the plans. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then...
Directory of Open Access Journals (Sweden)
Yuksel Celik
2013-01-01
Full Text Available Marriage in honey bees optimization (MBO is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.
Optimal Design of Materials for DJMP Based on Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
FENG Zhong-ren; WANG Xiong-jiang
2004-01-01
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.
A New Approach for Controlling Chaos Based on Direct Optimizing Predictive Control
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
We introduce the predictive control theory into the study of chaos control and propose a direct optimizing predictive control algorithm based on a neural network model. The proposed control system stabilizes the chaotic motion in an unknown chaotic system onto the desired target trajectory. Compared with the existing similar algorithms, the proposed control system has faster response, so it requires much shorter time for the stabilization of the chaotic systems.The proposed approach can control hyperchaos and the algorithm is simple. The convergence of the control algorithm and the stability of the control system can be guaranteed. The theoretic analysis and simulations demonstrate the effectiveness of the algorithm.
Differential evolution algorithm for global optimizations in nuclear physics
Qi, Chong
2017-04-01
We explore the applicability of the differential evolution algorithm in finding the global minima of three typical nuclear structure physics problems: the global deformation minimum in the nuclear potential energy surface, the optimization of mass model parameters and the lowest eigenvalue of a nuclear Hamiltonian. The algorithm works very effectively and efficiently in identifying the minima in all problems we have tested. We also show that the algorithm can be parallelized in a straightforward way.
A TRUST-REGION ALGORITHM FOR NONLINEAR INEQUALITY CONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
Xiaojiao Tong; Shuzi Zhou
2003-01-01
This paper presents a new trust-region algorithm for n-dimension nonlinear optimization subject to m nonlinear inequality constraints. Equivalent KKT conditions are derived,which is the basis for constructing the new algorithm. Global convergence of the algorithm to a first-order KKT point is established under mild conditions on the trial steps, local quadratic convergence theorem is proved for nondegenerate minimizer point. Numerical experiment is presented to show the effectiveness of our approach.
QOS-BASED MULTICAST ROUTING OPTIMIZATION ALGORITHMS FOR INTERNET
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
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.
A Globally Convergent Parallel SSLE Algorithm for Inequality Constrained Optimization
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Zhijun Luo
2014-01-01
Full Text Available A new parallel variable distribution algorithm based on interior point SSLE algorithm is proposed for solving inequality constrained optimization problems under the condition that the constraints are block-separable by the technology of sequential system of linear equation. Each iteration of this algorithm only needs to solve three systems of linear equations with the same coefficient matrix to obtain the descent direction. Furthermore, under certain conditions, the global convergence is achieved.
The study of cuckoo optimization algorithm for production planning problem
Akbarzadeh, Afsane; Shadkam, Elham
2015-01-01
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and common problems for production planning problem to optimize. In this study, one of the mathematical models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is efficient method to solve continues non linear problem. Moreover, mentioned models of production planning solved with Genetic algorithm and Lingo software and the results will compared. The Cucko...
Gradient Gene Algorithm: a Fast Optimization Method to MST Problem
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The extension of Minimum Spanning Tree(MST) problem is an NP hardproblem which does not exit a polynomial time algorithm. In this paper, a fast optimizat ion method on MST problem--the Gradient Gene Algorithm is introduced. Compar ed with other evolutionary algorithms on MST problem, it is more advanced: firstly, very simple and easy to realize; then, efficient and accurate; finally general on other combination optimization problems.
Advanced optimization of permanent magnet wigglers using a genetic algorithm
Energy Technology Data Exchange (ETDEWEB)
Hajima, Ryoichi [Univ. of Tokyo (Japan)
1995-12-31
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.
Parallel optimization algorithms and their implementation in VLSI design
Lee, G.; Feeley, J. J.
1991-01-01
Two new parallel optimization algorithms based on the simplex method are described. They may be executed by a SIMD parallel processor architecture and be implemented in VLSI design. Several VLSI design implementations are introduced. An application example is reported to demonstrate that the algorithms are effective.
Bio Inspired Algorithms in Single and Multiobjective Reliability Optimization
DEFF Research Database (Denmark)
Madsen, Henrik; Albeanu, Grigore; Burtschy, Bernard
2014-01-01
Non-traditional search and optimization methods based on natural phenomena have been proposed recently in order to avoid local or unstable behavior when run towards an optimum state. This paper describes the principles of bio inspired algorithms and reports on Migration Algorithms and Bees...
Institute of Scientific and Technical Information of China (English)
董飞飞; 刘涤尘; 吴军; 岑炳成; 宋春丽; 马文媛
2014-01-01
针对常用的次同步振荡控制器不能较好地适应电力系统时变非线性的特点，提出了一种引入余弦迁移模型、早熟判断机制、变尺度混沌变异策略及排重操作的改进生物地理学优化算法。基于该算法结合静止无功补偿器(SVC)抑制次同步振荡的机理，对次同步阻尼控制器进行优化设计，并采用特征值分析和时域仿真验证了控制系统的有效性。锦界电厂算例分析表明：经改进生物地理学算法优化的SVC次同步阻尼控制器能较好地提高机组扭振的模态阻尼，可有效抑制次同步振荡，进而保证机组和电网的安全稳定运行；与传统的生物地理学优化算法、粒子群算法及遗传算法相比，改进生物地理学优化算法在搜索最优控制参数时具有较快的搜索速度和较高的搜索精度。%In view of the common subsynchronous oscillation (SSO) controller incapability of suiting the time-varying and nonlinear characteristics of power system,the cosine migration model,the premature judging mechanism,the mutative scale of chaos mutation strategy, and re-scheduling operations are introduced into the improved biogeography-based optimization (IBBO) algorithm to design subsynchronous damping controller(SSDC)optimally based on the mechanism of suppressing SSO by static var compensator (SVC).Finally,eigenvalue analysis and electromagnetic simulation are conducted to verify the effectiveness of the controller developed.The simulation analysis of Jinjie Plant indicates that SVC-SSDC optimized by the IBBO algorithm can greatly improve the damping of the three torsional modes and thus effectively depress the multimodal SSO, ensuring stability of the system and safety of the generator shafts.Moreover,The IBBO algorithm has a faster search speed and higher search accuracy in searching for the optimal control parameters compared with the traditional biogeography-based optimization (BBO) algorithm
Optimal coordinated voltage control of power systems
Institute of Scientific and Technical Information of China (English)
LI Yan-jun; HILL David J.; WU Tie-jun
2006-01-01
An immune algorithm solution is proposed in this paper to deal with the problem of optimal coordination of local physically based controllers in order to preserve or retain mid and long term voltage stability. This problem is in fact a global coordination control problem which involves not only sequencing and timing different control devices but also tuning the parameters of controllers. A multi-stage coordinated control scheme is presented, aiming at retaining good voltage levels with minimal control efforts and costs after severe disturbances in power systems. A self-pattern-recognized vaccination procedure is developed to transfer effective heuristic information into the new generation of solution candidates to speed up the convergence of the search procedure to global optima. An example of four bus power system case study is investigated to show the effectiveness and efficiency of the proposed algorithm, compared with several existing approaches such as differential dynamic programming and tree-search.
Directory of Open Access Journals (Sweden)
Alireza Khosravi
2012-03-01
Full Text Available This paper deals with the design of optimal backstepping controller, by using the chaotic particle swarm optimization (CPSO algorithm to control of chaos in Lure like chaotic system. The backstepping method consists of parameters which could have positive values. The parameters are usually chosen optional by trial and error method. The controlled system provides different behaviors for different values of the parameters. It is necessary to select proper parameters to obtain a good response, because the improper selection of the parameters leads to inappropriate responses or even may lead to instability of the system. The proposed optimal backstepping controller without trial and error determines the parameters of backstepping controller automatically and intelligently by minimizing the Integral of Time multiplied Absolute Error (ITAE and squared controller output. Finally, the efficiency of the proposed optimal backstepping controller (OBSC is illustrated by implementing the method on the Lure like chaotic system.
Wind turbine pitch control using ICPSO-PID algorithm
DEFF Research Database (Denmark)
Xu, Chang; Tian, Qiangqiang; Shen, Wen Zhong
2013-01-01
of improved cooperative particle swarm optimization (ICPSO) and PID, subsequently, it was used to tune the pitch controller parameters; thus the difficulty in PID tuning was removed when a wind speed was above the rated speed. It was indicated that the proposed optimization algorithm can tune the pitch...... with ICPSO-PID algorithm has a smaller overshoot, a shorter tuning time and better robustness. The design method proposed in the paper can be applied in a practical electro-hydraulic pitch control system for WTG......., a pitch controller was designed based on power and wind speed and by considering the inertia and delay characteristics of a pitch-control system to achieve a constant power output when a wind speed was beyond the rated one. A novel ICPSO-PID control algorithm was proposed based on a combination...