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Harmony Search Method for Multimodal Size, Shape, and Topology optimization of Structural Frameworks

JOURNAL OF STRUCTURAL ENGINEERING-ASCE 2011年第11期137卷 1332-1339页

作者： Martini, Kirk Univ Virginia Dept Architecture Charlottesville VA 22904 USA

This paper describes and demonstrates modifications of the harmony search method to support multimodal structural optimization. Several researchers have recognized the potential of population-based optimization methods, such as genetic algorithms and particle swarm optimization, to support multimodal optimization, that is, generating a range of good alternative solutions, rather than a single best solution. Among these population-based methods is the harmony search method, which has been demonstrated to be efficient and effective in many unimodal structural optimization problems. Toward the goal of making the harmony search method more effective in multimodal optimization, this paper describes a new strategy for generating solutions called close-harmony improvisation, and a new strategy for replacing solutions called local replacement. Examples demonstrate the effect of the two strategies used individually and in tandem. The discussion compares results with conventional harmony search and finds that close-harmony improvisation consistently improves the fitness of the search results, although the effect is sometimes mild, whereas local replacement is quite effective in increasing the diversity of the search result. DOI: 10.1061/(ASCE)ST.1943-541X.0000378. (C) 2011 American Society of Civil Engineers.

关键词： optimization algorithms Structural design

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Green Scheduling to minimize Base station Transmit power and UE circuit power consumption

Green Scheduling to minimize Base station Transmit power and...

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22nd IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Gupta, Rohit Strinati, Emilio Calvanese CEA LETI MINATEC F-38054 Grenoble France

ISBN: (纸本)9781457713484

Packet scheduling algorithms are viewed as one of the key mechanisms for increasing the diversity order, robustness and effectiveness of a wireless multi-user communication systems. Traditional packet scheduling algorithms are designed to save energy at the Base-station(BS) in downlink by exploiting tradeoffs between spectral efficiency, delay and energy while at the same time meeting the QoS requirements of the system. However, these algorithms ignore the User-Equipment(UE) circuit power consumption to receive and process down link traffic. In this paper, we show that the optimization of only BS transmit power consumption in downlink can lead to significant drain of UE battery power. Hence, we propose sub-optimal algorithms that exploit Discontinuous Transmission(DTX) at the base-station to trade off delay with energy consumption to improve Energy Efficiency (EE) of the UE circuit power and BS transmit power, while at the same time meeting the application QoS requirements in terms of through put and service delay. The proposed algorithm is also shown to achieve good performance in saving transmit power at the base-station and also the UE circuit power consumption over traditional scheduling algorithms.

关键词： BASE STATION Scheduling algorithms Transmitter power Power consumption Energy sources Downlink optimization algorithms

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Knowledge management in the development of optimization algorithms

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Symposium on Human Interface held at the HCI International 2007

作者： Crawford, Broderick Castro, Carlos Monfroy, Eric Pontificia Univ Catolica Valparaiso Valparaiso Chile

ISBN: (纸本)9783540733447

This paper captures our experience developing algorithms to solve Combinatorial Problems using different techniques. Because it is a Software Engineering problem, then to find better ways of developing algorithms, solvers and metaheuristics is our interest too. Here, we fixed some concepts from Knowledge Management and Software Engineering applied in our work.

关键词： knowledge management software engineering agile development creativity optimization algorithms

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Multiobjective optimization for Optimal Placement and Size of DG using Shuffled Frog Leaping Algorithm

Multiobjective Optimization for Optimal Placement and Size o...

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2011 2nd International Conference on Advances in Energy Engineering(ICAEE)

作者： Chandrasekhar Yammani Sydulu Maheswarapu Sailajakumari Matam Department of Electrical Engineering National Institute of Technology

There has been a great interest in integration of distributed generation(DG) units at distribution level in the recent *** can provide cost-effective,environmental friendly,higher power quality and more reliable energy solutions than conventional *** maximum power loss reduction,proper sizing and position of distributed generators are ardently *** paper presents a simple method for optimizing cost and optimal placement of generators.A simple vector based load flow technique is implemented on 38 bus distribution *** paper presents a new methodology using a new population based meta heuristic approach namely Shuffled frog leaping algorithm for the placement of Distributed Generators(DG) in the radial distribution systems to reduce the real power losses and cost of the *** paper also focuses on optimization of weighting factor,which balances the cost and the loss factors and helps to build up desired objectives with maximum potential *** proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.

关键词： Distributed Generation optimization algorithms Shuffled Frog Leaping Algorithm

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Distributed Delayed Stochastic optimization 11

Distributed Delayed Stochastic Optimization

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Annual Conference on Neural Information Processing Systems

作者： Alekh Agarwal John C. Duchi Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA 94720

ISBN: (纸本)9781618395993

We analyze the convergence of gradient-based optimization algorithms whose updates depend on delayed stochastic gradient information. The main application of our results is to the development of distributed minimization algorithms where a master node performs parameter updates while worker nodes compute stochastic gradients based on local information in parallel, which may give rise to delays due to asynchrony. Our main contribution is to show that for smooth stochastic problems, the delays are asymptotically negligible. In application to distributed optimization, we show n-node architectures whose optimization error in stochastic problems-in spite of asynchronous delays-scales asymptotically as O( nT~1/2), which is known to be optimal even in the absence of delays.

关键词： Stochastic local information. delay asymptotic optimization algorithms Distribute Converge

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Improved Variable-Fidelity optimization Algorithm for Simulation-Driven Design of Antennas

Improved Variable-Fidelity Optimization Algorithm for Simula...

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2011 IEEE International Symposium on Antennas and Propagation(APSURSI 2011)

作者： Slawomir Koziel Stanislav Ogurtsov Engineering Optimization & Modeling Center School of Science and Engineering Reykjavik University Menntavegur 1 IS-101 Reykjavik Iceland

ISBN: (纸本)9781424495627;9781424495634

An improved version of the variable-fidelity optimization technique for the simulation-driven design of microwave structures including antennas is presented. It exploits a set of electromagnetic (EM)-based models of increasing discretization density that are sequentially optimized with the optimum of the "coarser" being the initial design for the "finer". The final design is refined using a polynomial-based approximation model constructed from the coarse-discretization simulation data. The coarse-discretization models are optimized using a novel algorithm that allows us to reduce the overall design time by up to 50percent when compared to the original version of the method. The presented technique is particularly suitable in cases where simulation-driven design is the only option. Operation of our approach is demonstrated using two examples of broadband antennas. In all cases, the optimal design is obtained at a low computational cost corresponding to a few high-fidelity simulations of the structure.

关键词： Computer-aided design (CAD) simulation-driven design coarse-discretization electromagnetic (EM) simulation variable-fidelity optimization antenna design simulating antenna design ANTENNA broadband antennas optimization algorithms preliminary design design year overall design simulated data

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Simultaneous Numerical optimization for Data Association and Parameter Estimation

Simultaneous Numerical Optimization for Data Association and...

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IEEE Conference on Decision and Control

作者： Anil Damle Lucy Pao Department of Electrical Computer and Energy Engineering University of Colorado Boulder Colorado 80309 USA

ISBN: (纸本)9781612848006

In some tracking environments, there is no clear linkage of measurements to objects, requiring data association algorithms to associate measurements to target tracks. Further complicating the problem is that targets can operate in different modes and hence have time-varying dynamics. It has recently been shown that treating the potential measurement data for an object as a single continuous signal, that contains impulses when the measurement data jumps to a new object, allows the data association problem to be cast as a trajectory optimization problem. This optimization problem can also be expanded to formulate estimates for dynamic parameters of the objects being measured. The contribution of the current paper is the development of a numerical optimization algorithm that allows the recently developed impulse-based data association and parameter estimation methods to be easily applied without a significant amount of scenario-specific analytical calculations.

关键词： data association Parameter estimation optimization algorithms measurement data dynamic parameter time varying

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An optimization Algorithm For Joint Mechanics Estimate Using Inertial Measurement Unit Data During A Squat Task

An Optimization Algorithm For Joint Mechanics Estimate Using...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Vincent Bonnet Claudia Mazza Philippe Fraisse Aurelio Cappozzo LABLAB Department of Human Movement and Sports Sciences University of Rome "Foro Italico" LIRMM UMR 5506 CNRS University of Montpellier 2

ISBN: (纸本)9781424441211

The use of dynamic optimization as a tool to estimate joint kinematics and kinetics, and ground reaction forces using data from a single inertial measurement unit (IMU) positioned on the lower trunk was investigated. The feasibility of this approach and its accuracy was explored for the analysis of a squat task, focusing on the ankle, knee and hip joints. An optimal motor control strategy aimed at minimizing the sum of the intersegmental couples and of their time derivatives was imposed to estimate the mechanics of a three-segment sagittal model. Moreover, in the optimization process constraints to the measured vertical acceleration, to the maximal vertical IMU excursion, and with regard to the maintenance of dynamic balance were imposed. Experiments were performed using 10 volunteers. Data were collected from the IMU, from a stereophotogrammetric system (SS) and from a force platform for validation purposes. Results showed a very good consistency of the model output with the lower limb joint trajectories, as obtained using the SS, and with the measured vertical component of the ground reaction (low root mean square differences (<10%) and high correlation coefficients (0.98)).

关键词： optimization algorithms INERTIAL MEASUREMENT UNITS mechanic Mechanics Squat Lower trunk Hip Joint kinetic equilibrium

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Effects of Genetic Algorithm Parameters on Multiobjective optimization Algorithm Applied to System Identification Problem

Effects of Genetic Algorithm Parameters on Multiobjective Op...

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International Conference on Modeling, Simulation and Applied optimization

作者： Mohd Zakimi Zakaria Hishamuddin Jamaluddin Robiah Ahmad Abdul Halim Muhaimin School of Manufacturing Engineering Universiti Malaysia Perlis Department of Applied Mechanics Faculty of Mechanical Engineering Universiti Teknologi Malaysia

ISBN: (纸本)9781457700033

The growing interest in multiobjective optimization algorithms and system identification resulted in a huge research area. System identification is about developing a mathematical model for representing the system observed. This paper describes the effects of genetic algorithm parameters used in multiobjective optimization algorithm (MOO) that is applied to system identification problem. Two simulated linear systems with known model structure were considered for representing the system identification problem. The performance metrics used in this study are convergence and diversity metric. These metrics show the performance of MOO when GA parameters are varied. The simulation results show the effects of GA parameter on MOO performance. A right combination of GA parameters used in MOO is shown in this study.

关键词： System identification optimization algorithms Genetic Algorithm (GA) Simulation results Model structures Performance metrics Gallium Linear system Mathematical Model parameters

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Distribution service restoration using chaotic optimization and immune algorithm

Distribution service restoration using chaotic optimization ...

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International Conference on Information Science and Technology

作者： Shaolan Lei Shan Li Jing Yang Dongrong Jiang school of Electronic Information and Automation in Chongqing University of technology

ISBN: (纸本)9781424494408

A method of chaotic optimization and immune algorithm is presented for service restoration after faults in distribution system in this paper, which might improve the probability for optimal solution, and have the characteristic of chaotic optimization and artificial immunity algorithm. In the proposed algorithm, chaotic optimization is used to initialize the antibody of immune algorithm firstly in order to improve convergence speed by the excellent global search ability, which is regarded as thick search. Then artificial immune algorithm is applied by local search to complete thin search in the chaotic optimization, in order to make the population diversity and avoid getting to stuck in local minima to a certain degree. The effectiveness of the proposed method is demonstrated with actual system taken from a certain district distribution grid in Chongqing. The result shows that chaos immune optimization algorithm in the paper might improve convergence speed and precision of restoration and avoid premature convergence.

关键词： failure recovery Immunity optimization algorithms dispensing system algorithms optimal solution Converge

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