Introduction to Optimum Design with MATLAB

Arora, Jasbir S.

doi:10.1016/b978-012064155-0/50012-4

Cited by 695 publications

(806 citation statements)

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“…This problem was first described by Belegundu [8] and Arora [9]. The design objective is to minimize the weight of a tension/compression spring subject to constraints on shear stress, surge frequency and minimum deflections as shown in Figure 1.…”

Section: Design Of Tension/compression Springmentioning

confidence: 99%

Extended Multipoint Approximation Method

Liu¹,

Liu²,

Mao³

et al. 2017

dtetr

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Abstract. Stemming from polynomial metamodels, multipoint approximation method (MAM) and moving least square method (MLSM) focus on the development of metamodels for the objective and constraint functions in solving a mid-range optimization problem with a trust region. Although both of these methods could solve problems successfully, there is still some room for improvement on the computational effort and search capability. To address this problem, the extended multipoint approximation method is proposed to seek the optimal solution in this paper. The developed method assimilating the advantage of Taylor's expansion used in MLSM demonstrates its superiority over other methods in terms of the computational efficiency and accuracy by some well-established benchmark problems.

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Section: Design Of Tension/compression Springmentioning

confidence: 99%

Extended Multipoint Approximation Method

Liu¹,

Liu²,

Mao³

et al. 2017

dtetr

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“…In this work, two optimizations algorithms of HAWTOPT are used: The sequential linear programming (SLP) method of Arora 14 and the simple genetic algorithm (SGA) of Goldberg. 15 The main reason for selecting these algorithm is that the authors had good experience working with them for wind turbine rotor optimization in previous works.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

“…The move-limits are adaptive and automatically adjusted according to the convergence toward an optimum on basis of the rate of convergence. 14 The SLP approach typically has an attractive rate of convergence and is therefore efficient in terms of the number of cost function evaluations required. However, this is at the expense of being sensitive to local minima.…”

Section: Sequential Linear Programmingmentioning

confidence: 99%

TOPFARM: Multi‐fidelity optimization of wind farms

et al. 2013

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A wind farm layout optimization framework based on a multi-fidelity optimization approach is applied to the offshore test case of Middelgrunden, Denmark as well as to the onshore test case of Stag Holt -Coldham wind farm, UK. While aesthetic considerations have heavily influenced the famous curved design of the Middelgrunden wind farm, this work focuses on demonstrating a method that optimizes the profit of wind farms over their lifetime based on a balance of the energy production income, the electrical grid costs, the foundations cost, and the cost of wake turbulence induced fatigue degradation of different wind turbine components. A multi-fidelity concept is adapted, which uses cost function models of increasing complexity (and decreasing speed) to accelerate the convergence to an optimum solution. In the EU-FP6 TOP-FARM project, three levels of complexity are considered. The first level uses a simple stationary wind farm wake model to estimate the Annual Energy Production (AEP), a foundations cost model depending on the water depth and an electrical grid cost function dictated by cable length. The second level calculates the AEP and adds a wake-induced fatigue degradation cost function on the basis of the interpolation in a database of simulations performed for various wind speeds and wake setups with the aero-elastic code HAWC2 and the dynamic wake meandering model. The third level, not considered in this present paper, includes directly the HAWC2 and the dynamic wake meandering model in the optimization loop in order to estimate both the fatigue costs and the AEP. The novelty of this work is the implementation of the multi-fidelity approach in the context of wind farm optimization, the inclusion of the fatigue degradation costs in the optimization framework, and its application on the optimal performance as seen through an economical perspective.

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“…Mutation is the third step that safeguards the process from a complete premature loss of valuable genetic material during selection and crossover. The foregoing three steps are repeated for successive generations of the population until no further improvement in fitness is attainable [14,15,16]. applied.…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

Srikanthakumar

Chen

2014

Robotica

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SUMMARYThis paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, and illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncertainties in physical parameters, sensor measurements, and even the model structure of the robot are taken into account in the worst-case analysis. The minimum distance to a moving obstacle is considered as an objective function in automatic search process. It is demonstrated that a local nonlinear optimization method may not be adequate, and global optimization techniques are necessary to provide reliable worst-case analysis. The Monte Carlo simulation is carried out to demonstrate that the proposed automatic search methods provide a significant advantage over random sampling approaches.

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Introduction to Optimum Design with MATLAB

Cited by 695 publications

References 0 publications

Extended Multipoint Approximation Method

Extended Multipoint Approximation Method

TOPFARM: Multi‐fidelity optimization of wind farms

Worst-case analysis of moving obstacle avoidance systems for unmanned vehicles

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