In this paper, a methodology for postoptimality studies to assess the robustness of the Pareto-optimal solutions computed with a multi-objective optimization algorithm is presented. The proposed Pareto-robust optimiza...
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In this paper, a methodology for postoptimality studies to assess the robustness of the Pareto-optimal solutions computed with a multi-objective optimization algorithm is presented. The proposed Pareto-robust optimization approach is based on factorial design for sampling the design region in the neighborhood of the Pareto-optimal solutions. It allows for estimating a metric for the Pareto robustness and contributes to improving convergence of the known Pareto-front toward the true Pareto front Further, sensitivity analysis of the performance and response surfaces in the neighborhood of the optimal solutions are computed without additional computational cost. The proposed approach is applied to two validation test cases and to the design of a satellite Earth-observation mission for disaster monitoring. The results show that the Pareto-robust optimization approach can correctly detect Pareto-robust solutions on the Pareto front, and that it provides additional Pareto-optimal solutions at the same time, eventually improving the original known Pareto front In the case of the Earth-observation mission, the study demonstrates the possibility to enable and promote tradeoffs among the engineering team members to obtain an effective decision-making process. The solution identified as the most Pareto-robust one can be considered quite uncommon but still very reasonable due to the assumptions, presenting a satellite in a nonsun-synchronous medium Earth orbit
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, tw...
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Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are modified and new configurations are used. The modified versions are tested on some well-known benchmark functions. Results show that the new changes have positive effects on the performance of ABC algorithm. (C) 2013 Published by Elsevier Inc.
The locations of the control hardware are typically a design variable in controller design for distributed parameter systems. In order to obtain the most efficient control system, the locations of control hardware as ...
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The locations of the control hardware are typically a design variable in controller design for distributed parameter systems. In order to obtain the most efficient control system, the locations of control hardware as well as the feedback gain should be optimized. These optimization problems are generally non-convex. In addition, the models for these systems typically have a large number of degrees of freedom. Consequently, existing optimization schemes for optimal actuator placement may be inaccurate or computationally impractical. In this paper, the feedback control is chosen to be an optimal linear quadratic regulator. The optimal actuator location problem is reformulated as a convex optimization problem. A subgradient-based optimization scheme which leads to the global solution of the problem is used to optimize actuator locations. The optimization algorithm is applied to optimize the placement of piezoelectric actuators in vibration control of flexible structures. This method is compared with a genetic algorithm, and is observed to be faster and more accurate. Experiments are performed to verify the efficacy of optimal actuator placement.
Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm that mimics the social behaviour of fireflies flying in the tropical and temperate summer sky. In this study, a novel application of FA is presente...
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Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm that mimics the social behaviour of fireflies flying in the tropical and temperate summer sky. In this study, a novel application of FA is presented as it is applied to solve tracking problem. A general optimisation-based tracking architecture is proposed and the parameters' sensitivity and adjustment of the FA in tracking system are studied. Experimental results show that the FA-based tracker can robustly track an arbitrary target in various challenging conditions. The authors compare the speed and accuracy of the FA with three typical tracking algorithms including the particle filter, meanshift and particle swarm optimisation. Comparative results show that the FA-based tracker outperforms the other three trackers.
Closed-loop identification of continuous systems, which can be considered as a nonlinear optimization problem, may result in a difficult solution problem when conventional methods are used. In this paper it is present...
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Closed-loop identification of continuous systems, which can be considered as a nonlinear optimization problem, may result in a difficult solution problem when conventional methods are used. In this paper it is presented a hybrid strategy based on an Adaptive Genetic Algorithm and the Simplex method, that results in a satisfactory solution for this problem. The proposal is compared with other techniques reported in the literature. Three examples show the performance of the method: identification of high order dynamics;identification of unstable second order dynamics in open-loop;and parameter estimation in power generation systems. Simulation results show that the proposed is a robust method for close-loop system identification.
In this paper we propose a new algorithm for fast l(1) minimization as frequently arising in compressed sensing. Our method is based on a split Bregman algorithm applied to the dual of the problem of minimizing parall...
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In this paper we propose a new algorithm for fast l(1) minimization as frequently arising in compressed sensing. Our method is based on a split Bregman algorithm applied to the dual of the problem of minimizing parallel to u parallel to(1) + 1/2 alpha parallel to u parallel to(2) such that u solves the under-determined linear system Au = f, which was recently investigated in the context of linearized Bregman methods. Furthermore, we provide a convergence analysis for split Bregman methods in general and show with our compressed sensing example that a split Bregman approach to the primal energy can lead to a different type of convergence than split Bregman applied to the dual, thus making the analysis of different ways to minimize the same energy interesting for a wide variety of optimization problems.
The light weight deflectometer (LWD) is a portable, nondestructive testing device that can estimate pavement layer parameters, namely moduli. Conventional backcalculation of layer parameters from LWD deflections is fo...
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The light weight deflectometer (LWD) is a portable, nondestructive testing device that can estimate pavement layer parameters, namely moduli. Conventional backcalculation of layer parameters from LWD deflections is formulated as an inverse problem where predicted vertical deflections are matched to observed vertical deflections using a gradient search algorithm. In this paper, we present an LWD backcalculation scheme to recover layer parameters, including top-layer thickness, of a two-layer earthwork system. Our approach resolves the problem using a dynamic finite-element (FE) model for the forward calculation of LWD deflection data and implements a genetic algorithm (GA) as the inverse solver. The objective function we minimize is formulated as a measure of the data misfit between predicted and observed data, normalized by the peak deflections, and it includes 180 data points from the dynamic deflection time history. The objective function contains multiple local minima that can potentially trap gradient search algorithms, thus validating application of GA as a global search technique for this problem. The GA is applied to both synthetic and experimental data, and we demonstrate that the recovered top-layer thickness, top-layer modulus, and underlying modulus for the experimental data compare favorably with expected values. (C) 2013 American Society of Civil Engineers.
For extremely high-performance lithographic lenses, the edge level accuracy of the manufacturing process and multicompensation strategies must be applied. Element clocking can be effectively used to compensate for the...
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For extremely high-performance lithographic lenses, the edge level accuracy of the manufacturing process and multicompensation strategies must be applied. Element clocking can be effectively used to compensate for the low-order figure errors of the elements. Considering that commercial optical software is usually incapable of obtaining good convergence for clocking optimization, this paper proposes a mathematical model of a lithographic lens containing the errors of a surface figure, after which a clocking optimization algorithm is programmed. A clocking optimization instance proving that the clocking optimization algorithm is capable of finding the optimized angle of elements and that clocking is an effective compensation strategy. The calculated accuracy of the proposed mathematic model was found to be acceptable for clocking optimization. (C) 2013 Optical Society of America
This paper presents a new method for finding two link-disjoint paths in WDM networks under wavelength continuity and lowest cost constraints. Such a problem is considered to be an NP-complete problem, which is only so...
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This paper presents a new method for finding two link-disjoint paths in WDM networks under wavelength continuity and lowest cost constraints. Such a problem is considered to be an NP-complete problem, which is only solvable using Integer Linear Programming (ILP). The presented method is based on transforming the original network into an auxiliary network with n x n nodes and 2 mn links, where n is the number of nodes and m is the number of links in the original network, and then applying a modified version of Dijkstra's shortest path algorithm on that network. Despite the larger network size, the execution time of the algorithm is in polynomial order. Considering that the problem is NP-complete, the presented algorithm takes much less time than using ILP, which generally requires exponential time. Yet, it is able to find all available disjoint paths obtainable by ILP.
The number of available algorithms for the so-called Basis Pursuit Denoising problem (or the related LASSO-problem) is large and keeps growing. Similarly, the number of experiments to evaluate and compare these algori...
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The number of available algorithms for the so-called Basis Pursuit Denoising problem (or the related LASSO-problem) is large and keeps growing. Similarly, the number of experiments to evaluate and compare these algorithms on different instances is growing. In this correspondence, we present a method to produce instances with exact solutions that is based on a simple observation, related to the so-called source condition from sparse regularization.
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