This paper discusses the identification of Ferrite Core (FC) power inductors parameters in the real operating conditions relevant to Switch-Mode Power Supplies starting from experimental measurements. A novel method f...
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ISBN:
(纸本)9781479966493
This paper discusses the identification of Ferrite Core (FC) power inductors parameters in the real operating conditions relevant to Switch-Mode Power Supplies starting from experimental measurements. A novel method for parameters identification is proposed, based on evolutionary algorithms (EAs) and on the analysis of inductors non-linear behavior. Two EAs, the Genetic Algorithm and the Differential Evolution, are investigated and compared. The results of the proposed method are experimentally validated by means of a buck converter evaluation board.
In this article we investigate Multi-agent simulation and Multi-objective evolutionary algorithms for optimizing resource allocation in Public Safety. We describe a tool that helps Law Enforcement authorities to evalu...
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ISBN:
(纸本)9781479998890
In this article we investigate Multi-agent simulation and Multi-objective evolutionary algorithms for optimizing resource allocation in Public Safety. We describe a tool that helps Law Enforcement authorities to evaluate, in a controlled environment, different strategies for allocating and dispatching resources, aiming at reducing conflicting goals such as response time, the number of unattended calls and cost of displacement of police cars. This tool is a multi-agent model to represent police cars that lives in a grid in which emergency occurrences appear. A comparison of the strategies for resource dispatch in this environment shows that serving first those calls with low estimated attendance times delivers the best overall performance in terms of waiting time. However this is practically impossible since prioritization of certain crime types is necessary leading to the increase of the waiting time in the queue. Instead of manually trying to identify the best allocation strategy to apply, we have coupled a multi-objective evolutionary algorithm to the simulation model in order to uncover automatically a function to rank the calls in the best order for attendance satisfying multiple and sometimes conflicting goals.
This paper presents a system for user-assisted reverse modeling: from digitized point-cloud to solid models ready to be used in a CAD modeling system. Our approach consists in the following steps: segmentation, fittin...
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ISBN:
(纸本)9781479974924
This paper presents a system for user-assisted reverse modeling: from digitized point-cloud to solid models ready to be used in a CAD modeling system. Our approach consists in the following steps: segmentation, fitting, and constructive model discovery. Each of these steps are based on evolutionary algorithms. The obtained objects can then be further edited or parameterized by users and fitted to adapt their shape to different point-clouds.
Rocket-based combined cycle (RBCC) engines are an airbreathing propulsion technology that offers considerable potential for efficient access-to-space. Successful design of RBCC-powered space transport systems requires...
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Rocket-based combined cycle (RBCC) engines are an airbreathing propulsion technology that offers considerable potential for efficient access-to-space. Successful design of RBCC-powered space transport systems requires reliable databases for both vehicle and engine performance, calling for an effective sampling method to accurately resolve non-linear characteristics in vast design space. This paper presents an optimal sampling strategy based on the function gradients to realize efficient database construction based on evolutionary algorithms and assesses its effectiveness by applying the methodology to various test functions with multiple objectives as well as surrogate models representing scramjet intake characteristics for validation. (C) 2015 Published by Elsevier Ltd.
The only rigorous approaches for achieving a numerical proof of optimality in global optimization are interval-based methods that interleave branching of the search-space and pruning of the subdomains that cannot cont...
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ISBN:
(纸本)9783319232195;9783319232188
The only rigorous approaches for achieving a numerical proof of optimality in global optimization are interval-based methods that interleave branching of the search-space and pruning of the subdomains that cannot contain an optimal solution. State-of-the-art solvers generally integrate local optimization algorithms to compute a good upper bound of the global minimum over each subspace. In this document, we propose a cooperative framework in which interval methods cooperate with evolutionary algorithms. The latter are stochastic algorithms in which a population of candidate solutions iteratively evolves in the search-space to reach satisfactory solutions. Within our cooperative solver Charibde, the evolutionary algorithm and the interval-based algorithm run in parallel and exchange bounds, solutions and search-space in an advanced manner via message passing. A comparison of Charibde with state-of-the-art interval-based solvers (GlobSol, IBBA, Ibex) and NLP solvers (Couenne, BARON) on a benchmark of difficult COCONUT problems shows that Charibde is highly competitive against non-rigorous solvers and converges faster than rigorous solvers by an order of magnitude.
Gradient methods and their value in single-objective, real-valued optimization are well-established. As such, they play a key role in tackling real-world, hard optimization problems such as deformable image registrati...
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ISBN:
(纸本)9781450334723
Gradient methods and their value in single-objective, real-valued optimization are well-established. As such, they play a key role in tackling real-world, hard optimization problems such as deformable image registration (DIR). A key question is to which extent gradient techniques can also play a role in a multi-objective approach to DIR. We therefore aim to exploit gradient information within an evolutionary-algorithm-based multi-objective optimization framework for DIR. Although an analytical description of the multi-objective gradient (the set of all Pareto-optimal improving directions) is available, it is nontrivial how to best choose the most appropriate direction per solution because these directions are not necessarily uniformly distributed in objective space. To address this, we employ a Monte-Carlo method to obtain a discrete, spatially-uniformly distributed approximation of the set of Pareto-optimal improving directions. We then apply a diversification technique in which each solution is associated with a unique direction from this set based on its multi- as well as single-objective rank. To assess its utility, we compare a state-of-the-art multi-objective evolutionary algorithm with three different hybrid versions thereof on several benchmark problems and two medical DIR problems. Results show that the diversification strategy successfully leads to unbiased improvement, helping an adaptive hybrid scheme solve all problems, but the evolutionary algorithm remains the most powerful optimization method, providing the best balance between proximity and diversity.
This paper presents the development of web logistics system to collect products, whose objective to minimize the transport cost and finding the best routes and represent them graphically on a map. The routing problem ...
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ISBN:
(纸本)9781479988709
This paper presents the development of web logistics system to collect products, whose objective to minimize the transport cost and finding the best routes and represent them graphically on a map. The routing problem studied in this paper uses vehicle capacity constraints, time windows and dynamic constraints based on the domain model of the system. The method proposed of resolution was genetic algorithm, which is widely used in complex problems of combinatorial explosion. The results show that the system is efficient to find good route and the model is flexible the restrictions imposed the problem and future.
On the design of a hybrid renewable energy system multiple objectives are in general required to be optimized simultaneously. This study presents a general multi-objective combinatorial model for optimizing the hybrid...
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ISBN:
(纸本)9781467376792
On the design of a hybrid renewable energy system multiple objectives are in general required to be optimized simultaneously. This study presents a general multi-objective combinatorial model for optimizing the hybrid PV-wind-diesel-battery system configuration. The model considers four objectives, i.e., minimizing the lifetime system cost, lifetime CO2 and SO2 emissions and maximizing the system output power. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) approach is employed to obtain a set of Pareto optimal solutions to the problem. Each solution corresponds to a non-inferior design, i.e., a good combination of PV, wind, diesel and battery. By further considering the practical situation, a satisfied design could be selected.
This paper presents new advances in development of dedicated evolutionary algorithms (EA) for large non-linear constrained optimization problems. The primary objective of our research is a significant increase of the ...
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ISBN:
(纸本)9781479974924
This paper presents new advances in development of dedicated evolutionary algorithms (EA) for large non-linear constrained optimization problems. The primary objective of our research is a significant increase of the computational efficiency of the standard EA. The EA are understood here as Genetic algorithms using decimal chromosomes, three standard operators: selection, crossover, and mutation, as well as additional new speed-up techniques. So far we have preliminarily proposed several general concepts, including smoothing and balancing, a'posteriori solution error analysis and related techniques, as well as an adaptive step-by-step mesh refinement. We discuss here the efficiency of chosen speed-up techniques using simple but demanding benchmark problems, including residual stress analysis in elastic-perfectly plastic bodies under cyclic loadings, and physically based smoothing of experimental data. Particularly, we consider a smoothing technique using average solution curvature, new criteria for selection based on global solution error, as well as a step-by-step mesh refinement combined with smoothing. Preliminary numerical results clearly indicate a possibility of significant acceleration of calculations, as well as practical application of the improved EA to the optimization problems considered.
This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing. include...
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ISBN:
(纸本)9780791846483
This paper presents an effective bearing fault parameter identification scheme based on evolutionary optimization techniques. Three seeded faults in the rotating machinery supported by the test roller bearing. include inner race fault, outer race fault and a single ball defect. The fault related features are extracted experimentally by processing the acquired vibration signals in both the time and frequency domain. Techniques based on the power spectral density (PSD) and wavelet transform (WT) are utilized for feature extraction. The sensitivity of the proposed method is investigated under varying operating speeds and radial bearing load. In this study, the inverse problem of parameter identification is investigated. The problem of parameter identification is recast as an optimization problem and two well known evolutionary algorithms, differential evolution (DE) and particle swarm optimization (PSO), are used to identify system parameters given a system response. For online parameter identification, differential evolution outperforms particle both in terms of adaptability and tighter convergence properties. The distinction between the two methods is not distinctively obvious on the offline parameter identification problem.
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