The optimization of many objectives requires a set of optimal solutions known as Pareto solutions. Similarly to the optimization of single objective in evolutionary algorithms (EAs), the Multiobjective evolutionary Al...
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ISBN:
(纸本)9781479986965
The optimization of many objectives requires a set of optimal solutions known as Pareto solutions. Similarly to the optimization of single objective in evolutionary algorithms (EAs), the Multiobjective evolutionary algorithms (MOEAs) also suffer from loss of genetic diversity, allowing the appearance of sparse regions along the Pareto frontier. A mechanism to maintain the population diversity along generations is needed. It is expected that, if diversity is controlled effectively, at the end of the evolutionary process, the Pareto Front optimum will be as uniformly distributed as possible. This paper proposes a new diversity operator that generates artificial solutions to fill sparse regions of the non-dominated set of solutions found by the MOEA. It uses artificial neural networks (ANN) to perform a reverse mapping from the phenotype to the corresponding genotype of an inserted artificial solution. This mechanism was tested with NSGA-II and SPEA2 algorithms. The addition of the diversity operator reached significant improvements in the hypervolume and the spread metrics of the obtained set of solutions non-dominated.
This paper focuses on the process of generating a sequence of sector configurations composed of two airspace component types Sharable Airspace Modules (SAMs) and Sectors Building Blocks (SBBs). An algorithm has been d...
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ISBN:
(纸本)9781479989409
This paper focuses on the process of generating a sequence of sector configurations composed of two airspace component types Sharable Airspace Modules (SAMs) and Sectors Building Blocks (SBBs). An algorithm has been developed that manages the main features of the dynamic sectors configuration (including sector design criteria). In order to make it run efficiently a pre-processing step will be presented to create a graph modelling of the inputs. Based on this initial graph, a mathematical model is defined which can be summarized by a multi-periods geometric graph partitioning problem. State, space, objective function and constraints will be also presented. Due to the induced complexity, a stochastic optimization algorithm based on artificial evolution is then proposed. A two layer chromosome is used for such a genetic algorithm for which recombination operators are proposed. Evaluation of the algorithm will be presented with a comparison to existing tools and operational approach.
The Inventory Routing Problem is an important problem in logistics and known to belong to the class of NP hard problems. In the bi-criteria inventory routing problem the goal is to simultaneously minimize distance cos...
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ISBN:
(纸本)9783319188331;9783319188324
The Inventory Routing Problem is an important problem in logistics and known to belong to the class of NP hard problems. In the bi-criteria inventory routing problem the goal is to simultaneously minimize distance cost and inventory costs. This paper is about the application of indicator-based evolutionary algorithms and swarm algorithms for finding an approximation to the Pareto front of this problem. We consider also robust vehicle routing as a tricriteria version of the problem.
In this article, a comparative study between population based optimization methods with random and restricted search space definition applied in the pattern synthesis of linear antenna arrays is presented. Synthesis p...
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ISBN:
(纸本)9781479983490
In this article, a comparative study between population based optimization methods with random and restricted search space definition applied in the pattern synthesis of linear antenna arrays is presented. Synthesis problem of reduced side lobe level and narrow beamwidth is considered. The design objective further considers the optimization of excitation amplitude and uniform inter element spacing using random and restricted search space definition by particle swarm optimization and differential evolution methods. As examples simulation of 12 and 21 elements have been considered. Effectiveness of the restriction in search space is proved through statistical and parametric analysis. Further comparison with published work has been carried out to prove the superiority of restricted search Particle Swarm Optimization.
Multimodal Optimization (MMO) aims at identifying several best solutions to a problem whereas classical optimization converge often to only one good solution. MMO has been an active research area in the past years and...
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ISBN:
(纸本)9781479975600
Multimodal Optimization (MMO) aims at identifying several best solutions to a problem whereas classical optimization converge often to only one good solution. MMO has been an active research area in the past years and several new evolutionary algorithms have been developed to tackle multimodal problems. In this work, we compare extensively three recent evolutionary algorithms (MoBiDE, Multimodal NSGAII and MOMMOP). Each algorithm uses multiobjectivization, together with niching techniques to address single objective MMO problems. We have fully re-implemented MoBiDE and MM-NSGAII in order to better evaluate their sensitivity to parameter changes and their strengths and weaknesses. We have carefully evaluated all algorithms on the same benchmark functions and with the same parameters settings. The algorithms are also compared to a non-multimodal evolutionary algorithm to better highlight the impact of the multimodal adaptations. Moreover, full access to the detailed results and source code is granted on our website for the ease of reproducibility.
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.
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.
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.
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.
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