Search results for "evolutionary optimization"

showing 9 items of 9 documents

Exploring the use of multi-gene genetic programming in regional models for the simulation of monthly river runoff series

2023

The use of new data-driven approaches based on the so-called expert systems to simulate runoff generation processes is a promising frontier that may allow for overcoming some modeling difficulties related to more complex traditional approaches. The present study highlights the potential of expert systems in creating regional hydrological models, for which they can benefit from the availability of large database. Different soft computing models for the reconstruction of the monthly natural runoff in river basins are explored, focusing on a new class of heuristic models, which is the Multi-Gene Genetic Programming (MGGP). The region under study is Sicily (Italy), where a regression based rain…

Artificial Neural NetworkSoft computingEnvironmental EngineeringRegional Runoff ModelSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaGenetic ProgrammingEnvironmental ChemistryEvolutionary OptimizationSafety Risk Reliability and QualityGeneral Environmental ScienceWater Science and Technology
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An execution, monitoring and replanning approach for optimal energy management in microgrids

2011

abstract This work develops a new approach for optimal energy management of electrical distribution ‘smart-grids’. Optimality aims at improving sustainability through the minimization of carbon emissions and atreducing production costs and maximizing quality. Input data are the forecasted loads and productionsfrom renewable generation units, output data are a set of control actions for the actuators. Theconsidered electrical distribution system includes storage units that must be considered over a 24 h timeinterval, to consider an entire charge and discharge cycle. The objectives for the optimal management ofdistributed (renewables and not) generation are technical, economical and environme…

EngineeringOptimization problemEnergy managementbusiness.industryMechanical EngineeringControl (management)Distributed energy resources management Multi-objective evolutionary optimization MicrogridsBuilding and ConstructionInterval (mathematics)PollutionIndustrial and Manufacturing EngineeringScheduling (computing)Reliability engineeringSettore ING-IND/33 - Sistemi Elettrici Per L'EnergiaGeneral EnergyProduction (economics)MinificationElectrical and Electronic EngineeringInterruptbusinessSimulationCivil and Structural Engineering
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A NEW REPRESENTATION OF ENERGY STORAGE SYSTEMS OPERATION USING FOURIER THEORY IN OPTIMAL SMART GRIDS MANAGEMENT

2012

This paper investigates the possibility to use a new modeling of Energy Storage Systems based on zero integral functions. Such functions represent the course of the energy level stored in batteries during the solution of optimal management problems in smart-grids. Storage devices, such as all the other components that are required to meet an integral capacity constraint along the dispatch time, must show the same State of Charge at the start and at the end of the timeframe considered for operation. In this paper, a set of sinusoidal functions have been used for the synthesis of the charge and discharge course of energy Storage Systems. Such representation allows to eliminate the difficult c…

Mathematical optimizationEngineeringbusiness.industryEnergy storageEvolutionary computationConstraint (information theory)integral constraints optimal management distributed energy resources multi-objective evolutionary optimization smart grids.Settore ING-IND/33 - Sistemi Elettrici Per L'Energiasymbols.namesakeSmart gridState of chargeFourier analysissymbolsbusinessRepresentation (mathematics)Energy (signal processing)
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Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management

2013

This paper presents a novel nature-inspired multi-objective optimization algorithm. The method extends the glowworm swarm particles optimization algorithm with algorithmical enhancements which allow to identify optimal pareto front in the objectives space. In addition, the system allows to specify constraining functions which are needed in practical applications. The framework has been applied to the power dispatch problem of distribution systems including Distributed Energy Resources (DER). Results for the test cases are reported and discussed elucidating both numerical and complexity analysis.

Mathematical optimizationMeta-optimizationComputer scienceDerivative-free optimizationTest functions for optimizationSwarm behaviourMulti-swarm optimizationevolutionary optimization swarm-optimization pareto optimization micro-gridsMulti-objective optimizationMetaheuristicEngineering optimization
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A Visualizable Test Problem Generator for Many-Objective Optimization

2022

Visualizing the search behavior of a series of points or populations in their native domain is critical in understanding biases and attractors in an optimization process. Distancebased many-objective optimization test problems have been developed to facilitate visualization of search behavior in a two-dimensional design space with arbitrarily many objective functions. Previous works have proposed a few commonly seen problem characteristics into this problem framework, such as the definition of disconnected Pareto sets and dominance resistant regions of the design space. The authors’ previous work has advanced this research further by providing a problem generator to automatically create use…

Mathematical optimizationProcess (engineering)Computer sciencevisualisointimulti-objective test problemsPareto principleevolutionary optimizationmonitavoiteoptimointiMulti-objective optimizationTheoretical Computer ScienceDomain (software engineering)Visualizationtest suiteRange (mathematics)avoin lähdekoodioptimointiComputational Theory and MathematicsTest suitebenchmarkingongelmanratkaisuvisualizationSoftwareGenerator (mathematics)IEEE Transactions on Evolutionary Computation
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Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms

2016

We study how different types of preference information coming from a human decision maker can be utilized in an interactive multiobjective evolutionary optimization algorithm (MOEA). The idea is to convert different types of preference information into a unified format which can then be utilized in an interactive MOEA to guide the search towards the most preferred solution(s). The format chosen here is a set of reference vectors which is used within the interactive version of the reference vector guided evolutionary algorithm (RVEA). The proposed interactive RVEA is then applied to the multiple-disk clutch brake design problem with five objectives to demonstrate the potential of the idea in…

Optimization problemLinear programmingComputer science0211 other engineering and technologiesEvolutionary algorithmInteractive evolutionary computationpreference information02 engineering and technologyMachine learningcomputer.software_genredecision makingEvolutionary computationSet (abstract data type)vectors0202 electrical engineering electronic engineering information engineeringta113021103 operations researchbusiness.industryta111Approximation algorithmPreferencemultiobjective evolutionary optimization algorithm020201 artificial intelligence & image processingArtificial intelligencebusinessoptimizationcomputer2016 IEEE Symposium Series on Computational Intelligence (SSCI)
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Influence of the evolutionary optimization parameters on the optimal topology

2016

Topological optimization can be considered as one of the most general types of structural optimization. Between all known topological optimization techniques, the Evolutionary Structural Optimization represents one of the most efficient and easy to implement approaches. Evolutionary topological optimization is based on a heuristic general principle which states that, by gradually removing portions of inefficient material from an assigned domain, the resulting structure will evolve towards an optimal configuration. Usually, the initial continuum domain is divided into finite elements that may or may not be removed according to the chosen efficiency criteria and other parameters like the spee…

Topology optimization Evolutionary optimization rejection ratio FEM efficiency criteriaMathematical optimizationFinal topologyComputer scienceContinuum (topology)Heuristic (computer science)Topology optimizationConvergence (routing)Multi-swarm optimizationTopologyMetaheuristicTopology (chemistry)
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Étude de statistiques combinatoires et de leur impact en optimisation évolutionnaire

2021

This thesis studies combina­­­torial objects, with both an algorithmic and a combinatorial point of view. In the combinatorial part, we take care first, the enumeration of Catalan words avoiding pairs of patterns of length three, presenting the proofs of each case with various enumeration methods. Catalan words are particular growth-restricted words counted by the eponymous integer sequence. More precisely­­­­, we systematically explore the structural properties of the sets of words under consideration and give enumerating results by constructive bijections or bivariate generating functions with respect to the length and descent number. Then, we study a sorting machine using two stacks in s…

[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH]Genetic algorithmCombinatoricsEvolutionary optimizationOptimisation evolutionaireAlgorithme genetiqueCombinatoireStatistiques combinatoireCombinatorial statistics
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Multi-modal search for multiobjective optimization: an application to optimal smart grids management

2012

This paper studies the possibility to use efficient multimodal optimizers for multi-objective optimization. In this paper, the application area considered for such new approach is the optimal dispatch of energy sources in smart microgrids. The problem indeed shows a non uniform Pareto front and requires efficient optimal search methods. The idea is to exploit the potential of agents in population-based heuristics to improve diversity in the Pareto front, where solutions show the same rank and are thus equally weighted. Since Pareto dominance is at the basis of the theory of multi-objective optimization, most algorithms show the non dominance ranking as quality indicator, with some problem i…

education.field_of_studyMathematical optimizationEngineeringbusiness.industryPopulationPareto principleEvolutionary algorithmmultimodal functions optimization optimal management distributed energy resources multi-objective evolutionary optimization smart gridsMulti-objective optimizationSettore ING-IND/33 - Sistemi Elettrici Per L'EnergiaRankingGenetic algorithmeducationEnergy sourcebusinessHeuristics
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