Optimization problems
An immune artificial algorithm for dynamic optimization problems: a case of study
... DTC (Dynamic T-Cell) is an algorithm inspired on our TCELL model, which we propose to solve dynamic optimization problems. DTC operates on four popu- lations, corresponding to the groups in which the ...
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Artificial immune system for solving global optimization problems
... Our results are compared with respect to a Differential Evolution algorithm (DE) [4], a Particle Swarm Optimizer (PSO) [4], a simple Evolutionary Algorithm (SEA) [4], an immunological algo- rithm for continuous global ...
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Hybridizing an immune artificial algorithm with simulated annealing for solving constrained optimization problems
... Coello Coello and Cruz-Cort´es have proposed an algorithm based on the clonal selection theory for solving constrained optimization problems. The au- thors experimented with both binary and real-value ...
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TítuloA minimum weight with stress constraints FEM approach for topology structural optimization problems
... However, since Bendsøe and Kikuchi proposed the basic concepts [5] in 1988, most of topology structural optimization problems have been routinely stated in terms of minimum compliance (maximum stiffness) ...
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Efficient Hill Climber for Constrained Pseudo-Boolean Optimization Problems
... Efficient hill climbers have been recently proposed for single- and multi-objective pseudo-Boolean optimization problems. For k-bounded pseudo-Boolean functions where each vari- able appears in at most a ...
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An Integrated Framework for the Representation and Solution of Stochastic Energy Optimization Problems
... Systems Optimization is increasing its importance due to regulations and de-regulations of the energy sector and the set- ting of targets such as the European Union’s ...systems optimization problems ...
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Memetic differential evolution for constrained numerical optimization problems
... by Krasnogor and Smith in [24] referring memetic algorithms with multiple LSOs, the coordination among the components becomes more complex (see Fig. 1.2(b)), since a trade-off among LSOs must be considered in order to ...
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A review of simheuristics: extending metaheuristics to deal with stochastic combinatorial optimization problems
... an optimization-based simulation framework, similar to that of [36], but where refinements are performed to the analytic ...recursive optimization–simulation approach to a stochastic supply chain planning ...
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A Bio-inspired algorithm to solve Dynamic Multi-Objective Optimization Problems
... scalar optimization subproblems. This is the basic idea behind many tradi- tional mathematical programming methods (see Chapter 2 of this document). The same idea is adopted by MOEA/D, i.e., it decomposes the MOP ...
166
Models and Algorithms for Deterministic and Stochastic Optimization Problems
... original optimization problem is then reduced to a finite dimensional convex optimization problem with linearly approximated conflict-free constraints on the waypoints and a quadratic objective ...the ...
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Optimization of a mechanical design problem with the modified bacterial foraging algorithm
... foraging optimization algorithm (BFOA), originally pro- posed to solve unconstrained single-objective optimization problems ...design problems called Modified Bacterial Foraging ...
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An experimental comparison of Variable Neighborhood Search variants for the minimization of the vertex-cut in layout problems
... A experimental comparison of three VNS variants for two vertex-cut mini- mization problems has been presented. In particular, we have considered the Vertex Separation Problem and the SumCut Minimization Problem. ...
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IDENTIFICATION AND CONTROL METHODS UTILIZING RANK AND CARDINALITY OPTIMIZATION APPROACH
... Rank-constrained optimization has gained increased attention in the last ...convex optimization and the development of easy-to-use optimization software have helped to increase the usage of such ...
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Acercamiento multi-objetivo para la minimización de casos de prueba en Líneas de Producción de Software.
... Many optimization problems of practical as well as theoretical importance consist of the search for a “best” configuration of a set of variables to achieve some ...of problems called Combinatorial ...
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Upgrading evolutionary algorithms through multiplicity for multiobjective optimization in job shop scheduling problems
... In previous works the ability of CPS-MCPC (an evolutionary, co-operative, population search method with multiple crossovers per couple) to build well delineated Pareto fronts in diverse multiobjective optimization ...
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Cheminformatic models to predict binding affinities to human serum albumin
... crossover and mutation are allowed to increase the search in the model space. Here, mutations represent modifications in the model equation, while crossovers correspond to transfers of some equation terms between models. ...
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Implementación en hardware de sistemas de alta fiabilidad basados en metodologías estocásticas
... exigent problems by using the current CMOS technology but replacing the classical computing way developed by Von Neumann by other forms of unconventional ...computing problems more efficiently than ...
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OPTIMIZATION METHODS APPLIED TO NETWORK PLANNING PROBLEMS
... guaranteed. Optimization methods are a better choice from the point of view of the optimality of the ...of optimization [60; 133]. Details about how optimization models deal with this situation are ...
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Commercial Aircraft Trajectory Planning based on Multiphase Mixed-Integer Optimal Control
... b) A multiphase optimal control approach to aircraft trajectory planning [71]. The multiphase optimal control problem is converted into a Nlp problem, first making the unknown switching times part of the state, and then ...
183
Automatic design of Ant Colony Optimization for permutation problems
... As we presented in chapter 2, the probabilistic selection is biased by two parame- ters: α and β. This parameters establish the influence of the pheromone trails and the heuristic information. However, in certain cases, ...
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