Search results for "Imperialist competitive algorithm"

showing 3 items of 3 documents

Imperialist competitive algorithm for determining the parameters of a Sugeno fuzzy controller

2020

Abstract We used an imperialist competitive algorithm to determine the parameters of a fuzzy controller of type Sugeno that would ensure a good unit step response of a second-order single-input and single-output automatic system.

Computer scienceControl theory020208 electrical & electronic engineering010401 analytical chemistry0202 electrical engineering electronic engineering information engineeringImperialist competitive algorithm02 engineering and technology01 natural sciencesFuzzy logic0104 chemical sciencesInternational Journal of Advanced Statistics and IT&C for Economics and Life Sciences
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Tuning a Mamdani Fuzzy Controller with an Imperialist Competitive Algorithm

2021

We have implemented a fuzzy controller with a view to regulating a single-input and single-output second-order linear system. The fuzzy controller was a Mamdami proportional-derivative controller. To determine the parameters of the fuzzy controller we have used an imperialist competitive algorithm. This type of algorithm has a long running time so we implemented also a parallel version of the algorithm that we run on HPC Zamolxes located at the Engineering Faculty of “Lucian Blaga” University from Sibiu. Because we did not have on this computer a version of MATLAB allowing to write parallel algorithms, we implemented the entire application in the C language using the MPI library.

Computer scienceControl theoryLinear systemParallel algorithmImperialist competitive algorithmMATLABcomputerFuzzy logicRunning timecomputer.programming_language
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A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization

2018

We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distr…

Pareto optimalityPareto-tehokkuus0209 industrial biotechnologyMathematical optimizationOptimization problemComputer sciencemodel managementpäätöksentekoEvolutionary algorithmInteractive evolutionary computation02 engineering and technologyEvolutionary computationTheoretical Computer Science020901 industrial engineering & automationKrigingalgoritmit0202 electrical engineering electronic engineering information engineeringvektorit (matematiikka)multiobjective optimizationcomputational costsurrogate-assisted evolutionary algorithmsBayesian optimizationta113Cultural algorithmpareto-tehokkuusbayesilainen menetelmäta111Approximation algorithmImperialist competitive algorithmmonitavoiteoptimointiKrigingkoneoppiminenComputational Theory and Mathematics020201 artificial intelligence & image processingreference vectorsSoftwareIEEE Transactions on Evolutionary Computation
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