Result: A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization
Title:
A hybrid shuffled complex evolution approach based on differential evolution for unconstrained optimization
Authors:
Source:
Applied mathematics and computation. 217(12):5822-5829
Publisher Information:
Amsterdam: Elsevier, 2011.
Publication Year:
2011
Physical Description:
print, 51 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Equations algébriques et transcendantes non linéaires, Nonlinear algebraic and transcendental equations, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Programmation mathématique numérique, Numerical methods in mathematical programming, Optimisation et calcul variationnel numériques, Numerical methods in optimization and calculus of variations, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse numérique, Numerical analysis, Análisis numérico, Calcul variationnel, Variational calculus, Cálculo de variaciones, Equation algébrique, Algebraic equation, Ecuación algebraica, Equation non linéaire, Non linear equation, Ecuación no lineal, Equation transcendante, Transcendental equation, Ecuación trascendente, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Méthode mathématique, Mathematical method, Método matemático, Méthode optimisation, Optimization method, Método optimización, Méthode stochastique, Stochastic method, Método estocástico, Optimisation sans contrainte, Unconstrained optimization, Optimización sin restricción, Programmation mathématique, Mathematical programming, Programación matemática, Programmation stochastique, Stochastic programming, Programación estocástica, Solution globale, Global solution, Solución global, Stratégie recherche, Search strategy, Estrategia investigación, Virgule flottante, Floating point, Coma flotante, 49XX, 65H20, 65K05, 65K10, 65Kxx, Benchmark functions, Differential evolution, Evolutionary algorithms, Shuffled complex evolution algorithm
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mechanical Engineering, PPGEM, Pontifical Catholic University of Paraná, PUCPR, Imaculada Conceição, 1155, 80215-901 Curitiba, Paraná, Brazil
Department of Electrical Engineering, PPGEE Federal University of Paraná, UFPR, Polytechnic Center, 81531-970 Curitiba, Paraná, Brazil
Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Paraná, PUCPR, Imaculada Conceição, 1155, 80215-901 Curitiba, Paraná, Brazil
Institute of Technology for Development (LACTEC), Eletroelectronics Department (DPEE), Electrical System Division (DVSE), Federal University of Paraná, UFPR BR 116, Km 98, Jardim das Américas, 81531-980 Curitiba, Paraná, Brazil
Department of Electrical Engineering, PPGEE Federal University of Paraná, UFPR, Polytechnic Center, 81531-970 Curitiba, Paraná, Brazil
Industrial and Systems Engineering Graduate Program, PPGEPS, Pontifical Catholic University of Paraná, PUCPR, Imaculada Conceição, 1155, 80215-901 Curitiba, Paraná, Brazil
Institute of Technology for Development (LACTEC), Eletroelectronics Department (DPEE), Electrical System Division (DVSE), Federal University of Paraná, UFPR BR 116, Km 98, Jardim das Américas, 81531-980 Curitiba, Paraná, Brazil
ISSN:
0096-3003
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.23885094
Database:
PASCAL Archive
Further Information
Numerous optimization methods have been proposed for the solution of the unconstrained optimization problems, such as mathematical programming methods, stochastic global optimization approaches, and metaheuristics. In this paper, a metaheuristic algorithm called Modified Shuffled Complex Evolution (MSCE) is proposed, where an adaptation of the Downhill Simplex search strategy combined with the differential evolution method is proposed. The efficiency of the new method is analyzed in terms of the mean performance and computational time, in comparison with the genetic algorithm using floating-point representation (GAF) and the classical shuffled complex evolution (SCE-UA) algorithm using six benchmark optimization functions. Simulation results and the comparisons with SCE-UA and GAF indicate that the MSCE improves the search performance on the five benchmark functions of six tested functions.