Multiobjective Optimization Based on Rough Set Theory: Methodology and Application de Abd Elsameea Hussein, Mohamed, Allah A. Mousa, Abd, A. Elsaw: New paperback (2016)

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Detalles bibliogr�ficos

T�tulo: Multiobjective Optimization Based on Rough Set Theory: Methodology and Application
Editor: LAP LAMBERT Academic Publishing
Fecha de publicaci�n: 2016
Idioma: Ingl�s
ISBN 10: 3659846856
ISBN 13: 9783659846854
Encuadernaci�n: paperback
Estado: New
Tipo de libro: book

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Sinopsis

In this book we present a new optimization algorithm, the proposed algorithm operates in two phases: in the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems (Economic Environmental Dispatch of Power Systems).

Rese�a del editor

In this book we present a new optimization algorithm, the proposed algorithm operates in two phases: in the first one, multiobjective version of genetic algorithm is used as search engine in order to generate approximate true Pareto front. This algorithm based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε-dominance. Then, in the second stage, rough set theory is adopted as local search engine in order to improve the spread of the solutions found so far. The results, provided by the proposed algorithm for benchmark problems, are promising when compared with exiting well-known algorithms. Also, our results suggest that our algorithm is better applicable for solving real-world application problems (Economic Environmental Dispatch of Power Systems).

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