Multiobjective Optimization Based on Rough Set Theory

Mohamed Abd Elsameea Hussein

ISBN 10: 3659846856 / ISBN 13: 9783659846854

Editore: LAP LAMBERT Academic Publishing Feb 2016, 2016

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Neuware -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 ¿-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).Books on Demand GmbH, Überseering 33, 22297 Hamburg 128 pp. Englisch.

Codice articolo 9783659846854

Dati bibliografici

• Titolo: Multiobjective Optimization Based on Rough Set Theory
• Editore: LAP LAMBERT Academic Publishing Feb 2016
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3659846856
• ISBN 13: 9783659846854
• Rilegatura: Taschenbuch
• Condizione: Neu

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Riassunto

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).