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Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation

Mashwani, WK and Salhi, A (2016) 'Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation.' Applied Soft Computing Journal, 39. 292 - 309. ISSN 1568-4946

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© 2015 Elsevier B.V. All rights reserved. In last two decades, multiobjective optimization has become main stream and various multiobjective evolutionary algorithms (MOEAs) have been suggested in the field evolutionary computing (EC) for solving hard combinatorial and continuous multiobjective optimization. Most of MOEAs employ single evolutionary operators such as crossovers, mutation and selection for population evolution. In this paper, we have suggested a multiobjective evolutionary algorithm based on multimethod (MMTD) with dynamical resources allocation for coping with continuous multi-objective optimization problems (MOPs). The suggested algorithm has employed two well-known population based stochastic algorithms as a constituent algorithms namely MOEA/D and NSGA-II for population evolution based on dynamic resource allocation scheme. We have examined the performance of our proposed MMTD dealing with two different MOPs test suites, the widely used ZDT problems and recently formulated test instances for the special session of MOEAs competition in 2009 IEEE congress on evolutionary computation (CEC'09). The experimental results accomplished by suggested MMTD are more promising than some state-of-the-art MOEAs in terms of inverted generational distance (IGD)-metric values obtained for most test problems.

Item Type: Article
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 26 Feb 2016 14:00
Last Modified: 30 Jan 2019 16:19

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