Mashwani, Wali Khan and Salhi, Abdel (2016) Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation. Applied Soft Computing, 39. pp. 292-309. DOI https://doi.org/10.1016/j.asoc.2015.08.059
Mashwani, Wali Khan and Salhi, Abdel (2016) Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation. Applied Soft Computing, 39. pp. 292-309. DOI https://doi.org/10.1016/j.asoc.2015.08.059
Mashwani, Wali Khan and Salhi, Abdel (2016) Multiobjective evolutionary algorithm based on multimethod with dynamic resources allocation. Applied Soft Computing, 39. pp. 292-309. DOI https://doi.org/10.1016/j.asoc.2015.08.059
Abstract
In the last two decades, multiobjective optimization has become main stream and various multiobjective evolutionary algorithms (MOEAs) have been suggested in the field of evolutionary computing (EC) for solving hard combinatorial and continuous multiobjective optimization problems. Most MOEAs employ single evolutionary operators such as crossover, mutation and selection for population evolution. In this paper, we suggest a multiobjective evolutionary algorithm based on multimethods (MMTD) with dynamic resource allocation for coping with continuous multi-objective optimization problems (MOPs). The suggested algorithm employs two well known population based stochastic algorithms namely MOEA/D and NSGA-II as constituent algorithms for population evolution with a dynamic resource allocation scheme. We have examined the performance of the proposed MMTD on two different MOPs test suites: the widely used ZDT problems and the recently formulated test instances for the special session on MOEAs competition of the 2009 IEEE congress on evolutionary computation (CEC’09). Experimental results obtained by the suggested MMTD are more promising than those of some state-of-the-art MOEAs in terms of the inverted generational distance (IGD)-metric on most test problems.
Item Type: | Article |
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Uncontrolled Keywords: | Multiobjective optimization; Pareto optimality; MOEA/D; NSGA-II; Multimethod (MMTD) |
Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 26 Feb 2016 14:00 |
Last Modified: | 30 Oct 2024 20:21 |
URI: | http://repository.essex.ac.uk/id/eprint/16030 |