A multimethod search approach based on adaptive generations level

Author

Mashwani, W. Khan

Author_Institution

Dept. of Math. Sci., Univ. of Essex, Colchester, UK

Volume

1

fYear

2011

fDate

26-28 July 2011

Firstpage

23

Lastpage

27

Abstract

Integration of single methods into hybrid are researched scarcely in the recent past. This paper investigates the effect of integration of single methods: MOEA/D and NSGA-II in a multimethod search approach, so-called, MMTD, based on self-adaptive generations level proposed in this paper. During implementation, MMTD borrows some concepts from the specialized literature of evolutionary multi-objective optimization (EMO). The synergetic combination of MOEA/D and NSGA-II can unleash their full strength and biases self-adaptively in MMTD framework and can solve efficiently two set of problems: 1) ZDT test problems, 2) cec09 unconstrained test instances, as compared to the state-of-the-art EMO methods, MOEA/D only and NSGA-II only.

Keywords

genetic algorithms; search problems; MMTD; MOEA/D; NSGA-II; ZDT test problem; cec09 unconstrained test instances; evolutionary multiobjective optimization; multimethod search approach; self-adaptive generations level; Approximation algorithms; Benchmark testing; Measurement; Pareto optimization; Search problems; Tuning; MOEA/D and NSGA-II; Multiobjective optimization; multimethod search approach (MMTD);

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2011 Seventh International Conference on

Conference_Location

Shanghai

ISSN

2157-9555

Print_ISBN

978-1-4244-9950-2

Type

conf

DOI

10.1109/ICNC.2011.6022052

Filename

6022052