DocumentCode
2174363
Title
MOEA/D with guided local search: Some preliminary experimental results
Author
Alhindi, Ahmad ; Qingfu Zhang
Author_Institution
Sch. of Comput. Sci. & Electron. Eng., Univ. of Essex, Colchester, UK
fYear
2013
fDate
17-18 Sept. 2013
Firstpage
109
Lastpage
114
Abstract
Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation into a number of single-objective problem and optimises them in a collaborative manner. This paper investigates how to use the Guided Local Search (GLS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the GLS applies to these subproblems to escape local Pareto optimal solutions. The experimental studies have shown that MOEA/D with GLS outperforms the classical MOEA/D on a bi-objective travelling salesman problem.
Keywords
Pareto optimisation; evolutionary computation; search problems; travelling salesman problems; GLS heuristic; MOEA-D; Pareto optimal solutions; bi-objective travelling salesman problem; guided local search; multiobjective evolutionary algorithm based on decomposition; multiobjective optimisation; single-objective optimisation; Cities and towns; Educational institutions; Pareto optimization; Sociology; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Electronic Engineering Conference (CEEC), 2013 5th
Conference_Location
Colchester
Type
conf
DOI
10.1109/CEEC.2013.6659455
Filename
6659455
Link To Document