Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems

Task scheduling problem in heterogeneous systems (TSPHS) is a multiobjective optimization problem (MOP). Multiobjective evolutionary algorithms (MOEA) are well suited for solving multiobjective task scheduling problem. In this paper, the two conflicting objectives namely, makespan and reliability ar...

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Bibliographic Details
Published in:Applied soft computing Vol. 11; no. 2; pp. 2725 - 2734
Main Authors: Chitra, P., Rajaram, R., Venkatesh, P.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.03.2011
Subjects:
Local search
Makespan
Multiobjective evolutionary algorithms
Multiobjective Optimization
Reliability
Task scheduling
Task scheduling
Local search
Multiobjective evolutionary algorithms
Makespan
Multiobjective Optimization
Reliability
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:Task scheduling problem in heterogeneous systems (TSPHS) is a multiobjective optimization problem (MOP). Multiobjective evolutionary algorithms (MOEA) are well suited for solving multiobjective task scheduling problem. In this paper, the two conflicting objectives namely, makespan and reliability are considered. The performance of MOEAs can be improved by hybridization with local search. Hybridization of MOEAs improves the convergence speed to Pareto front. Simple neighborhood search (SNS) algorithm is used as the local search algorithm. The weighted-sum based approach for solving the MOP with its hybrid version is compared. Then the two MOEAs: SPEA2 and NSGA-II are compared with each other in the pure and hybrid version for random task graphs and also for a real-time numerical application graph. The simulations confirm that Hybrid NSGA-II is best suited for solving the task scheduling problem.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2010.11.003