A Hybrid Method for Task Scheduling
Task Graph Scheduling is an NP-Hard problem. In this paper a new hybrid method based on Genetic Algorithm and Learning Automata is proposed. The hybrid method begins with an initial population of randomly generated chromosomes. A chromosome is Equal to learning automaton. Each Chromosome by itself represents a stochastic scheduling. The scheduling is optimized within a learning process. Compared with current genetic approaches to DAG scheduling better results are achieved. The main reason underlying this achievement is that an evolutionary approach such as genetics, looks for the best chromosomes within genetic populations whilst in the approach presented in this paper hybrid algorithm is applied to find the most suitable position for the genes and looking for the best chromosomes too. The scheduling resulted from applying our hybrid algorithm to some benchmark task graphs are compared with the existing ones
Genetic Algorithm Learning Automata Multiprocessor Systems Scheduling Task Graph
Habib Motee Ghader Kambiz Fakhr Saeed Ahmadi Arzil
Young Research Club Islamic Azad University- Tabriz Branch Tabriz, Iran Computer Engineering Department Industrial management Institute of Azerbayjan Tabriz, Iran
国际会议
上海
英文
91-95
2010-06-22（万方平台首次上网日期，不代表论文的发表时间）