Mobile Agent Multi-task Scheduling Algorithm
Liyun Chen · Jisuanji gongcheng · 2008
A hybrid genetic algorithm is proposed to search the optimal solution of mobile agent multi-task matching and scheduling problem. The problem model and chromosome representation are defined. The initial population is generated by the taboo search and random selecting method. And a new crossover mechanism is designed to make the new scheduling evolved by crossover mechanism valid. To accelerate the algorithm convergence, taboo search and tasks load mutation operator is adopted. Also the best chromosome preserving strategy is adopted to keep optimal solution non-decreasing performance. Simulation results of 18 graphs combined by 2 kinds of task nodes, 3 kinds of communication costs and 3 kinds of host nodes show that the algorithm can get 37.1% improvement compared with standard genetic algorithm.