A novel algorithm combining finite state method and genetic algorithm for solving crude oil scheduling problem.
A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method.
Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China.
Qian-Qian
QQ
Duan
Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China.
Gen-Ke
GK
Yang
Department of Automation and Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China.
Chang-Chun
CC
Pan
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D010363:Pattern Recognition, Automated / Q000379:methods*; D010578:Petroleum / Q000600:supply & distribution*; D011340:Problem Solving; D015203:Reproducibility of Results
D010578:Petroleum