Application of Hybrid Particle Swarm Optimization in Resource Constrained Multi-project Scheduling

被引:0
|
作者
Du Hui [1 ,2 ]
Lou Pei-Huang [3 ]
Ye Wen-Hua [3 ]
机构
[1] Zaozhuang Univ, Zaozhuang 277160, Peoples R China
[2] Nanjing Univ Aeronaut & Astronaut, Nanjing 210016, Jiangsu, Peoples R China
[3] Nanjing Univ Aeronaut & Astronaut, Dept Mech Engn, Nanjing 210016, Jiangsu, Peoples R China
关键词
Improved Particle Swarm Optimization (IPSO); Adaptive inertia weight; Simulated Annealing (SA); Resource-Constrained Multi-project Scheduling (RCMPSP); GENETIC ALGORITHM;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The Resource Constrained Multi-project Scheduling Problem (RCMPSP) is a NP-hard optimization problem, which is hard to be solved effectively by using single algorithm. This paper presents a hybrid algorithm based on Improved Particle Swarm Optimization and Simulated Annealing (IPSOSA) algorithm to solve the RCMPSP. Aimed at overcoming the shortcomings of premature convergence of standard PSO, adaptive inertia weight with cyclical attenuation strategy and Simulated Annealing algorithm (SA) are employed in the hybrid algorithm. The proposed IPSOSA was applied to aircraft assembly tooling manufacturing, and we compare the result of the IPSOSA with the results of GA, SA and standard PSO methods. The simulation results and algorithm comparison show that the IPSOSA algorithm is an effective approach for the RCMPSP.
引用
收藏
页码:371 / 379
页数:9
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