Hybrid particle swarm optimization algorithm for multi-objective scheduling in service-workflows

被引：0
|
作者：
Zhang X. [1 ]
Wang Q. [1 ]
机构：
[1] School of Computer Science and Engineering, Southeast University
来源：
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | 2010年 / 40卷 / 03期
关键词：
Directed acrylic graph (DAG); Multi-objective optimization; Pareto optimal set; Particle swarm optimization (PSO); Service-workflow;
D O I：
10.3969/j.issn.1001-0505.2010.03.011
中图分类号：
学科分类号：
摘要：
A multi-objective hybrid PSO (particle swarm optimization) method is proposed for the time-cost optimization problem in service-workflows, a NP-Hard problem. Characteristics of service-workflows are analyzed. Discrete particles are constructed. HMOPSO is included: initial population generation, fitness distribution, population diversity maintainance, outside population and extreme choice. The initial population is generated by setting optimal solutions to single-objective problems. To obtain an evenly distributed Pareto set, an outside population and a meshing method based on the niche technique are introduced. Experimental results show that the proposed algorithm is efficient and effective for the considered problem. Many evenly distributed Pareto sets with high quality are obtained for various characteristic instances.
引用
收藏
页码：491 / 495
页数：4
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