A Multi-Objective Discrete Particle Swarm Optimization Algorithm for SLA-Aware Service Composition Problem

YIN Hao; ZHANG Chang-sheng; ZHANG Bin; SUN Ruo-nan; LIU Ting-ting

doi:10.3969/j.issn.0372-2112.2014.10.018

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A Multi-Objective Discrete Particle Swarm Optimization Algorithm for SLA-Aware Service Composition Problem

更新时间：2025-07-16

- A Multi-Objective Discrete Particle Swarm Optimization Algorithm for SLA-Aware Service Composition Problem
- Acta Electronica Sinica Vol. 42, Issue 10, Pages: 1983-1990(2014)
- 作者机构：
  
  东北大学信息科学与工程学院,辽宁,沈阳,110819
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61100090, No.61073062, No.61100027);Fundamental Research Funds for the Central Universities (No.N11024006, No.N110604002, No.N120604003)
- DOI：10.3969/j.issn.0372-2112.2014.10.018
  CLC： TP311
- Published：2014
- 稿件说明：
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YIN Hao, ZHANG Chang-sheng, ZHANG Bin, et al. A Multi-Objective Discrete Particle Swarm Optimization Algorithm for SLA-Aware Service Composition Problem[J]. Acta Electronica Sinica, 2014, 42(10): 1983-1990.
DOI：

YIN Hao, ZHANG Chang-sheng, ZHANG Bin, et al. A Multi-Objective Discrete Particle Swarm Optimization Algorithm for SLA-Aware Service Composition Problem[J]. Acta Electronica Sinica, 2014, 42(10): 1983-1990. DOI： 10.3969/j.issn.0372-2112.2014.10.018.

摘要

针对SLA等级感知服务组合问题

本文提出了一种求解该问题的多目标离散粒子群算法(MDPSO)

建立了多目标粒子群算法优化模型.根据该问题的特征

对粒子更新策略进行重新设计;并且提出粒子变异策略以抑制群体的早熟收敛增强群体的全局搜索能力.另外

提出了一种基于约束支配关系的局部搜索策略并将其结合到MDPSO算法

形成算法MDPSO+.最后对MDPSO算法的参数设值进行了分析

并将算法MDPSO、MDPSO+与最近提出的求解该问题的E

-MOGA算法及NSGA-II算法在不同规模的测试用例上进行了实验对比

结果表明算法MDPSO+能够更加有效的解决该问题.

Abstract

For SLA-aware service composition problem (SSC)

a multi-objective discrete particle swarm optimization algorithm (MDPSO) is proposed in this paper and an optimization model for this algorithm is also built.According to the character of this SSC problem

a particle updating strategy is redesigned by introducing crossover operator.A particle mutation strategy is proposed to increase the swarm diversity and restrain particle swarm's premature convergence.In addition

algorithm MDPSO+ is formed by incorporating a local search strategy based on constraint-domination into the algorithm MDPSO.At last

some parameters in algorithm MDPSO are analyzed and set with relative proper values

and then the algorithm MDPSO and the algorithm MDPSO+ are compared with the recently proposed algorithm E3-MOGA and NSGA-II on different-scale cases;the results show that algorithm MDPSO+ can solve the SSC problem more effectively.

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