CAJ | 학술논문

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MDP-SO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E3-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。
침대SLA등급감지복무조합문제，본문제출료일충구해해문제적다목표리산입자군산법（MDPSO ），건립료다목표입자군산법우화모형。근거해문제적특정，대입자경신책략진행중신설계；병차제출입자변이책략이억제군체적조숙수렴증강군체적전국수색능력。령외，제출료일충기우약속지배관계적국부수색책략병장기결합도MDP-SO산법，형성산법MDPSO＋。최후대MDPSO산법적삼수설치진행료분석，병장산법MDPSO、MDPSO＋여최근제출적구해해문제적E3-MOGA산법급NSGA-II산법재불동규모적측시용례상진행료실험대비，결과표명산법MDPSO＋능구경가유효적해결해문제。
For SLA-aware service composition problem (SSC) ,a multi-objective discrete particle swarm optimization algo-rithm (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 pro-posed 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 .