Aiming at the problems of low search efficiency and inaccurate optimization of existing service composition optimization methods, a new multiobjective optimization model of cloud manufacturing service composition was constructed, which took service matching degree, composition synergy degree, cloud entropy, execution time, and execution cost as optimization objectives, and an improved particle swarm optimization algorithm (IPSOA) was proposed. In the IPSOA, the integer encoding method was used for particle encoding. The inertia coefficient and two acceleration coefficients were improved by introducing the normal cloud model, sine function, and cosine function. The global search ability of IPSOA in the early stage was improved, and its prematurity was restrained to form a more comprehensive solution space. In the later stage, IPSOA focused on the local fine search and improved the optimization precision. Taking automatic guided forklift manufacturing task as an example, the correctness of the proposed multiobjective optimization model of cloud manufacturing service composition and the effectiveness of its solution algorithm were verified. The performance of IPSOA was analyzed and compared with standard genetic algorithm (SGA) and traditional particle swarm optimization (PSO). Under the same conditions, IPSOA had a faster convergence speed than PSO and SGA and had better performance than PSO.

中文翻译：

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改进粒子群算法的云制造服务组合多目标优化

针对搜索效率低，现有服务组合优化方法优化不准确的问题，构建了一种新的云制造服务组合多目标优化模型，该模型考虑了服务匹配度，组合协同度，云熵，执行时间和执行成本。作为优化目标，提出了一种改进的粒子群优化算法（IPSOA）。在IPSOA中，整数编码方法用于粒子编码。通过引入正常的云模型，正弦函数和余弦函数，改善了惯性系数和两个加速度系数。早期的IPSOA的全局搜索能力得到了提高，并限制了其成熟度，从而形成了更全面的解决方案空间。在后期阶段 IPSOA专注于本地精细搜索并提高了优化精度。以自动制导叉车制造任务为例，验证了所提出的云制造服务组合多目标优化模型的正确性及其求解算法的有效性。分析了IPSOA的性能，并将其与标准遗传算法（SGA）和传统粒子群优化（PSO）进行了比较。在相同条件下，IPSOA的收敛速度比PSO和SGA快，并且性能比PSO更好。验证了所提出的云制造服务组合多目标优化模型的正确性及其求解算法的有效性。分析了IPSOA的性能，并将其与标准遗传算法（SGA）和传统粒子群优化（PSO）进行了比较。在相同条件下，IPSOA的收敛速度比PSO和SGA快，并且性能比PSO更好。验证了所提出的云制造服务组合多目标优化模型的正确性及其求解算法的有效性。分析了IPSOA的性能，并将其与标准遗传算法（SGA）和传统粒子群优化（PSO）进行了比较。在相同条件下，IPSOA的收敛速度比PSO和SGA快，并且性能比PSO更好。