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在分析基本粒子群优化算法和建立虚拟企业伙伴选择多目标决策模型的基础上,提出了一种求解供应链联盟伙伴选择的优化问题的改进粒子群算法。在优化过程中,该算法以优良适应值粒子取代部分不良适应值粒子,使算法具有过滤能力,加快了搜索速度,并保证了收敛于全局最优解。实验结果用基本粒子群算法进行了验证和比较,表明该改进粒子群算法具有较好的性能和简单快速准确等特点。
Based on the analysis of basic particle swarm optimization algorithm and the establishment of multi-objective decision model of partner selection in virtual enterprise, an improved particle swarm optimization algorithm is proposed to solve the optimization problem of supply chain alliance partner selection. In the process of optimization, the algorithm replaces some bad fitness particles with good fitness particles, which makes the algorithm have the ability to filter, speed up the search and ensure convergence to the global optimal solution. The experimental results are verified and compared with the basic particle swarm optimization algorithm, which shows that the improved particle swarm optimization has the characteristics of good performance, simple, fast and accurate.