改进微粒群算法在“工期固定—资源均衡”优化中的研究
n Improved Version of PSO for fixed time limit-resource leveling Optimization
微粒群算法通过群体中微粒间的合作与竞争而产生的群体只能优化搜索,算法具有较强的通用性,具有全局寻优的特点。本文主要是把微粒群算法和工期固定、资源均衡问题相结合,对微粒群的惯性权重与速度更新公式进行了改进,在约束处理上采用了适应性罚函数法,使微粒群算法更好的应用于工程资源优化问题中。
Particle Swarm Optimization particles through inter-group cooperation and competition can only be created by groups to optimize the search; the algorithm has strong versatility, with the characteristics of global optimization. In this paper, particle swarm optimization (PSO) is combined with the \\\\\\\\\\\\\\\"fixed time limit - resource leveling\\\\\\\\\\\\\\\" optimization problem. The PSO inertia weight and the velocity update formula are improved and adaptive penalty function method is used to deal with the constraints, so particle swarm optimization makes use better in the problem of optimizing engineering resources.
陈欢、陈娜
工程基础科学
微粒群算法网络优化工期固定—资源均衡
Particle Swarm Optimizationnetwork optimization
陈欢,陈娜.改进微粒群算法在“工期固定—资源均衡”优化中的研究[EB/OL].(2010-03-01)[2025-08-02].http://www.paper.edu.cn/releasepaper/content/201003-15.点此复制
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