摘要
为了提高电力工程企业的经济效益,在综合考虑成本、质量和进度的基础上,提出了工期-收益-质量多目标优化模型。粒子群优化算法是基于群体智能理论的算法。该算法利用生物群体内个体的合作与竞争等复杂性行为产生群体智能,并为工程优化问题提供高效的解决方法。但是粒子群优化算法同样存在一些问题,针对这些问题提出了一种新算法,即基于速度松弛策略的模拟退火粒子群算法(RSAPSO)。运用RSAPSO算法对多目标优化模型进行求解,最后通过工程实例验证模型和算法的有效性。
Based on the synthetic study of costing, quality and construction period, the multi-objective optimal model considering the maximal pure value and quality of electric power construction project is introduced in order to improve economic benefit of electric power construction enterprise. Particle swarm optimization （PSO） algorithm is based on swarm intelligence theory. The algorithm can provide efficient solutions for optimization problems through intelligence generated from complex activities such as cooperation and competition among individuals in the biologic colony. But, the PSO algorithm also had some problems. In dealing with these problems, a new method called RSAPSO （particle swarm optimization and simulated annealing based on the strategy of relaxation-velocity-update） is introduced into the model of multi-objective optimization. The model and validity of this algorithm are tested through project case.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第12期3237-3239,共3页
Computer Engineering and Design
基金
华北电力大学青年教师科研基金项目(200611041)
关键词
网络计划
多目标优化
粒子群算法
速度松弛策略
模拟退火
network planning
multi-objective optimization
particle swarm optimization
relaxation-velocity-update strategy
simulated annealing
