摘要
布局优化是NP难问题,也是复杂的非线性约束优化问题.针对这个问题.将新的基于粒子群优化的方法应用于布局参数的优化,提出了适合粒子群优化的约束处理,并通过与直接搜索算法的混合,加强了算法在局部区域的搜索能力.通过实例将该算法与乘子法以及基于遗传算法的布局优化方法进行了比较.仿真结果表明,该算法可以提高布局优化问题解的质量,同时降低计算费用.
Layout optimization is an NP-hard problem. It also belongs to complex nonlinear constrained optimization problem. In view of this problem, a new methodology based on particle swarm optimization (PSO) is developed to optimize layout parameters. A constraint handling strategy suit for PSO is proposed. Furthermore, improvement is made by using direct search to intensify local search ability of PSO algorithm. Simulation results show that the proposed algorithm improves the quality of the solution while lowering the computational cost.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第1期36-40,共5页
Control and Decision
基金
国家自然科学基金项目(50305008).
关键词
粒子群优化
布局优化
约束处理
局部搜索
Constraint theory
Convergence of numerical methods
Genetic algorithms
Nonlinear systems
