CAJ | 학술논문

针对非线性双层规划难以获得全局最优的问题, 汲取粒子群算法的快速搜索能力及变邻域搜索算法的全局搜索优势, 提出了求解非线性双层规划问题的混合变邻域粒子群算法. 首先利用Kuhn-Tucker条件, 将非线性双层规划转化为一个单层规划问题, 然后由粒子群算法得到一个较优的群体, 通过审敛因子判断陷入局部最优的粒子, 并进一步利用变邻域搜索算法的全局搜索能力对陷入局部最优的粒子进行优化, 从而得到全局最优. 测试函数的仿真实验对比分析证明了该算法的有效性.
침대비선성쌍층규화난이획득전국최우적문제, 급취입자군산법적쾌속수색능력급변린역수색산법적전국수색우세, 제출료구해비선성쌍층규화문제적혼합변린역입자군산법. 수선이용Kuhn-Tucker조건, 장비선성쌍층규화전화위일개단층규화문제, 연후유입자군산법득도일개교우적군체, 통과심렴인자판단함입국부최우적입자, 병진일보이용변린역수색산법적전국수색능력대함입국부최우적입자진행우화, 종이득도전국최우. 측시함수적방진실험대비분석증명료해산법적유효성.
In this paper, a hybrid intelligent algorithm by combining the particle swarm optimization （PSO） with variable neighborhood search （VNS） is presented on the basis of analyzing the problem of nonlinear bilevel programming. This method integrates the fast search capability of PSO with the global search ability of VNS. Firstly, the bilevel programming is transformed into a single level programming problem by use of the Kuhn-Tucker conditions. Then, the preferable swarm is obtained by PSO algorithm. Furthermore, the swarm get into local optima, which is estimated by convergence criterions, is optimized by VNS algorithm. Finally, the result of benchmark problems demonstrates the proposed algorithm is effective than the compared algorithms.