摘要
提出了一种非线性约束优化问题改进的自适应差分进化算法。该算法对差分进化算法中固定的加权因子和交叉概率因子进行改进;定义了约束违反度函数,将约束优化问题转化为无约束双目标优化问题,在每次迭代中按照约束违反度的大小保留一部分性能较优不可行粒子,有效地维持了种群的多样性;为了扩大粒子的搜索范围引入变异算子。数值实验表明,新算法具有较快的收敛速度和较好的全局寻优能力。
This paper presents an improved adaptive differential evolution algorithm for the nonlinear constrained optimization problems.In this algorithm,the fixed weighting factor and crossover probability factor of the differential evolution are improved.The constrained optimization problems are converted into unconstrained bi-objective optimization problem by the definition of the constraint violation function.In each iteration,keeping a part of the performance of better infeasible particles is to maintain the diversity of the swarm.Mutation operator is introduced to expand the search range of the particle.Numerical experiments show that the proposed algorithm has faster convergence speed and better ability of global optimization.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第25期44-48,共5页
Computer Engineering and Applications
基金
国家自然科学基金(No.60962006)
商洛学院科研基金项目~~
关键词
全局最优
非线性约束优化
差分进化
混沌
global optimal
nonlinear constrained optimization
differential evolution
chaotic
