求解约束优化问题的改进灰狼优化算法

Improved grey wolf optimization algorithm for constrained optimization problem

针对基本灰狼优化(GWO)算法存在求解精度低、收敛速度慢、局部搜索能力差的问题,提出一种改进灰狼优化(IGWO)算法用于求解约束优化问题。该算法采用非固定多段映射罚函数法处理约束条件,将原约束优化问题转化为无约束优化问题,然后利用IGWO算法对转换后的无约束优化问题进行求解。在IGWO算法中,引入佳点集理论生成初始种群,为算法全局搜索奠定基础;为了提高局部搜索能力和加快收敛,对当前最优灰狼个体执行Powell局部搜索。采用几个标准约束优化测试问题进行仿真实验,结果表明该算法不仅克服了基本GWO的缺点,而且性能优于差分进化和粒子群优化算法。

The standard Grey Wolf Optimization （GWO） algorithm has a few disadvantages of low solving precision, slow convergence, and bad local searching ability. In order to overcome these disadvantages of GWO, an Improved GWO （IGWO） algorithm was proposed to solve constrained optimization problems. Using non-stationary multi-stage assignment penalty function method to deal with the constrained conditions, the original constrained optimization problem was converted into an unconstrained optimization problem. The proposed IGWO algorithm was applied to solve the converted problem. In proposed IGWO algorithm, good point set theory was used to initiate population, which strengthened the diversity of global searching. PoweU search method was applied to the current optimal individual to improve local search ability and accelerate convergence. Simulation experiments were conducted on the well-known benchmark constrained optimization problems. The simulation results show that the proposed algorithm not only overcomes shortcomings of the original GWO algorithm, but also outperforms differential evolution and particle swarm optimization algorithms.