求解高维全局优化问题的改进飞蛾火焰优化算法

Improved Moth-flame Optimization Algorithm for Solving High-dimensional Global Optimization Problems

针对标准飞蛾火焰优化算法在求解高维全局优化问题时存在收敛速度慢、解精度低和易陷入局部最优等缺点,提出一种改进的飞蛾火焰优化算法(简记为IMFO).该算法首先引入动态惯性权重对飞蛾位置更新方程进行修改以平衡算法的勘探和开采能力.受差分进化算法启发,设计出一种新的随机差分变异策略,以帮助种群跳出局部最优,选取18个高维(100、500和1000维)全局优化问题进行数值测试,结果表明,在相同的适应度函数评价次数下,IMFO在收敛速度和求解精度指标上明显优于基本MFO算法和其他对比算法.

The basic moth-flame optimization(MFO)algorithm has a few shortcomings of slow convergence,low solving precision,and high possibility of being trapped in local optimum.To overcome these shortcomings of MFO,an improved version of MFO,named IMFO,is proposed to solve high-dimensional global optimization problems.Firstly,in order to balance the capability of exploration and exploitation,a dynamic inertia weight strategy is introduced for modifying the position-updating equation of moths.Secondly,inspired by differential evolution algorithm,a random differential mutation strategy is designed to help the population jump out of local optimum.Simulation experiments are tested on the 18 high-dimensional(100,500,and 1000 dimensions)global optimization problems.The experimental results demonstrate that IMFO has better performance in convergence speed and solution precision than basic MFO and other comparison algorithms under the same number of fitness function evaluations.