一种基于人工蜂群的高维非线性优化算法

针对传统优化算法在求解高维非线性优化问题时,存在收敛速率慢和求解精度不高等问题.提出一种改进的人工蜂群优化算法.正交试验设计算法被用于初始化蜂群和侦察蜂探索新蜜源.采蜜蜂利用高斯分布估计优化算法在蜜源附近搜索,跟随蜂采用自适应差分算法进行搜索.最后,通过4个标准的高维Benchmark函数测试表明,本文算法在收敛速度、求解精度和稳定性方面有一定优势.

A New High-Dimensional Nonlinear Optimization Algorithm Based on Artificial Bee Colony

About convergence rate and solution precision are not high in high-dimensional Nonlinear Optimization Problem(NOP),an improved Artificial Bee Colony(ABC) optimization algorithm is proposed in this paper.Firstly,the orthogonal experimental design algorithm was used to generate initial population and discover a new food source for the scout;Secondly,employed bees uses Gaussian Distribution Estimate Algorithm(GDEA) to search,according to fitness value,onlooker bees select one employed bees and search new nectar source in an self-adaptive differential search algorithm.At last this algorithm is tested on 4 standard benchmark functions,and the experimental results show this algorithm has some advantages in convergence velocity,solution precision,and stabilization.