基于混沌和高斯局部优化的混合差分进化算法

Hybrid differential evolution combined with chaos and Gaussian local optimization

针对标准差分进化(DE)算法在高维复杂函数优化中易早熟收敛,进而导致搜索精度降低甚至优化失败的问题,提出一种基于混沌和高斯局部优化的混合差分进化算法(CGHDE).该算法在进化初期利用混沌的遍历性,可有效地避免算法陷入局部最优;而在进化后期,采用高斯搜索又可有效地提高收敛精度.实验表明,CGHDE算法对函数维度的敏感性大大低于标准DE算法,并且寻优能力强、稳定性好、搜索精度高,特别适合于工程中高维复杂函数的优化问题.

Chaos and Gaussian local optimization based hybrid differential evolution（CGHDE） is proposed to solve the premature convergence and low precision of standard differnential evolution（DE） when applied to high-dimensional complex engineering problems.By means of the randomicity of chaotic local search,the CGHDE algorithm tends to explore in a wide search space to overcome the premature in the earlier evolution phase,and then performs exploitation to refine the optimum by using Gaussian optimization to improve the output in the later run phase.Simulations show that,CGHDE algorithm is not as sensitive to function dimensions as standard DE and has the advantages of powerful optimizing ability,more stability,higher optimizing precision and suitable for high-dimensional complex functions optimization.