区间长度可变的反向混沌优化算法

Opposite Based Chaos Optimization Algorithm with Variable Interval Length

针对现有算法在大空间和高维度寻优存在效率较低的问题,提出一种区间长度可变的反向混沌优化算法,并证明了该算法以概率1收敛于全局最优解.算法采用区间长度可变的反向优化策略,利用反向优化方法增大算法进化过程的多样性,使优化的变量区间不断减小.同时,提出基于Fuch混沌映射的反向混沌优化策略增大算法逃逸局部极值的能力,以及两级优化策略提高算法执行后期的寻优精度.通过22个基准函数测试结果表明,本文提出的算法与改进的混沌优化算法以及其他智能优化算法相比,其搜索的综合性能要优于其他算法.

To improve search efficiency in large space and high dimension for the algorithm,an opposite based chaos optimization algorithm (VILOC) with variable interval length is proposed,which is verified to converge global optimum solution with probability one.Meanwhile,an opposite optimization approach to increase the diversity of the algorithm is also introduced,which gives rise to decrease of the optimized variable interval.In the implementation procedure of VILOC,an anti-chaotic optimization strategy based on Fuch chaotic map is accommodated to escape the local extremum,and the two-stage optimization strategy to increase the convergence precision.The comparisons are carried out through experiments and the numerical results demonstrate that the proposed algorithm is superior to other improved chaos optimization algorithms and intelligent optimization algorithms.