![](https://csdnimg.cn/release/download_crawler_static/86980336/bg3.jpg)
abstract:
in order to overcome the shortcomings of covariance matrix
adaptation evolution strategy(cmaes), such as premature
convergence and low precision, when it is used in
high-dimensional multimodal optimization, an hybrid
algorithm combined cmaes with orthogonal design with
quantization(od/q) was proposed in this study. firstly, the
small population cmaes was used to realize a fast searching.
when orthogonal cmaes algorithm trapped in local extremum,
base vectors for od/q were selected dynamically based on the
position of current best solution. then the entire solution
space, including the field around extreme value, was explored
by trial vectors generated by od/q. the proposed algorithm
was guided by this process jumping out of thelocal optimum.
the new approach is tested on six high-dimensional multimodal
benchmark functions. compared with other algorithms, the new
algorithm has better search precision, convergent speed and
capacity of global search. in order to overcome the
shortcomings of covariance matrix adaptation evolution
strategy (cmaes), such as premature convergence and low
precision, when it is used in high.dimensional multimodal
optimization, a hybrid algorithm combined cmaes with