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,a hybrid algorithm combined CMAES with Orthogonal Design with Quantization(OD/Q) was proposed.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 the local optimum.The new approach was tested on six high-dimensional multimodal benchmark functions.Compared with other algorithms,the new algorithm has better searching precision,convergence speed and capacity of global search.
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