The spider wasp optimizer has problems such as irrational initial population distribution,unbalanced transition between search and exploitation,and a tendency to fall into local optimization.Therefore,a dynamic spider wasp optimizer combined with duality learning(CLDSWO)is proposed to solve these problems.Firstly,the Tent-Sinusoidal (TS)mapping which combines the Tent and Sinusoidal mapping is designed to generate the initial spider-wasp population with a wider and uniform distribution.Secondly,a dynamic tradeoff factor is developed to adaptively adjust the tradeoff between hunting and mating behaviors to achieve a balance between global search and local optimization.Finally,a mutation mechanism based on duality learning is introduced to accelerate the convergence and enhance the ability to escape from the local optimum.To verify the effectiveness of CLDSWO,10 benchmark functions,CEC2017 functions, and Wilcoxon tests are carried out.The results show that CLDSWO is more competitive in balancing convergence accuracy and speed.The CLDSWO algorithm is applied to the pressure vessel design problem and the time difference of arrival localization problem.The results show that the accuracy of CLDSWO was improved by 1.28%and 36.67%, respectively,validating the effectiveness of CLDSWO in solving practical engineering applications