Differential evolution is a global heuristic algorithm

which is simple

easy-to-use and robust in practice.Combining with the constraint-handling techniques

it can solve constrained optimization problems.Machine learning often guides population to evolve in the evolution computation

and is widely applied to unconstrained differential evolution algorithm.However

machine learning is rarely applied to constrained differential evolution algorithm

so this paper proposed a constrained differential evolution algorithm framework using opposition-based learning.The algorithm can improve the diversity and convergence of differential evolution.At last

the proposed algor

ithm framework is applied to two popular constrained differential evolution variants

that is (

+

) -CDE and ECHT-DE.And 18 benchmark functions presented in CEC 2010 are chosen as the test suite

experimental results show that comparing with (

+

) -CDE and ECHT-DE

our algorithms are able to improve global search ability

convergence speed and accuracy in the majority of test cases.