For the whole evaluation strategy in cuckoo search algorithm in the face of multi-dimension function optimization problems

the coupling phenomena among dimensions will deteriorate the search speed and convergence accuracy.Therefore

a new cuckoo search algorithm based on the equilibrium single evolution mechanism is proposed.Then

a new equilibrium single evolution evaluation strategy is also used to update randomly the single dimension of the objective function on each iteration.Note that the randomly updated dimensions obey the uniform distribution to avoid mutual interference between dimensions.Furthermore

two new random walking update laws are proposed to improve the global search speed and convergence accuracy.The results of the 10 benchmark functions and statistical significance demonstrate that ESCES algorithm has a great improvement in global optimization performance

search speed and convergence accuracy compared with the five modified CS algorithms and seven other state-of-the art algorithms.