As a popular population based evolutionary computation algorithm, artificial bee colony (ABC) algorithms have attracted many researchers to do further works. In this paper, by utilizing a linkage detection strategy to distinguish nonseparable and separable functions, we present new balance strategies for the updating equations of ABC on the two types of functions respectively. First, for accelerating convergence rate, we propose a historic array to preserve the best individual which the population ever achieved. Second, for the different role of employed and onlooker bees during iterations, compared with the traditional algorithm, we present two updating equations for the two types of bees on the nonseparable and separable functions respectively. Third, a new multi-dimensional updating mechanism for the worst individual and a new updating strategy for scout bees are presented respectively. At last, for verifying its effectiveness, we apply the improved algorithm to optimize the power consumption of sensor nodes in a wireless sensor network. In this paper, verified by the test on the network, the basic benchmark functions and CEC2014, our algorithm shows superior performance with the compared modern algorithms.