In wireless sensor network (WSN) there are many sensors and tiny devices which are used to sense the real-time environmental circumstances. The sensed data will be meaningless if each node in WSN doesn't know its location in the real world. There are many cost-effective techniques for localization used to locate the sensor node. Among those techniques, the range-based localization techniques are known for their accuracy in predicting sensor node location. Differential Evolution Algorithm (DEA) is popular optimization technique as it has good convergence properties but it has few control parameters, which are fixed throughout the entire iteration process and it is not an easy task to tune that control parameters. So, in this paper, we propose Adaptive Differential Evolution Algorithm (ADEA) for obtaining adaptive control over the parameters. In DEA we consider appropriate solutions to have more probability of reproduction compared to inappropriate ones, but there is always a possibility that population elements that look inappropriate in each stage may contain more useful information than appropriate ones. So, we propose Invasive Weed Optimization Algorithm (IWO) which reaches an optimal solution more easily by giving a chance for inappropriate one's to survive and reproduce similar to the mechanism that happens in nature. © 2018 IEEE.