Estimating software cost and time is one of the important aspects of software project management and a serious concern for software project managers. So far, various methods have been proposed for accurately estimating software cost, each of which has been developed based on a specific perspective. One of the most important methods is analogy-based estimation, which is commonly used today based on different algorithms. The main objective of the current study is to compare various nature-inspired algorithms for improving the accuracy of feature weighting as a step in the analogy-based method. The results indicate that these algorithms can increase the accuracy of software cost estimation from this perspective. Moreover, among the different available algorithms, the learnable evolution model and the artificial bee colony algorithm were able to outperform other algorithms due to their special internal structure.