The estimation of gear selectivity is a critical issue in fishery stock assessment and management.Several methods have been developed for estimating gillnet selectivity,but they all have their limitations,such as inappropriate objective function in data fitting,lack of unique estimates due to the difficulty in finding global minima in minimization,biased estimates due to outliers,and estimations of selectivity being influenced by the predetermined selectivity functions.In this study,we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet selectivity.The proposed algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers.According to the estimated gillnet selectivity curve,the selectivity function can also be determined.This study suggests that the proposed algorithm is not sensitive to outliers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool.We suggest the proposed approach be used in estimating gillnet selectivity. The estimation of gear selectivity is a critical issue in fishery stock assessment and management. All of these have developed for estimating gillnet selectivity, but they all have their limitations, such as suitable objective function in data fitting, lack of unique estimates due to the difficulty in finding global minima in minimization, biased estimates due to outliers, and estimations of selectivity being influenced by the predetermined selectivity functions. In this study, we develop a new algorithm that can overcome the above-mentioned problems in estimating the gillnet. algorithms include minimizing the sum of squared vertical distances between two adjacent points and minimizing the weighted sum of squared vertical distances between two adjacent points in the presence of outliers. According to the estimated gillnet selectivity curve, the selectivity function can also be determined. This study suggests that the proposed algorithm is not sensitive to outl iers in selectivity data and improves on the previous methods in estimating gillnet selectivity and relative population density of fish when a gillnet is used as a sampling tool. We suggest the proposed approach be used in estimating gillnet selectivity.