© 2021 World Scientific Publishing Company.In continuous multiple criteria problems, finding a distinct preferred solution for a decision maker (DM) is not straightforward. There are few recent studies proposed for this task, and the algorithms developed are cognitively difficult and complex for the DM in general. We propose a novel interactive algorithm to guide the DM in converging highly-preferred solutions in continuous multiple criteria problems. We test our algorithm on portfolio optimization problems formed with the stocks included in the S&P 100 index using expected return, liquidity, conditional value at risk, and mean absolute deviation as criteria. We simulate DM responses with linear and nonlinear preference functions and use various weights for the criteria. The experiments show that our algorithm is able to find highly-preferred solutions in considerably low number of iterations. We also test our algorithm against benchmark algorithms and demonstrate that our algorithm produces superior or comparable results.