The emergence of sophisticated technologies and economic competition in recent decades has led to the increasing importance of decision-making about the future of organizations. Deciding in the future based on past knowledge may not guarantee the best choice, but it can guide decision-makers in the right direction. In this study, we present an innovative technique for ranking alternatives and weighting criteria simultaneously based on past data. In this method, it is possible for the reference weight to be affected by other weighting methods, and more accurate weights are assigned to the criteria. All records are considered to evaluate alternatives regarding criteria. Subsequently, the scatter and the starting point of changes are characterized. Finally, the nonlinear mathematical model determines the reference weight coefficients (i.e. the weight with the least difference from the coefficient values) and the final score of the alternatives. Finally, the efficiency of the PSWCA method is obtained on four real-world samples, and the results are compared with other methods.