Predicting the temperature- and time-dependent nonlinear material behavior of thermoplastics and fiber-reinforced thermoplastics requires the selection and parameter identification of appropriate constitutive laws. However, many material parameters cannot be predicted in advance, and their actual values can span over ten orders of magnitude. In such cases, it is difficult to find the optimal design parameters within a wide search range using multi-point search methods employing ordinary meta-heuristic algorithms. In this study, we propose a new optimization method that applies the adaptive range search algorithm to ordinary meta-heuristic algorithms, such as PSO and DE and the search area is adjusted based on the optimization results in each trial. The elastic-creep-damage combined model is employed as a material constitutive law for thermoplastic resins, and the effectiveness of the proposed method is demonstrated through the parameter identification of a typical thermoplastic resin. The proposed method exhibited excellent search capability for finding the global optimum over a wide range (10-9–109) of design parameters.
