In order to characterize thermal dependent physical properties of materials, potentially to be used in technological applications,an accurate interatomic-potential parameter set is a must. In general, conjugate-gradient methods and more recently,metaheuristics such as genetic algorithms are employed in determining these interatomic potentials, however, especially theuse of metaheuristics specifically designed for optimization of real valued problems such as particle swarm and evaluationstrategies are limited in the mentioned problem. In addition, some of these parameters are conflicting in nature, for which multiobjective optimization procedures have a great potential for better understanding of these conflicts. In this respect, we aim topresent a widely used interatomic potential parameter set, the Stillinger–Weber potential, obtained through three differentoptimization methods (particle swarm optimization, PSO, covariance matrix adaptation evolution strategies, CMA-ES, andnon-dominated sorting genetic algorithm, NSGA-III) for two-dimensional materials $MoS_2,;WS_2,;WSe_2,;and;MoSe_2.$. These twodimensional transition metal dichalcogenides are considered as a case mainly due to their potential in a variety of promisingtechnologies for next generation flexible and low-power nanoelectronics, (such as photonics, valleytronics, sensing, energy storage,and optoelectronic devices) as well as their excellent physical properties (such as electrical, mechanical, thermal, and opticalproperties) different from those of their bulk counterparts. The results show that the outputs of all optimization methods convergeto ideal values with sufficiently long iterations and at different trials. However, when we consider the results of the statisticalanalyses of different trials under similar conditions, we observe that the method with the lowest error rate is the CMA-ES.