The major objective of the present study is to demonstrate the performance and efficiency of the Repulsive Particle Swarm Optimization (RPSO) method as an inverse analysis solver by extending its application to the inverse heat conduction problem. As the first outcome, an estimation of unknown parameters of a time-varying plane heat source in a one-dimensional inverse heat conduction problem was considered. The overall performance of the RPSO method was examined based on the effects of the forms of unknown heat source, the number of parameters, the measurement errors and the population sizes on the estimation accuracy. In addition, the final results were compared with those of the Levenberg-Marquardt Method (LMM), which is widely used for the inverse heat conduction problem, to investigate the advantages and disadvantages of the RPSO method. The present results prove that the RPSO method has more robust characteristics and yields rather confidential inverse estimations than the LMM for the inverse heat conduction problem although it requires more computational cost. (C) 2019 Elsevier Ltd. All rights reserved.