Modification of Permutation Method in Multi-Criteria Decision Making Using PSO-SA

Permutation is one of the widely used methods in Multi-Criteria Decision Making (MCDM) for prioritizing alternatives. Making the best decision will become more challenging by growing the number of criteria. High computational cost is the main disadvantage of this method. In this paper, an innovative algorithm is applied for obtaining an optimized solution for the permutation method. In this research, a combined algorithm of particle swarm optimization (PSO) and simulated annealing (SA) is proposed and implemented for reducing the high calculation costs. This combined algorithm possesses the higher convergence properties of PSO in finding the best answer as well as holds the SA feature of avoiding from facing local minimum. This algorithm investigated two sets of numeric data. The results verify its highest efficiency in detecting an optimized sequence of prior alternatives choices at a shorter time. This work proved that the suggested methodology could be employed with high confidence for common permutation problems in case of the presence of many alternatives.