Towards a Canonical Particle Swarm Optimized Direct Least Squares Prioritization Method for Ratio Pairwise Comparison: An Application of Mutual Fund Selection

Pairwise comparison is a popular method for decision making. Analytic Hierarchy Process (AHP) is based on pairwise comparison (PC), which the rating results of PC forms pairwise reciprocal matrices (PRM) further to be derived as the priority values by the Eigen method. Prioritization method using Eigen method is still open to discuss, although many applications using this method in AHP have been made. This study proposes the Canonical Particle Swarm Optimized Direct Least Squares Prioritization (CPSO-DLSP) method for deriving the priority values from a PRM. The direct least squares function leads to the least total square variance between ground PRM and the derived priority values. The Canonical Particle Swarm Optimization (CPSO) algorithm is one of the promising methods to effectively solve the direct least squares optimization problem. An application of mutual fund selection using the conventional AHP method is revised and demonstrated by the proposed method.