Integrating fuzzy case-based reasoning and particle swarm optimization to support decision making

Case-based reasoning (CBR) is a useful technique to support decision making (DM) by learning from past experiences. It solves a new problem by retrieving, reusing, and adapting past solutions to old problems that are closely similar to the current problem. In this paper, we combine fuzzy logic with case-based reasoning to identify useful cases that can support the DM. At the beginning, a fuzzy CBR based on both problems and actors’ similarities is advanced to measure usefulness of past cases. For efficiency, we need an optimal design of membership functions of fuzzy sets. Then, we rely on a meta-heuristic optimization technique i.e. Particle Swarm Optimization to adjust the parameters of the inputs and outputs fuzzy membership functions.