A PSO-Fuzzy group decision-making support system in vehicle performance evaluation

Group decision-making (GD) is a fuzzy problem with high complexity and is difficult for us to handle. Usually the rule-based Group Decision-making Support System (GDSS) is used to solve the GD problem. But the definitions of the fuzzy rules and membership functions in GDSS are generally affected by subjective decision. So the rationality of GDSS is difficult to be judged. In this paper, the Particle Swarm Optimization (PSO) algorithm is introduced to improve the fuzzy rule base through optimizing the position and shape of the fuzzy rule set and weights of rules. A PSO-Fuzzy GDSS is set up and used for a real application of vehicle performance evaluation. From the performance of the three methods, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), non-weighted fuzzy rule base, and PSO-Fuzzy GDSS, it can be seen that the weighted fuzzy rule base after PSO optimized is better than the non-weighted fuzzy rule base, and the evaluation values of PSO-Fuzzy GDSS are very close to the TOPSIS. Therefore, the PSO-Fuzzy GDSS is an efficient method for vehicle performance evaluation and can be applied to more domains.