A Hybrid Multi-objective PSO-SA Algorithm for the Fuzzy Rule Based Classifier Design Problem with the Order Based Semantics of Linguistic Terms *
Phong Pham Dinh, Thuy Nguyen Thanh, Thanh Tran Xuan · 2014
A number of studies [26, 28, 33] have shown that the method of designing fuzzy rule based classifiers (FRBCs) using multi-objective optimization evolutionary algorithms (MOEAs) clearly depends on the evolutionary quality. Each evolutionary algorithm has the advantages and the disadvantages. There are some hybrid mechanisms proposed to tackle the disadvantages of a specific algorithm by making use of the advantages of the others. To improve the application of the multi-objective particle swarm optimization with fitness sharing (MO-PSO) for the FRBC design method proposed in [33], this paper represents an application of a hybrid multi-objective particle swarm optimization algorithm with simulated annealing behavior (MOPSO-SA) to optimize the semantic parameters of the linguistic variables and fuzzy rule selection in designing FRBCs based on hedge algebras proposed in [7] which uses the genetic simulated annealing algorithm (GSA). By simulation, the MOPSO-SA has shown to be more efficient and produced better results than both the GSA algorithm in [7] and the MO-PSO algorithm in [33]. That is, to show a method of the FRBC design is better than another one using MOEA, the same MOEA must be used.