Multi-objective particle swarm optimization algorithm and its application to the fuzzy rule based classifier design problem with the order based semantics of linguistic terms

A method of designing fuzzy rule based classification systems (FRBCSs) using multi-objective optimization evolutionary algorithms (MOEAs) clearly depends on evolutionary quality. There are two types of such algorithms: Genetic Algorithms (GAs) and Swarm Intelligence (SI). Naturally arises a question how strongly utilized evolutionary algorithms influence on the efficiency of a method of designing FRBCS making this better than another. Particle swarm optimization (PSO) algorithm [13, 14] is among SI series. This paper represents an application of the multi-objective PSO algorithm with fitness sharing (MO-PSO) proposed in [8] to optimize the semantic parameters of linguistic variables and fuzzy rule selection in designing FRBCSs based on hedge algebras proposed as in [7] (using GSA-genetic simulated annealing algorithm). By simulation, MO-PSO is shown to be more efficient and produces better results than GSA-algorithm. That is to show a method of the FRBCS design is better than another one using MOEA, the same MOEA must be used.