A novel algorithm based on hybridization of artificial immune system and simulated annealing for clustering problem

Abstract

A hybrid clustering method is proposed in this paper based on artificial immune system and simulated annealing. An integration of simulated annealing and immunity-based algorithm, combining the merits of both these approaches, is used for developing an efficient clustering method. Tuning the parameters of method is investigated using Taguchi method in order to select the optimum levels of parameters. Proposed method is implemented and tested on three real datasets. In addition, its performance is compared with other well-known meta-heuristics methods, such as ant colony optimization, genetic algorithm, simulated annealing, Tabu search, honey-bee mating optimization, and artificial immune system. Computational simulations show very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required, comparing with mentioned methods.