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 metaheuristics 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.

Integrating the sequence dependent setup time open shop problem and preventive maintenance policies

“…Many AIS algorithms have been proposed to find solutions to a widespread class of complex problems. AIS has been applied to the areas such as clustering (Karaboga & Ozturk, 2011;Abdi et al, 2012;Duma et al, 2013), anomaly detection (Gonzalez & Dasgupta, 2003), network security (Forrest & Hoffmeyr, 2000), optimization (Gao & Fang, 2009), scheduling (Engin & Döyen, 2004;Anandaraman et al, 2012), etc. The immune system is a dramatic and complex set of cells, molecules and organs with the primary role of defending of the host organism by pathogens (called antigens, Ag), which brings out an immune response.…”

A novel multi-population coevolution immune optimization algorithm

“…All these applausive integrated algorithms have been applied to solve the practical optimization problem successfully; Pan et al (2014) constructed a hybrid immune algorithm (HIA), which employs GA to initialize and utilizes DO to delete routes of crossover. With dynamic mutation operator (DMO) adopted to improve searching precision, this proposed algorithm can increase the likelihood of global optimum after the hybridization; hybridization of artificial immune system and simulated annealing (Abdi et al 2012) or K-means (Kuo et al 2014) could also be found.…”

A mathematical model and simulated annealing algorithm for solving the cyclic scheduling problem of a flexible robotic cell

“…SAA is prominence from high solution performance, fine results in short times among meta-heuristics approach. In the literature, the SAA has been used for solving the traveling salesman problem (TSP), 16 the location-routing problem, 17 the emergency logistics problem, 18 the assembly line balancing problem, 19,20 disassembly scheduling problem, 21 the production and preventive maintenance problem, 22 the flow shop scheduling problem, 23 the clustering problem, 24 the facility layout problem, 25 the cell formation problem, 26 for distributed job shop problem, 27 and so on.…”