An attraction based particle swarm optimization for solving multi-objective availability allocation problem under uncertain environment

Article type: Research Article

Authors: Samanta, Aniruddha^{; *} | Basu, Kajla

Affiliations: Department of Mathematics, NIT Durgapur, India

Correspondence: [*] Corresponding author. Aniruddha Samanta, Research Scholar, Department of Mathematics, NIT Durgapur-713209, India. E-mail: [email protected].

Abstract: Reliability optimization and availability optimization are two classes of optimization problems in redundancy allocation problem (RAP). Contrary to reliability optimization, very few researchers have focused on availability optimization to find out the optimal redundancy. This paper proposes a multi-objective optimization problem of availability allocation in a series-parallel system with repairable components. The two objectives are maximizing the system availability and minimizing the total cost of the system. In real life situation, due to complexity of the systems and non-linearity of their behaviour, most of the data are usually uncertain and imprecise. Hence in order to make the model more reliable, fuzzy theory has been introduced in terms of triangular data for handling the uncertainties. Thus in fuzzy environment a fuzzy multi-objective availability allocation problem is formulated. In order to solve the problem a crisp optimization problem has been reformulated using fuzzy programming technique and finally an attraction based particle swarm optimization (APSO) has been proposed to solve this crisp optimization problem. The proposed APSO is compared with the traditional Particle swarm optimization(PSO) to show the efficiency and consistency of the proposed approach. Based on a numerical example,the statistical analysis of the experimental results establish that the proposed APSO has a better and consistent performance compared to traditional PSO.

Keywords: Series-Parallel System, availability allocation problem, fuzzy programming technique, Attraction based particle swarm optimization (APSO)

DOI: 10.3233/JIFS-18029

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 1, pp. 1169-1178, 2018