A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights. (November 2016)

• Record Type: Journal Article
• Authors: Ebrahimnejad, Ali; Tavana, Madjid; Alrezaamiri, Hamidreza
• Is Part Of: Measurement. Volume 93(2016)
• Page Start: 48
• Page End: 56
• Publication Date: 2016-11
• Subjects: Shortest path problem -- Fuzzy numbers -- Genetic algorithm -- Particle swarm optimization -- Artificial bee colony; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2016.06.050
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: We formulate a shortest path (SP) problem with fuzzy arc weights. An artificial bee colony (ABC) algorithm is designed to solve this fuzzy SP problem. Convergence time of the proposed ABC algorithm is compared with the existing algorithms. An application of the proposed algorithm in wireless sensor networks is presented. Abstract: The shortest path (SP) problem is a network optimization problem with a wide range of applications in business and engineering. Conventional network problems assume precise values for the weights of the edges. However, these weights are often vague and ambiguous in practical applications. Several heuristics have been proposed to find the shortest path (SP) weight and the corresponding SP on a network with fuzzy arc weights. These heuristics largely use α -cuts and the least squares method. We propose an artificial bee colony (ABC) algorithm to solve the fuzzy SP (FSP) problems with fuzzy arc weights. The performance of the proposed ABC algorithm is compared with the performance of other competing algorithms with two SP problems taken from the literature. We present a wireless sensor network (WSN) problem and demonstrate the applicability of the proposed method and exhibit the efficiency of the procedures and algorithms.