Title: An adaptive firefly algorithm for blocking flow shop scheduling problem

Authors: Wenjun Wang

Addresses: School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China

Abstract: In this paper, we present an Adaptive Firefly Algorithm (AFA) for solving the Blocking Flow Shop Scheduling Problem (BFSSP). It is known that the basic firefly algorithm (FA) works on continuous search space, while the BFSSP is a discrete problem. To handle discrete variables, the Smallest Position Value (SPV) rule is employed. An adaptive parameter strategy is utilised to reduce the dependence on parameters. Furthermore, two local search operators are used to improve the quality of solutions. To save computational time, a random attraction model is used to decrease the number of attractions among fireflies. Experiments are conducted on a set of Taillard's benchmark instances. Simulation results show that the proposed AFA achieves better solutions than the basic FA and four other algorithms.

Keywords: firefly algorithm; adaptive parameters; random attraction model; flow shop scheduling problem; discrete optimisation.

DOI: 10.1504/IJWMC.2017.088089

International Journal of Wireless and Mobile Computing, 2017 Vol.13 No.2, pp.174 - 178

Received: 04 May 2017
Accepted: 31 May 2017

Published online: 14 Nov 2017 *

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