Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 103059, 15 pages
Research Article

A Tabu Search-Based Memetic Algorithm for Hardware/Software Partitioning

1Department of Mathematics, Minjiang University, Fuzhou 350108, China
2Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou 350108, China
3School of Computational and Applied Mathematics, Faculty of Science, University of the Witwatersrand, (Wits), Johannesburg 2050, South Africa
4TCSE, Faculty of Engineering and Build Environment, University of the Witwatersrand, (Wits), Johannesburg 2050, South Africa

Received 24 March 2014; Revised 16 June 2014; Accepted 16 June 2014; Published 13 July 2014

Academic Editor: Jiaji Wu

Copyright © 2014 Geng Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hardware/software (HW/SW) partitioning is to determine which components of a system are implemented on hardware and which ones on software. It is one of the most important steps in the design of embedded systems. The HW/SW partitioning problem is an NP-hard constrained binary optimization problem. In this paper, we propose a tabu search-based memetic algorithm to solve the HW/SW partitioning problem. First, we convert the constrained binary HW/SW problem into an unconstrained binary problem using an adaptive penalty function that has no parameters in it. A memetic algorithm is then suggested for solving this unconstrained problem. The algorithm uses a tabu search as its local search procedure. This tabu search has a special feature with respect to solution generation, and it uses a feedback mechanism for updating the tabu tenure. In addition, the algorithm integrates a path relinking procedure for exploitation of newly found solutions. Computational results are presented using a number of test instances from the literature. The algorithm proves its robustness when its results are compared with those of two other algorithms. The effectiveness of the proposed parameter-free adaptive penalty function is also shown.