HSLS: An efficient local search algorithm for the hardware and software partitioning problem

Abstract

Hardware and software partitioning (HW/SW) is a crucial step in the co‐design of embedded systems, which is a typical combinatorial optimisation problem. In practice, HW/SW instances would be of large scale, so it is of significant importance to design effective heuristic algorithms to obtain a high‐quality partitioning scheme. Different from previous heuristics, the HW/SW problem is treated as the minimum weight dominating set (MWDS) problem in this letter, and the mature local search framework for solving the MWDS problem is introduced to solve the HW/SW problem. In addition, two suitable vertex selection rules based on scoring function and tabu are proposed. Combined with the introduced local search framework, an efficient local search algorithm named HSLS is obtained. 96 classic benchmark instances are used to evaluate the performance of the HSLS algorithm, and the four latest evolutionary algorithms designed for the HW/SW problem are used as comparison algorithms, which are genetic algorithm, binary particle swarm optimisation, differential evolution, and group theory‐based optimisation algorithm. The experimental results show that the HSLS algorithm is far superior to the four comparison algorithms in terms of the optimal solution and obtains optimal solution on 78 benchmark instances, the four comparison algorithms obtain 43, 29, 42, and 41 optimal solutions respectively.

A local search algorithm with hybrid strategies for the maximum weighted quasi‐clique problem

“…It is always tighter than the previous bounds, and leads to a significant reduction in search trees. Moreover, heuristics and meta-heuristics have shown a good ability to solve various optimization problems [12][13][14][15], and are important approaches to solving the MQCP. For example, Pinto et al proposed two variants of a biased random-key genetic algorithm for solving the MQCP [7].…”