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

Summary: This study introduces a cooperative water wave optimization algorithm CWWO to solve the distributed assembly no-idle flow-shop scheduling problem DANIFSP, aiming to minimize the maximum assembly completion time. The algorithm uses a reinforcement learning mechanism in the propagation phase, introduces a combination of path-relinking and VNS method as the modified breaking operator in the improvement phase, and applies a multi-neighborhood perturbation strategy in the refraction phase to increase the probability of escaping the local optimal.

Summary: This article discusses a single BPM scheduling problem with unequal release times and job sizes, proposing a self-adaptive differential evolution algorithm to address the issue. Experimental results show that the proposed algorithm is more effective in solving the scheduling problem compared to other existing algorithms.

Summary: Hardware/software partitioning is a significant problem in hardware-software co-design, and it is an NP-hard problem. In this study, a new greedy algorithm is proposed to solve the HW/SW problem effectively, and a general algorithm framework based on discrete evolutionary algorithm is introduced. By comparing the calculation results of various algorithms, it is confirmed that group theory-based optimization algorithm and binary particle swarm optimization are more suitable for solving large-scale HW/SW instances.