A nonmonotone GRASP

A greedy randomized adaptive search procedure (GRASP) is an itera- tive multistart metaheuristic for difficult combinatorial optimization problems. Each GRASP iteration consists of two phases: a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed solution is sought. Repeated applications of the con- struction procedure yields different starting solutions for the local search and the best overall solution is kept as the result. The GRASP local search applies iterative improvement until a locally optimal solution is found. During this phase, starting from the current solution an improving neighbor solution is accepted and considered as the new current solution. In this paper, we propose a variant of the GRASP framework that uses a new “nonmonotone” strategy to explore the neighborhood of the current solu- tion. We formally state the convergence of the nonmonotone local search to a locally optimal solution and illustrate the effectiveness of the resulting Nonmonotone GRASP on three classical hard combinatorial optimization problems: the maximum cut prob- lem (MAX-CUT), the weighted maximum satisfiability problem (MAX-SAT), and the quadratic assignment problem (QAP)

A Hybrid Heuristic Algorithm for HW-SW Partitioning Within Timed Automata

Hardware/Software (HW-SW) partitioning is a critical problem in co-design of embedded systems. This paper focuses on the synchronous system model, and formalizes the partitioning problem using timed automata (TA), which captures the key elements of the partitioning problem. Based on the TA model, we propose a hybrid heuristic algorithm to obtain near-optimal solutions effectively and efficiently. The experiments conducted show that our approach can deal with large applications with hundreds of nodes in task graph.Computer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsSCI(E)EICPCI-S(ISTP)