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High Level Synthesis: from Algorithm to Digital Circuit- P26

High Level Synthesis: from Algorithm to Digital Circuit- P26: This book presents an excellent collection of contributions addressing different aspects of high-level synthesis from both industry and academia. "High-Level Synthesis: from Algorithm to Digital Circuit" should be on each designer's and CAD developer's shelf, as well as on those of project managers who will soon embrace high level design and synthesis for all aspects of digital system design. | 13 Operation Scheduling Algorithms and Applications 239 One of the first problems to which ACO was successfully applied was the Traveling Salesman Problem TSP 15 for which it gave competitive results comparing with traditional methods. Researchers have since formulated ACO methods for a variety of traditional NT-hard problems. These problems include the maximum clique problem the quadratic assignment problem the graph coloring problem the shortest common super-sequence problem and the multiple knapsack problem. ACO also has been applied to practical problems such as the vehicle routing problem data mining network routing problem and the system level task partitioning problem 12 48 49 . It was shown 19 that ACO converges to an optimal solution with probability of exactly one however there is no constructive way to guarantee this. Balancing exploration to achieve close-to-optimal results within manageable time remains an active research topic for ACO algorithms. MAX-MIN Ant System MMAS 42 is a popularly used method to address this problem. MMAS is built upon the original ACO algorithm which improves it by providing dynamically evolving bounds on the pheromone trails so that the heuristic never strays too far away from the best encountered solution. As a result all possible paths will have a non-trivial probability of being selected thus it encourages broader exploration of the search space while maintaining a good differential between alternative solutions. It was reported that MMAS was the best performing ACO approach on a number of classic combinatory optimization tasks. Both time constrained and resource constrained scheduling problems can be effectively solved by using ACO. Unfortunately in the consideration of space we can only give a general introduction on the ACO formulation for the TCS problem. For a complete treatment of the algorithms including detailed discussion on the algorithms implementation applicability complexity extensibility parameter selection .