MGR: Multi-level global router

In this paper, we propose a new global router, BoxRouter, powered by the concept of box expansion, progressive integer linear programming (PILP), and adaptive maze routing (AMR). BoxRouter first uses a simple prerouting strategy to predict and capture the most congested region with high fidelity as compared to the final routing. Based on progressive box expansion initiated from the most congested region, BoxRouting is performed with PILP and AMR. Our PILP is shown to be much more efficient than the traditional ILP in terms of speed and quality, and the AMR based on multisource multitarget with bridge model is effective in minimizing the congestion and wirelength. It is followed by an effective postrouting step, which reroutes without rip-up to enhance the routing solution further and obtain smooth tradeoff between wirelength and routability. Our experimental results show that the BoxRouter significantly outperforms the state-of-the-art published global routers, e.g., 91 % better routability than Labyrinth (with 14% less wirelength and 3.3times speedup), 79% better routability than Chi-dispersion router (with similar wirelength and 2times speedup), and 4.2% less wirelength and 16times speedup than a multicommodity flow-based router (with similar routability). Additional enhancement in box expansion and postrouting further improves the result with similar wirelength but much better routability than the latest work in global routing.

In this paper, we present a new global router, NTHU-Route, for modern designs. NTHU-Route is based on iterative rip-ups and reroutes, and several techniques are proposed to enhance our global router. These techniques include (1) a history based cost function which helps to distribute overflow during iterative rip-ups and reroutes, (2) an adaptive multi-source multi-sink maze routing method to improve the wirelength of maze routing, (3) a congested region identification method to specify the order for nets to be ripped up and rerouted, and (4) a refinement process to further reduce overflow when iterative history based rip-ups and reroutes reach bottleneck. Compared with two state-of-the-art works on ISPD98 benchmarks, NTHU-Route outperforms them in both overflow and wirelength. For the much larger designs from the ISPD07 benchmark suite, our solution quality is better than or comparable to the best results reported in the ISPD07 routing contest.

The ACS (ant colony system) algorithm is quite suited for solving the obstacle detoured routing (ODR) problem in the physical design of VLSI circuits. But with the rapid growth of the number of circuit modules, the solution space of primitive ACS is substantially increased. In this paper, we present an heuristic algorithm based on an enhanced ACS to reduce the search space and accelerate the convergence. Firstly, this algorithm initializes the pheromone of a grid graph of a BBL (building block layout) mode layout respectively with an out of usual strategy. After that, we complete the main routing process based on the ACS algorithm with the heuristic of initial pheromone and obtain a feasible solution. In order to accelerate the convergence of primitive ACS, we developed a fast approach called the PEAK-CUT algorithm to shorten the path length. Taking advantage of its merits, we are able to get a local optimum solution closest to that feasible solution quickly. Comparative simulation experiments with respect to the original ACS demonstrate that our enhanced approach is superior to the ACS-only approach in several aspects, such as yielding better quality of solution, faster convergence and resulting in higher complete rate of routing.

In order to effectively and efficiently disclose the ever-growing amount of widely distributed RDF data to demanding users in real-time environments, RDF query engines need to optimize the join order of partial query results. For this, a two-phase optimization (2PO) algorithm and a genetic algorithm (GA) have already been proposed. We propose an alternative approach - an ant colony system (ACS). On a large RDF data source, our approach significantly outperforms both 2PO and the GA in terms of execution time and solution quality for RDF chain queries consisting of up to about ten joins. For larger queries, our novel ACS delivers solutions of better quality than 2PO does, while realizing a solution quality that is comparable to the solution quality of the GA method. However, the GA approach offers the best trade-off between execution time and solution quality for such larger queries.

In order to effectively and efficiently disclose the ever-growing amount of widely distributed RDF data to demanding users in real-time environments, RDF query engines need to optimize the join order of partial query results. For this, a two-phase optimization (2PO) algorithm and a genetic algorithm (GA) have already been proposed. We propose an alternative approach - an ant colony system (ACS). On a large RDF data source, our approach significantly outperforms both 2PO and the GA in terms of execution time and solution quality for RDF chain queries consisting of up to about ten joins. For larger queries, our novel ACS delivers solutions of better quality than 2PO does, while realizing a solution quality that is comparable to the solution quality of the GA method. However, the GA approach offers the best trade-off between execution time and solution quality for such larger queries.

In this paper, we propose a new value ordering heuristic for weighted constraint satisfaction problems (WCSP) based on the quality of solutions. The H-quality of an assignment estimates its capacity at occurring in solutions of good quality. Experiments using limited discrepancy search on random WCSP instances and CELAR benchmarks show that our value ordering always outperforms MinAC in a significant way.

We propose a parallel synchronous and asynchronous implementation of the coupled simulated annealing (CSA) algorithm in a shared-memory architecture. The original CSA was implemented synchronously in a distributed-memory architecture. It synchronizes at each temperature update, which leads to idling and loss of efficiency when increasing the number of processors. The proposed synchronous CSA (SCSA) is implemented as the original, but in a shared-memory architecture. The proposed asynchronous CSA (ACSA) does not synchronize, allowing a larger parallel efficiency for larger numbers of processors. Results from extensive experiments show that the proposed ACSA presents much better quality of solution when compared to the serial and to the SCSA. The experiments also show that the performance of the proposed ACSA is better than the SCSA for less computationally intensive problems or when a larger number of processing cores are available. Moreover, the parallel efficiency of the ACSA improves by increasing the size of the problem. With the advent of the Multi-core Era, the use of the proposed algorithm becomes more attractive than the original synchronous CSA.

This paper draws on the “human reliability” concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of deviation to encompass three elegant adjustments to literature model in terms of maintenance time, workforce performance and return-on-workforce investments. These fully explain the results of our influence. The presented structure breaks new grounds in maintenance workforce theory and practice from a number of perspectives. First, we have successfully implemented fuzzy goal programming (FGP) and differential evolution (DE) techniques for the solution of optimisation problem in maintenance of a process plant for the first time. The results obtained in this work showed better quality of solution from the DE algorithm compared with those of genetic algorithm and particle swarm optimisation algorithm, thus expressing superiority of the proposed procedure over them. Second, the analytical discourse, which was framed on stochastic theory, focusing on specific application to a process plant in Nigeria is a novelty. The work provides more insights into maintenance workforce planning during overhaul rework and overtime maintenance activities in manufacturing systems and demonstrated capacity in generating substantially helpful information for practice.

This paper presents a novel method for solving optimal power quality monitor placement problem in monitoring voltage sags in power systems using the adaptive quantum-inspired particle swarm optimization (PSO). The optimization considers multi objective functions and handles observability constraint determined by the concept of the topological monitor reach area. The overall objective function consists of two functions which are based on monitor overlapping index and sag severity index. In this algorithm, the standard quantum-inspired binary PSO is modified by applying the concept of artificial immune system as an adaptive element to make it more flexible towards better quality of solution and computational speed. The proposed algorithm is applied on the IEEE 30-bus transmission system and the IEEE 34-node distribution system and compared to the conventional PSO.

Sensibility Analysis with Genetic Algorithm to Allocate Distributed Generation and Capacitor Banks in Unbalanced Distribution Systems

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithms. LSTM model produces predictive signals that help inform the search trajectory of CS, enabling better exploration–exploitation tradeoff of resource scheduling on uncertainty. Results Simulation experiments on benchmark renewable energy datasets showed that ML-HCS not only converges 12% faster than the best of the GA, PSO, and classical CS, but also achieves 7–10% better quality of solutions and 9% higher robustness. This model also adapted better in multi-objective optimization tasks: cost minimization, scheduling stability and prediction accuracy. Conclusions Finally, the ML-HCS framework provides a prediction-oriented, data-driven, scalable optimization methodology for renewable energy systems. Its use of machine learning and metaheuristic search provide for high forecasting accuracy and resiliency in operation, which will enable its future large scale smart grid and renewable energy management applications.

With aggressive technology scaling, the complexity of the global routing problem is poised to grow rapidly. Solving such a large computational problem demands a high-throughput hardware platform such as modern graphics processing units (GPUs). In this paper, we explore a hybrid GPU-CPU high-throughput computing environment as a scalable alternative to the traditional CPU-based router. We introduce net-level concurrency (NLC), which is a novel parallel model for router algorithms and aims to exploit concurrency at the level of individual nets. To efficiently uncover NLC, we design a scheduler to create groups of nets that can be routed in parallel. At its core, our scheduler employs a novel algorithm to dynamically analyze data dependencies between multiple nets. We believe such an algorithm can lay the foundation for uncovering data-level parallelism in routing, which is a necessary requirement for employing high-throughput hardware. Detailed simulation results show an average of 4× speedup over NTHU-Route 2.0 with negligible loss in solution quality. To the best of our knowledge, this is the first work on utilizing GPUs for global routing.

<p>The topic of this Ph.D. thesis is the application of evolution-based algorithms (EAs) to various highly constrained combinatorial optimization problems arising in layout synthesis of VLSI integrated circuits. The purpose is to investigate which performance can be obtained by EAs compared to traditional methods, to identify strengths and weaknesses of EAs within this application domain and to identify possible EA design principles yielding the best performance.</p><p>In particular, the study focuses on genetic algorithms (GAs) for placement and global routing of macro-cell layouts. GAs for the these problems are developed, and their performance compared to that of existing state-of-the-art tools, in terms of absolute solution quality and absolute runtime. The main results are new GAs for both macro-cell placement and global routing, which are comparable to or better than the current state-of-the-art approaches. Especially, a GA for the Steiner problem in a graph is presented, which to our knowledge is the current best heuristic for this problem, in terms of solution quality as well as runtime.</p><p>Finally, various design principles significantly affecting algorithm performance are identified, of which the constraint handling method is of utmost importance. Within the application domain considered, enforcing constraint satisfaction at all times is found to consistently outperform methods based on penalty terms.</p>

This paper introduces a novel variant of PSO called accelerated exploration particle swarm optimizer (AEPSO). The AEPSO algorithm select the particles that are far away from the global solution and accelerates them towards global optima with an exploration power to avoid the premature convergence. The performance comparisons such as search efficiency, quality of solution and algorithmic complexity of the proposed algorithm are provided against different high performance PSOs. The comparison is carried out on the set of 30 and 50 dimensional complex multimodal benchmark functions with and without coordinate rotation. Simulation results indicate that the proposed algorithm gives robust results with good quality solution and faster convergence.

Wire routing, an important step in modern VLSI design, is increasingly responsible for timing closure and manufacturability. The CAD community has witnessed remarkable improvements in speed and quality of global routing algorithms in response to the inaugural ISPD 2007 Global Routing Contest, where prizes were awarded for best results on a new set of large industry benchmarks.