The aim of the article is to choose algorithms suited for optimal placement of piles in real grillages by performing experimental comparison of different global optimization algorithms. The comparison includes several algorithms: random search, metaheuristics (simulated annealing and genetic algorithm) and local optimization combined with random search. The algorithms are compared using the res...

Because of complexity in modern printed wiring board (PWB) designs which can consist of a large number of components, the placement of the components has become an extremely time consuming task and may involve tradeoffs of equally important weight. To help a design engineer, placement automation techniques have been developed. Most of them are developed for improving wireability [Che84, Got78, ...

A good placement of sensors is crucial to get good performance in detecting and isolating faults. Here, the sensor placement problem is cast as a minimal cost optimization problem. Previous works have considered this problem with qualitative detectability and isolability specifications. A key contribution here is that quantified detectability and isolability performance is considered in the opt...

Simulated annealing, a methodology for solving combinatorial optimization problems, is a very computationally expensive algorithm, and as such, numerous researchers have undertaken efforts to parallelize it. In this paper, we investigate three of these parallel simulated annealing strategies when applied to standard cell placement, specifically the TimberWolfSC placement tool. We have examined ...

Virtual machine (VM) placement in the cloud infrastructure is an important problem that remains to be effectively addressed. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for cloud data center...

Genetic algorithms are one of the most well-known evolutionary computing methods using heuristic searches that mimic the process of natural evolution. These methods provide very useful results to optimization and search problems. There are several factors which have particular impact on algorithm to be successful. That means correct population initialization, sufficient simulation time and opti...