Abstract: In this paper we present an application of Alternating Direction Implicit Algorithm to solving non-stationary PDE-s, allowing to obtain linear computational complexity. We illustrate this approach by solving two example non-stationary three-dimensional problems using explicit Euler time-stepping scheme: heat equation and linear elasticity equations for a cube.

Abstract: We propose a numerical approach based on the Lattice-Boltzmann method (LBM) for dealing with mesh refinement of Non-uniform Staggered Cartesian Grid. We explain, in detail, the strategy for mapping LBM over such geometries. The main benefit of this approach, compared to others, consists of solving all fluid units only once per time-step, and also reducing considerably the complexity of the communication and memory management between different refined levels. Also, it exhibits a better matching for parallel processors. To validate our method, we analyze several standard test scenarios, reaching satisfactory results with respect to other state-of-the-art methods. The performance evaluation proves that our approach not only exhibits a simpler and efficient scheme for dealing with mesh refinement, but also fast resolution, even in those scenarios where our approach needs to use a higher number of fluid units.

Abstract: While forestry is an important economic factor, the methods commonly used to estimate potential financial gains from undertaking a harvesting operation are usually based on heuristics and experience. Those methods use an abstract view on the harvesting project at hand, focusing on a few general statistical parameters. To improve the accuracy of felling cost estimates, we propose a novel, single-tree-based cost estimation approach, thich utilizes knowledge about the harvesting operation at hand to allow for a more specific and accurate estimate of felling costs. The approach utilizes well-known symbolic planning algorithms which are interfaced via the Planning Domain Definition Language (PDDL) and compile work orders. The work orders can then be used to estimate the total working time and thus the estimated cost for an individual harvesting project, as well as some additional efficiency statistics. Since a large proportion of today's harvesting operations are mechanized instead of motor manual, we focus on the planning of harvester and forwarder workflows. However, the use of these heavy forest machines carries the risk of damaging forest soil when repeatedly driving along skidding roads. Our approach readily allows for assessment of these risks.

Abstract: This paper is focused on genetic algorithm with chaotic crossover operator. We have performed some experiments to study possible use of chaos in simulated evolution. A novel genetic algorithm with chaotic optimization operation is proposed to optimization of multimodal functions. As the basis of a new crossing operator a simple equation involving chaos is used, concrete the logistic function. The logistic function is a simple one-parameter function of the second order that shows a chaotic behavior for some values of the parameter. Generally, solution of the logistic function has three areas of its behavior: convergent, periodic and chaotic. We have supposed that the convergent behavior leads to exploitation and the chaotic behavior aids to exploration. The periodic behavior is probably neutral and thus it is a negligible one. Results of our experiments conrm these expectations. A proposed genetic algorithm with chaotic crossover operator leads to more ecient computation in comparison with the traditional genetic algorithm.

Abstract: Dynamic task allocation in a robotic swarm is a necessary process for proper management of the swarm. It allows the distribution of the identified tasks to be performed, among the swarm of robots, in such a way that a pre-defined proportion of execution of those tasks is achieved. In this context, there is no central unit to take care of the task allocation. So any algorithm proposal must be distributed, allowing every, and each robot in the swarm to identify the task it must perform. This paper proposes a distributed control algorithm to implement dynamic task allocation in a swarm robotics environment. The algorithm is inspired by the particle swarm optimization. In this context, each robot that integrates the swarm must run the algorithm periodically in order to control the underlying actions and decisions. The algorithm was implemented on ELISA III real swarm robots and extensively tested. The algorithm is effective and the corresponding performance is promising.