Search results for " optimization."
showing 10 items of 2333 documents
Experimental model-based linearization of a S.I. engine gas injector flow chart:
2014
Experimental tests previously executed by the authors on the simultaneous combustion of gasoline and gaseous fuel in a spark ignition engine revealed the presence of strong nonlinearities in the lower part of the gas injector flow chart. These nonlinearities arise via the injector outflow area variation caused by the needle impacts and bounces during the transient phenomena that take place in the opening and closing phases of the injector and may seriously compromise the air-fuel mixture quality control for the lower injection times, thus increasing both fuel consumption and pollutant emissions. Despite the extensive literature about the operation and modelling of fuel injectors, there are …
HOW SMART DOES AN AGENT NEED TO BE?
2005
The classic distributed computation is done by atoms, molecules or spins in vast numbers, each equipped with nothing more than the knowledge of their immediate neighborhood and the rules of statistical mechanics. These agents, 1023 or more, are able to form liquids and solids from gases, realize extremely complex ordered states, such as liquid crystals, and even decode encrypted messages. We will describe a study done for a sensor-array "challenge problem" in which we have based our approach on old-fashioned simulated annealing to accomplish target acquisition and tracking under the rules of statistical mechanics. We believe the many additional constraints that occur in the real problem ca…
Combining finite learning automata with GSAT for the satisfiability problem
2010
A large number of problems that occur in knowledge-representation, learning, very large scale integration technology (VLSI-design), and other areas of artificial intelligence, are essentially satisfiability problems. The satisfiability problem refers to the task of finding a satisfying assignment that makes a Boolean expression evaluate to True. The growing need for more efficient and scalable algorithms has led to the development of a large number of SAT solvers. This paper reports the first approach that combines finite learning automata with the greedy satisfiability algorithm (GSAT). In brief, we introduce a new algorithm that integrates finite learning automata and traditional GSAT use…
Solving Graph Coloring Problems Using Learning Automata
2008
The graph coloring problem (GCP) is a widely studied combinatorial optimization problem with numerous applications, including time tabling, frequency assignment, and register allocation. The growing need for more efficient algorithms has led to the development of several GCP solvers. In this paper, we introduce the first GCP solver that is based on Learning Automata (LA). We enhance traditional Random Walk with LA-based learning capability, encoding the GCP as a Boolean satisfiability problem (SAT). Extensive experiments demonstrate that the LA significantly improve the performance of RW, thus laying the foundation for novel LA-based solutions to the GCP.
Heuristics for the Constrained Incremental Graph Drawing Problem
2019
Abstract Visualization of information is a relevant topic in Computer Science, where graphs have become a standard representation model, and graph drawing is now a well-established area. Within this context, edge crossing minimization is a widely studied problem given its importance in obtaining readable representations of graphs. In this paper, we focus on the so-called incremental graph drawing problem, in which we try to preserve the user’s mental map when obtaining successive drawings of the same graph. In particular, we minimize the number of edge crossings while satisfying some constraints required to preserve the position of vertices with respect to previous drawings. We propose heur…
Multiple SIP strategies and bottom-up adorning in logic query optimization
1990
Preprocessing methods called “readorning” and “bottom-up adorning” are introduced as means of enlarging the application domain of magic sets and related query optimization strategies for logic databases. Readorning tries to make possible the simultaneous use of multiple sideways information passing (sip) strategies defined for a rule, thus yielding an optimization effect that may not be achieved by any particular choice of sip strategies. Bottom-up adorning is used to make magic sets applicable to cases in which potential optimizations can be derived from bindings coming upwards from rule bodies to rule heads in bottom-up evaluation. These include the cases in which we know that some base r…
A grid ant colony algorithm for the orienteering problem
2005
In this paper we propose a distributed ant colony algorithm to solve large scale orienteering problem instances. Our approach is based on a multi-colony strategy where each colony works in an independent portion (cluster) in the original graph. This results in no need for communicating pheromones information among colonies and in increasing speedup. We have implemented our algorithm as a .NET Web services infrastructure following a grid computing philosophy and we provide some promising experimental results to show the feasibility and effectiveness of our approach
Convex semi-infinite games
1986
This paper introduces a generalization of semi-infinite games. The pure strategies for player I involve choosing one function from an infinite family of convex functions, while the set of mixed strategies for player II is a closed convex setC inRn. The minimax theorem applies under a condition which limits the directions of recession ofC. Player II always has optimal strategies. These are shown to exist for player I also if a certain infinite system verifies the property of Farkas-Minkowski. The paper also studies certain conditions that guarantee the finiteness of the value of the game and the existence of optimal pure strategies for player I.
Existence and Optimality of Nash Equilibria in Inventory Games
2005
Abstract This paper studies the stability and optimality of a distributed consensus protocol for n -player repeated non cooperative games under incomplete information. At each stage, the players choose binary strategies and incur in a payoff monotonically decreasing with the number of active players. The game is specialized to an inventory application, where fixed costs are shared among all retailers, interested in whether reordering or not from a common warehouse. The authors focus on Pareto optimality as a measure of coordination of reordering strategies, proving that there exists a unique Pareto optimal Nash equilibrium that verifies certain stability conditions.
Noncooperative dynamic games for inventory applications: A consensus approach
2008
We focus on a finite horizon noncooperative dynamic game where the stage cost of a single player associated to a decision is a monotonically nonincreasing function of the total number of players making the same decision. For the single-stage version of the game, we characterize Nash equilibria and derive a consensus protocol that makes the players converge to the unique Pareto optimal Nash equilibrium. Such an equilibrium guarantees the interests of the players and is also social optimal in the set of Nash equilibria. For the multi-stage version of the game, we present an algorithm that converges to Nash equilibria, unfortunately not necessarily Pareto optimal. The algorithm returns a seque…