Search results for "optimization"
showing 10 items of 2824 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
Design of Representations and Search Operators
2015
Successful and efficient use of evolutionary algorithms depends on the choice of genotypes and the representation – that is, the mapping from genotype to phenotype – and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other. This chapter gives recommendations on the design of representations and corresponding search operators and discusses how to consider problem-specific knowledge. For most problems in the real world, similar solutions have similar fitness values. This fact can be exploited by evolutionary algorithms if they ensure that the representations and search operators used are defined in such a way that simila…
High Locality Representations for Automated Programming
2011
We study the locality of the genotype-phenotype mapping used in grammatical evolution (GE). GE is a variant of genetic programming that can evolve complete programs in an arbitrary language using a variable-length binary string. In contrast to standard GP, which applies search operators directly to phenotypes, GE uses an additional mapping and applies search operators to binary genotypes. Therefore, there is a large semantic gap between genotypes (binary strings) and phenotypes (programs or expressions). The case study shows that the mapping used in GE has low locality leading to low performance of standard mutation operators. The study at hand is an example of how basic design principles o…
Optimal Resource Discovery Paths of Gnutella2
2008
This paper shows that the performance of peer-to-peer resource discovery algorithms is upper bounded by a k-Steiner minimum tree and proposes an algorithm locating near-optimal query paths for the peer-to-peer resource discovery problem. Global knowledge of the topology and the resources from the peer-to-peer network are required as an input to the algorithm. The algorithm provides an objective measure for defining how good local search algorithms are. The performance is evaluated in simulated peer-to-peer scenarios and in the measured Gnutella2 P2P network topology with four local search algorithms: breadth-first search, self-avoiding random walker, highest degree search and Dynamic Query …