Search results for " optimization."
showing 10 items of 2333 documents
On the Exploratory Function of Agent-Based Modeling
2021
Abstract Agent-based models (ABM) derive the behavior of artificial socio-economic entities computationally from the actions of a large number of agents. One objection is that highly idealized ABMs fail to represent the real world in any reasonable sense. Another objection is that they at best show how observed patterns may have come about, because simulations are easy to produce and there is no evidence that this is really what happens. Moreover, different models may well yield the same result. I will rebut these objections by focusing on an often neglected, but crucial function of ABMs. Building on Gelfert’s (2016) account of the exploratory uses of scientific models I show that, in the a…
No-Preference Methods
1998
In no-preference methods, where the opinions of the decision maker are not taken into consideration, the multiobjective optimization problem is solved using some relatively simple method and the solution obtained is presented to the decision maker. The decision maker may either accept or reject the solution. It seems quite unlikely that the solution best satisfying the decision maker could be found with these methods. That is why no-preference methods are suitable for situations where the decision maker does not have any special expectations of the solution and (s)he is satisfied simply with some optimal solution. The working order here is: 1) analyst, 2) none.
A Priori Methods
1998
In the case of a priori methods, the decision maker must specify her or his preferences, hopes and opinions before the solution process. The difficulty is that the decision maker does not necessarily know beforehand what it is possible to attain in the problem and how realistic her or his expectations are. The working order in these methods is: 1) decision maker, 2) analyst.
Robust estimation of partial directed coherence by the vector optimal parameter search algorithm
2009
We propose a method for the accurate estimation of Partial Directed Coherence (PDC) from multichannel time series. The method is based on multivariate vector autoregressive (MVAR) model identification performed through the recently proposed Vector Optimal Parameter Search (VOPS) algorithm. Using Monte Carlo simulations generated by different MVAR models, the proposed VOPS algorithm is compared with the traditional Vector Least Squares (VLS) identification method. We show that the VOPS provides more accurate PDC estimates than the VLS (either overall and single-arc errors) in presence of interactions with long delays and missing terms, and for noisy multichannel time series. ©2009 IEEE.
Developing Domain-Knowledge Evolutionary Algorithms for Network-on-Chip Application Mapping
2013
This paper addresses the Network-on-Chip (NoC) application mapping problem. This is an NP-hard problem that deals with the optimal topological placement of Intellectual Property cores onto the NoC tiles. Network-on-Chip application mapping Evolutionary Algorithms are developed, evaluated and optimized for minimizing the NoC communication energy. Two crossover and one mutation operators are proposed. It is analyzed how each optimization algorithm performs with every genetic operator, in terms of solution quality and convergence speed. Our proposed operators are compared with state-of-the-art genetic operators for permutation problems. Finally, the problem is approached in a multi-objective w…
A multi-objective strategy for concurrent mapping and routing in networks on chip
2009
The design flow of network-on-chip (NoCs) include several key issues. Among other parameters, the decision of where cores have to be topologically mapped and also the routing algorithm represent two highly correlated design problems that must be carefully solved for any given application in order to optimize several different performance metrics. The strong correlation between the different parameters often makes that the optimization of a given performance metric has a negative effect on a different performance metric. In this paper we propose a new strategy that simultaneously refines the mapping and the routing function to determine the Pareto optimal configurations which optimize averag…
A note on the Bregmanized Total Variation and dual forms
2009
This paper considers two approaches to perform image restoration while preserving the contrast. The first one is the Total Variation-based Bregman iterations while the second consists in the minimization of an energy that involves robust edge preserving regularization. We show that these two approaches can be derived form a common framework. This allows us to deduce new properties and to extend and generalize these two previous approaches.
Tangent and Normal Cones in Nonconvex Multiobjective Optimization
2000
Trade-off information is important in multiobjective optimization. It describes the relationships of changes in objective function values. For example, in interactive methods we need information about the local behavior of solutions when looking for improved search directions.
Error bounds for a convexity-preserving interpolation and its limit function
2008
AbstractError bounds between a nonlinear interpolation and the limit function of its associated subdivision scheme are estimated. The bounds can be evaluated without recursive subdivision. We show that this interpolation is convexity preserving, as its associated subdivision scheme. Finally, some numerical experiments are presented.
Learning Automata-Based Solutions to Stochastic Nonlinear Resource Allocation Problems
2009
“Computational Intelligence” is an extremely wide-ranging and all-encompassing area. However, it is fair to say that the strength of a system that possesses “Computational Intelligence” can be quantified by its ability to solve problems that are intrinsically hard. One such class of NP-Hard problems concerns the so-called family of Knapsack Problems, and in this Chapter, we shall explain how a sub-field of Artificial Intelligence, namely that which involves “Learning Automata”, can be used to produce fast and accurate solutions to “difficult” and randomized versions of the Knapsack problem (KP).