De Melo V.V.,University of Sao Paulo |
Expert Systems with Applications | Year: 2014
In the last decades, a number of novel meta-heuristics and hybrid algorithms have been proposed to solve a great variety of optimization problems. Among these, constrained optimization problems are considered of particular interest in applications from many different domains. The presence of multiple constraints can make optimization problems particularly hard to solve, thus imposing the use of specific techniques to handle fitness landscapes which generally show complex properties. In this paper, we introduce a modified Covariance Matrix Adaptation Evolution Strategy (CMA-ES) specifically designed for solving constrained optimization problems. The proposed method makes use of the restart mechanism typical of most modern variants of CMA-ES, and handles constraints by means of an adaptive penalty function. This novel CMA-ES scheme presents competitive results on a broad set of benchmark functions and engineering problems, outperforming most state-of-the-art algorithms as for both efficiency and constraint handling. © 2014 Elsevier Ltd. All rights reserved.
Stein Shiromoto H.,INCAS3 |
Andrieu V.,University Claude Bernard Lyon 1 |
Andrieu V.,University of Wuppertal |
Prieur C.,CNRS GIPSA Laboratory
Automatica | Year: 2015
A sufficient condition for the stability of a system resulting from the interconnection of dynamical systems is given by the small gain theorem. Roughly speaking, to apply this theorem, it is required that the gains composition is continuous, increasing and upper bounded by the identity function. In this work, an alternative sufficient condition is presented for the case in which this criterion fails due to either lack of continuity or the bound of the composed gain is larger than the identity function. More precisely, the local (resp. non-local) asymptotic stability of the origin (resp. global attractivity of a compact set) is ensured by a region-dependent small gain condition. Under an additional condition that implies convergence of solutions for almost all initial conditions in a suitable domain, the almost global asymptotic stability of the origin is ensured. Two examples illustrate and motivate this approach. © 2014 Elsevier. Ltd All rights reserved.
De Roo F.,INCAS3 |
Mazo Jr. M.,Technical University of Delft
Proceedings of the IEEE Conference on Decision and Control | Year: 2013
In the present paper we provide a methodology to approximately solve complex optimal control problems by using symbolic models. It has been shown in recent years that symbolic models (also called discrete abstractions) offer a convenient approach to deal with the analysis and control of complex qualitative control problems. We show how the notion of approximate simulation enables also the transfer of quantitative information between a given control system and its symbolic model. In particular, we show that quantities computed on the symbolic model provide lower and upper bounds for the optimal achievable cost of the control system. Finally, we indicate how the theoretical results may be applied to automatically synthesize controllers for qualitative specifications, given in a fragment of Linear Temporal Logics, and simultaneously guaranteeing some lower and upper bounds for the attainable cost. ©2013 IEEE.
Bucur D.,University of Groningen |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016
We live in a world of social networks. Our everyday choices are often influenced by social interactions. Word of mouth, meme diffusion on the Internet, and viral marketing are all examples of how social networks can affect our behaviour. In many practical applications, it is of great interest to determine which nodes have the highest influence over the network, i.e., which set of nodes will, indirectly, reach the largest audience when propagating information. These nodes might be, for instance, the target for early adopters of a product, the most influential endorsers in political elections, or the most important investors in financial operations, just to name a few examples. Here, we tackle the NP-hard problem of influence maximization on social networks by means of a Genetic Algorithm. We show that, by using simple genetic operators, it is possible to find in feasible runtime solutions of high-influence that are comparable, and occasionally better, than the solutions found by a number of known heuristics (one of which was previously proven to have the best possible approximation guarantee, in polynomial time, of the optimal solution). The advantages of Genetic Algorithms show, however, in them not requiring any assumptions about the graph underlying the network, and in them obtaining more diverse sets of feasible solutions than current heuristics. © Springer International Publishing Switzerland 2016.
Zamani M.,University of California at Los Angeles |
Pola G.,University of LAquila |
Mazo Jr. M.,INCAS3 |
Mazo Jr. M.,University of Groningen |
Tabuada P.,University of California at Los Angeles
IEEE Transactions on Automatic Control | Year: 2012
Finite-state models of control systems were proposed by several researchers as a convenient mechanism to synthesize controllers enforcing complex specifications. Most techniques for the construction of such symbolic models have two main drawbacks: either they can only be applied to restrictive classes of systems, or they require the exact computation of reachable sets. In this paper, we propose a new abstraction technique that is applicable to any nonlinear sampled-data control system as long as we are only interested in its behavior in a compact set. Moreover, the exact computation of reachable sets is not required. The effectiveness of the proposed results is illustrated by synthesizing a controller to steer a vehicle. © 2011 IEEE.