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État de l'art des méthodes “d'optimisation globale”

Gérard Berthiau, Patrick Siarry (2010)

RAIRO - Operations Research

We present a review of the main “global optimization" methods. The paper comprises one introduction and two parts. In the introduction, we recall some generalities about non linear constraint-less optimization and we list some classifications which have been proposed for the global optimization methods. We then describe, in the first part, various “classical" global optimization methods, most of which available long before the appearance of Simulated Annealing (a key event in this field)....

Evaluating the Kernighan-Lin heuristic for hardware/software partitioning

Zoltán Mann, András Orbán, Viktor Farkas (2007)

International Journal of Applied Mathematics and Computer Science

In recent years, several heuristics have been proposed for the hardware/software partitioning problem. One of the most promising directions is the adaptation of the Kernighan-Lin algorithm. The Kernighan-Lin heuristic was originally developed for circuit partitioning, but it has been adapted to other domains as well. Moreover, numerous improvements have been suggested so that now several variants of the original algorithm exist. The aim of this paper is to systematically evaluate the possibilities...

Event-triggered design for multi-agent optimal consensus of Euler-Lagrangian systems

Xue-Fang Wang, Zhenhua Deng, Song Ma, Xian Du (2017)

Kybernetika

In this paper, a distributed optimal consensus problem is investigated to achieve the optimization of the sum of local cost function for a group of agents in the Euler-Lagrangian (EL) system form. We consider that the local cost function of each agent is only known by itself and cannot be shared with others, which brings challenges in this distributed optimization problem. A novel gradient-based distributed continuous-time algorithm with the parameters of EL system is proposed, which takes the distributed...

Evolution Problems and Minimizing Movements

Ugo Gianazza, Massimo Gobbino, Giuseppe Savarè (1994)

Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni

We recall the definition of Minimizing Movements, suggested by E. De Giorgi, and we consider some applications to evolution problems. With regards to ordinary differential equations, we prove in particular a generalization of maximal slope curves theory to arbitrary metric spaces. On the other hand we present a unifying framework in which some recent conjectures about partial differential equations can be treated and solved. At the end we consider some open problems.

Evolutionary algorithms and fuzzy sets for discovering temporal rules

Stephen G. Matthews, Mario A. Gongora, Adrian A. Hopgood (2013)

International Journal of Applied Mathematics and Computer Science

A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented...

Evolutionary computation based on Bayesian classifiers

Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga (2004)

International Journal of Applied Mathematics and Computer Science

Evolutionary computation is a discipline that has been emerging for at least 40 or 50 years. All methods within this discipline are characterized by maintaining a set of possible solutions (individuals) to make them successively evolve to fitter solutions generation after generation. Examples of evolutionary computation paradigms are the broadly known Genetic Algorithms (GAs) and Estimation of Distribution Algorithms (EDAs). This paper contributes to the further development of this discipline by...

Evolutionary learning of rich neural networks in the Bayesian model selection framework

Matteo Matteucci, Dario Spadoni (2004)

International Journal of Applied Mathematics and Computer Science

In this paper we focus on the problem of using a genetic algorithm for model selection within a Bayesian framework. We propose to reduce the model selection problem to a search problem solved using evolutionary computation to explore a posterior distribution over the model space. As a case study, we introduce ELeaRNT (Evolutionary Learning of Rich Neural Network Topologies), a genetic algorithm which evolves a particular class of models, namely, Rich Neural Networks (RNN), in order to find an optimal...

Evolutionary optimization of interval mathematics-based design of a TSK fuzzy controller for anti-sway crane control

Jarosław Smoczek (2013)

International Journal of Applied Mathematics and Computer Science

A hybrid method combining an evolutionary search strategy, interval mathematics and pole assignment-based closed-loop control synthesis is proposed to design a robust TSK fuzzy controller. The design objective is to minimize the number of linear controllers associated with rule conclusions and tune the triangular-shaped membership function parameters of a fuzzy controller to satisfy stability and desired dynamic performances in the presence of system parameter variation. The robust performance objective...

Exact and stable least squares solution to the linear programming problem

Evald Übi (2005)

Open Mathematics

A linear programming problem is transformed to the finding an element of polyhedron with the minimal norm. According to A. Cline [6], the problem is equivalent to the least squares problem on positive ortant. An orthogonal method for solving the problem is used. This method was presented earlier by the author and it is based on the highly developed least squares technique. First of all, the method is meant for solving unstable and degenerate problems. A new version of the artifical basis method...

Existence of minimizers and necessary conditions in set-valued optimization with equilibrium constraints

Truong Q. Bao, Boris S. Mordukhovich (2007)

Applications of Mathematics

In this paper we study set-valued optimization problems with equilibrium constraints (SOPECs) described by parametric generalized equations in the form 0 ∈ G ( x ) + Q ( x ) , where both G and Q are set-valued mappings between infinite-dimensional spaces. Such models particularly arise from certain optimization-related problems governed by set-valued variational inequalities and first-order optimality conditions in nondifferentiable programming. We establish general results on the existence of optimal solutions under...

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