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R.A. Gallego
Researcher at State University of Campinas
Publications - 9
Citations - 1756
R.A. Gallego is an academic researcher from State University of Campinas. The author has contributed to research in topics: Simulated annealing & Combinatorial optimization. The author has an hindex of 8, co-authored 8 publications receiving 1707 citations.
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Transmission system expansion planning by simulated annealing
TL;DR: In this article, a simulated annealing approach to the long-term transmission expansion planning problem is presented, which is a hard, large scale combinatorial problem and is compared with a more conventional optimization technique based on mathematical decomposition with a zero-one implicit enumeration procedure.
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Optimal Capacitor Placement in Radial Distribution Networks
TL;DR: In this paper, a hybrid method drawn upon the Tabu search approach, extended with features taken from other combinatorial approaches such as genetic algorithms and simulated annealing, and from practical heuristic approaches is proposed.
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Multistage and coordinated planning of the expansion of transmission systems
TL;DR: In this paper, an efficient genetic algorithm (GA) is presented to solve the problem of multistage and coordinated transmission expansion planning, which is a mixed integer nonlinear programming problem, difficult for systems of medium and large size and high complexity.
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Tabu search algorithm for network synthesis
TL;DR: The proposed parallel tabu search algorithm has shown to be effective in exploring this type of optimization landscape and is the most comprehensive combinatorial optimization technique available for treating difficult problems such as the transmission expansion planning.
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Transmission system expansion planning by an extended genetic algorithm
TL;DR: In this paper, an extended genetic algorithm for solving the optimal transmission network expansion planning problem is presented, where two main improvements have been introduced in the genetic algorithm: (a) initial population obtained by conventional optimisation based methods; (b) mutation approach inspired in the simulated annealing technique.