A hyperheuristic optimization technique to reduce computational times for the design of pipeline networks is presented. The proposed strategy is an A-team approach comprising the guided execution of three metaheuristics: a genetic algorithm, simulated annealing, and an ant colony optimization. Besides, a specialized learning mechanism for information exchange was defined in order to speed up the search process. Moreover, the algorithm was implemented in parallel so as to allow several metaheuristics to run simultaneously, thus achieving a significant reduction of time overhead. In the algorithmic design, realistic scenarios were employed so as to appraise the impact of each agent on optimization efficiency. The cases correspond to real-worl...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
The increasing exploration of alternative methods for solving optimization problems causes that para...
The parallel genetic algorithms implementation for neural networks models construction is discussed....
This paper presents the general framework of a parallel cooperative hyper-heuristic optimizer (PCHO)...
In this work the determination of optimally located pipeline networks has been proposed by means of ...
This work describes a general algorithm for a cooperative hyper-heuristics that enables the optimiza...
Distributed computing environments are nowadays composed of many heterogeneous computers able to wor...
We showcase geospatial heuristic methods for network design and optimization. We propose and adapt g...
Abstract — This work examines a novel method that provides a parallel search of a very large network...
Hyper-heuristics operate at the level above traditional (meta-)heuristics that ‘optimise the optimis...
This dissertation presents a new algorithm for dynamic pipeline network time-area optimization, call...
AbstractHyper-heuristics operate at the level above traditional (meta-)heuristics that ‘optimise the...
Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes...
Fecha de lectura de Tesis Doctoral 14 mayo 2020Green parallel metaheuristics (GPM) is a new concept ...
The objective of this dissertation is to develop a multi-resolution optimization strategy based on t...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
The increasing exploration of alternative methods for solving optimization problems causes that para...
The parallel genetic algorithms implementation for neural networks models construction is discussed....
This paper presents the general framework of a parallel cooperative hyper-heuristic optimizer (PCHO)...
In this work the determination of optimally located pipeline networks has been proposed by means of ...
This work describes a general algorithm for a cooperative hyper-heuristics that enables the optimiza...
Distributed computing environments are nowadays composed of many heterogeneous computers able to wor...
We showcase geospatial heuristic methods for network design and optimization. We propose and adapt g...
Abstract — This work examines a novel method that provides a parallel search of a very large network...
Hyper-heuristics operate at the level above traditional (meta-)heuristics that ‘optimise the optimis...
This dissertation presents a new algorithm for dynamic pipeline network time-area optimization, call...
AbstractHyper-heuristics operate at the level above traditional (meta-)heuristics that ‘optimise the...
Three parallel physical optimization algorithms for allocating irregular data to multicomputer nodes...
Fecha de lectura de Tesis Doctoral 14 mayo 2020Green parallel metaheuristics (GPM) is a new concept ...
The objective of this dissertation is to develop a multi-resolution optimization strategy based on t...
153 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1999.Parallel implementations of g...
The increasing exploration of alternative methods for solving optimization problems causes that para...
The parallel genetic algorithms implementation for neural networks models construction is discussed....