Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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01 Apr 1992
TL;DR: In this paper entropy and information measures are defined which form the basis for the use of information theoretic greedy heuristics for computing solutions to multi-stage decision problems.
Abstract: Many problems in computer science such as the traveling salesman problem, the maximum clique problem, and the set cover problem can be described as multi-stage decision problems. In this paper entropy and information measures are defined which form the basis for the use of information theoretic greedy heuristics for computing solutions to such problems. A bound on the computed solution is given in terms of the measures which, in that sense, provides a formal justification for the greedy heuristic.
1 citations
TL;DR: It is shown that the optimization of the graph partitions is particularly difficult for sparse graphs with average connectivities near the percolation threshold, and a new general purpose method based on self-organized criticality produces near-optimal partitions with bounded error at any low connectivity at a comparable computational cost.
Abstract: The partitioning of random graphs is investigated numerically using “simulated annealing” and “extremal optimization”. While generally in an NP-hard problem, it is shown that the optimization of the graph partitions is particularly difficult for sparse graphs with average connectivities near the percolation threshold. At this threshold, the relative error of “simulated annealing” is found to diverge in the thermodynamic limit. On the other hand, “extremal optimization”, a new general purpose method based on self-organized criticality, produces near-optimal partitions with bounded error at any low connectivity at a comparable computational cost.
1 citations
01 Jan 1991
TL;DR: Using Genetic Algorithms to Solve Combinatorial Optimization Problems and how they can be used to improve the quality of human-computer interaction.
Abstract: OF THE THESIS Using Genetic Algorithms to Solve Combinatorial Optimization Problems
1 citations
Posted Content•
TL;DR: A parsimonious method to deal with the capacitor placement problem that incorporates resonance constraints, ensuring that every allocated capacitor will not act as a harmonic source is proposed, based upon a physical inspired metaheuristic known as Extremal Optimization.
Abstract: Installation of capacitors in distribution networks is one of the most used procedure to compensate reactive power generated by loads and, consequently, to reduce technical losses. So, the problem consists in identifying the optimal placement and sizing of capacitors. This problem is known in the literature as optimal capacitor placement problem. Neverthless, depending on the location and size of the capacitor, it may become a harmonic source, allowing capacitor to enter into resonance with the distribution network, causing several undesired side effects. In this work we propose a parsimonious method to deal with the capacitor placement problem that incorporates resonance constraints, ensuring that every allocated capacitor will not act as a harmonic source. This proposed algorithm is based upon a physical inspired metaheuristic known as Extremal Optimization. The results achieved showed that this proposal has reached significant gains when compared with other proposals that attempt repair, in a post-optimization stage, already obtained solutions which violate resonance constraints.
1 citations