Nonlinear Network Optimization—An Embedding Vector Space Approach

Carrano, Eduardo G.; Takahashi, Ricardo H. C.; Fonseca, Carlos M.; Neto, Oriane M.

doi:10.1109/tevc.2009.2028330

Cited by 35 publications

(25 citation statements)

References 50 publications

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“…The comparison was performed on eight randomly generated instances, with different number of nodes (10,20,30,60) and topology (tree or star). For each instance, the average number of generations needed to find the optimal solution was computed over 30 runs with different initial populations.…”

Section: The One-max Tree Problemmentioning

confidence: 99%

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EvoGeneSys, a new evolutionary approach to graph generation

Cordella

Stefano

Fontanella

et al. 2013

Applied Soft Computing

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Graphs are widely used to represent complex and structured information of interest in various fields of science and engineering. When using graph representations, problems of special interest often imply searching. For example, searching for the prototypes representing a dataset of graphs or for the graph that optimizes a set of parameters. In any case, it is necessary that the problem solution be expressed in terms of graphs. Therefore, defining effective methods for automatically generating single graphs, or sets of graphs, representing problem solutions, is a key issue. A new evolutionary computation-based approach specifically devised for generating graphs is presented. The method is based on a special data structure, called multilist, which allows the encoding of any type of graph, directed or undirected, with or without attributes. Graph encoding by multilists makes it possible to define effective crossover and mutation operators, overcoming the problems normally encountered when implementing genetic operators on graphs. Further advantages of the proposed approach are that it does not require any problem specific knowledge and it is able to search for graphs whose number of nodes is not known a priori. Three sets of experiments were performed to test the proposed approach and the solutions found were compared with those obtained by other approaches proposed in the literature.

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Section: The One-max Tree Problemmentioning

confidence: 99%

“…More recently, in [20] real valued vectors have been used to directly encode tree-structured graphs. The aim is to use standard genetic operators for exploring tree search spaces.…”

Section: Introductionmentioning

confidence: 99%

EvoGeneSys, a new evolutionary approach to graph generation

Cordella

Stefano

Fontanella

et al. 2013

Applied Soft Computing

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“…Notwithstanding, there are other potential uses of the assignment of a geometry to a combinatorial problem that have not been exploited yet. Some of those uses have been suggested in Carrano et al (2010), without an instantiation to actual algorithms or problems. A contribution of this work is the investigation of the usage of two geometric entities that have not been considered yet in the literature about geometric operators: (i) descent directions, and (ii) subspaces.…”

Section: Introductionmentioning

confidence: 99%

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

Martins

Carrano

Wanner

et al. 2014

Evolutionary Computation

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Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors’ knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.

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“…Since the geometric properties of a decision domain play an important role in optimization algorithms that deal with real spaces, by indicating promising directions for the search, in the recent years it has been investigated the application of geometric concepts into combinatorial spaces, which may also guide the algorithm to some optimum, providing more efficient strategies to this optimization field [7]- [12]. An important property of real space problems that has inspired these applications is the distance concept, which supports the definition of some other ideas, such as the neighborhood notion.…”

Section: Introductionmentioning

confidence: 99%

A distance based variable neighborhood search for parallel machine scheduling

Batista

2014

2014 IEEE Symposium on Computational Intelligence in Production and Logistics Systems (CIPLS)

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Throughout the years, scheduling problems have been broadly addressed in the literature due to their wide application in practice. Some examples include the production line optimization, the scheduling aircraft landing, the daily nurse care, among others. In this work one investigate the efficiency of applying geometric-based operators in a version of this problem that deals with the schedule of independent tasks for parallel machines, which can be either identical or unrelated. In order to validate this study, a Variable Neighborhood Search approach is proposed and applied to a specific scheduling problem regarding the minimization of the weighted sum of the earliness/tardiness task, a well-known NP-Hard problem. The test instances are solved for either a due date known a priori or not. The algorithm is compared with two other methods from the literature and the results show promising.

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Nonlinear Network Optimization—An Embedding Vector Space Approach

Cited by 35 publications

References 50 publications

EvoGeneSys, a new evolutionary approach to graph generation

EvoGeneSys, a new evolutionary approach to graph generation

On a Vector Space Representation in Genetic Algorithms for Sensor Scheduling in Wireless Sensor Networks

A distance based variable neighborhood search for parallel machine scheduling

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