Dot Net Platform for Distributed Evolutionary Algorithms with Application in Hydroinformatics

2014 ◽  
pp. 362-386
Author(s):  
Boban Stojanović ◽  
Nikola Milivojević ◽  
Miloš Ivanović ◽  
Dejan Divac
Keyword(s):  
Evolutionary Algorithms ◽  
Complex Systems ◽  
Geographic Location ◽  
Computational Time ◽  
Cpu Time ◽  
Whole Process ◽  
Simulation Based ◽  
Scenarios Simulation ◽  
Distributed Evaluation ◽  
Distributed Evolutionary Algorithms

Real-world problems often contain nonlinearities, relationships, and uncertainties that are too complex to be modeled analytically. In these scenarios, simulation-based optimization is a powerful tool to determine optimal system parameters. Evolutionary Algorithms (EAs) are robust and powerful techniques for optimization of complex systems that perfectly fit into this concept. Since evolutionary algorithms require a large number of time expensive evaluations of candidate solutions, the whole process of optimization can take huge CPU time. In this chapter, .NET platform for distributed evaluation using WCF (Windows Communication Foundation) Web services is presented in order to reduce computational time. This concept provides parallelization of evolutionary algorithms independently of geographic location and platform where evaluation is performed. Hydroinformatics is a typical representative of fields where complex systems with many uncertainties are studied. Application of the developed platform in hydroinformatics is also presented in this chapter.

Simulation-Based Seismic Hazard Assessment Using Monte-Carlo Earthquake Catalogs: Application to CyberShake

2021 ◽  
Author(s):  
Sarah Azar ◽  
Mayssa Dabaghi
Keyword(s):  
Monte Carlo ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Seismic Hazard Assessment ◽  
Computational Time ◽  
Earthquake Catalog ◽  
Earthquake Catalogs ◽  
Earthquake Scenarios ◽  
Simulation Based ◽  
Hazard Curves

ABSTRACT The use of numerical simulations in probabilistic seismic hazard analysis (PSHA) has achieved a promising level of reliability in recent years. One example is the CyberShake project, which incorporates physics-based 3D ground-motion simulations within seismic hazard calculations. Nonetheless, considerable computational time and resources are required due to the significant processing requirements imposed by source-based models on one hand, and the large number of seismic sources and possible rupture variations on the other. This article proposes to use a less computationally demanding simulation-based PSHA framework for CyberShake. The framework can accurately represent the seismic hazard at a site, by only considering a subset of all the possible earthquake scenarios, based on a Monte-Carlo simulation procedure that generates earthquake catalogs having a specified duration. In this case, ground motions need only be simulated for the scenarios selected in the earthquake catalog, and hazard calculations are limited to this subset of scenarios. To validate the method and evaluate its accuracy in the CyberShake platform, the proposed framework is applied to three sites in southern California, and hazard calculations are performed for earthquake catalogs with different lengths. The resulting hazard curves are then benchmarked against those obtained by considering the entire set of earthquake scenarios and simulations, as done in CyberShake. Both approaches yield similar estimates of the hazard curves for elastic pseudospectral accelerations and inelastic demands, with errors that depend on the length of the Monte-Carlo catalog. With 200,000 yr catalogs, the errors are consistently smaller than 5% at the 2% probability of exceedance in 50 yr hazard level, using only ∼3% of the entire set of simulations. Both approaches also produce similar disaggregation patterns. The results demonstrate the potential of the proposed approach in a simulation-based PSHA platform like CyberShake and as a ground-motion selection tool for seismic demand analyses.

scholarly journals Distributed evolutionary algorithms with hierarchical evaluation

2009 ◽  
Vol 41 (11) ◽  
pp. 1037-1049 ◽  
Author(s):  
Ioannis C. Kampolis ◽  
Kyriakos C. Giannakoglou
Keyword(s):  
Evolutionary Algorithms ◽  
Distributed Evolutionary Algorithms ◽  
Hierarchical Evaluation

scholarly journals Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of Distributed Evolutionary Algorithms

2016 ◽  
Vol 16 (1) ◽  
pp. 80-88 ◽  
Author(s):  
Todor Balabanov ◽  
Iliyan Zankinski ◽  
Maria Barova
Keyword(s):  
Distributed Computing ◽  
Evolutionary Algorithms ◽  
Heterogeneous Computing ◽  
Local Population ◽  
Combinatorial Problem ◽  
Star Topology ◽  
Remote Computation ◽  
Distributed Evolutionary Algorithms ◽  
High Level ◽  
Communication Demands

Abstract One of the strongest advantages of Distributed Evolutionary Algorithms (DEAs) is that they can be implemented in distributed environment of heterogeneous computing nodes. Usually such computing nodes differ in hardware and operating systems. Distributed systems are limited by network latency. Some Evolutionary Algorithms (EAs) are quite suitable for distributed computing implementation, because of their high level of parallelism and relatively less intensive network communication demands. One of the most widely used topologies for distributed computing is the star topology. In a star topology there is a central node with global EA population and many remote computation nodes which are working on a local population (usually sub-population of the global population). This model of distributed computing is also known as island model. What is common for DEAs is an operation called migration that transfers some individuals between local populations. In this paper, the term 'distribution' will be used instead of the term 'migration', because it is more accurate for the model proposed. This research proposes a strategy for distribution of EAs individuals in star topology based on incident node participation (INP). Solving the Rubik's cube by a Genetic Algorithm (GA) will be used as a benchmark. It is a combinatorial problem and experiments are done with a C++ program which uses OpenMPI.

Using free cloud storage services for distributed evolutionary algorithms

2011 ◽  
Author(s):  
Maribel García-Arenas ◽  
Juan Julián Merelo Guervós ◽  
Pedro Castillo ◽  
Juan Luis Jiménez Laredo ◽  
Gustavo Romero ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Cloud Storage ◽  
Distributed Evolutionary Algorithms ◽  
Cloud Storage Services

Evolutionary Approaches and Their Applications to Distributed Systems

2010 ◽  
pp. 114-149 ◽  
Author(s):  
Thomas Weise ◽  
Raymond Chiong
Keyword(s):  
Distributed Systems ◽  
Evolutionary Algorithms ◽  
Network Topology ◽  
Large Scale ◽  
Computational Time ◽  
Distributed Environment ◽  
Rapid Changes ◽  
Np Complete ◽  
Nature Inspired Algorithms ◽  
Ubiquitous Presence

The ubiquitous presence of distributed systems has drastically changed the way the world interacts, and impacted not only the economics and governance but also the society at large. It is therefore important for the architecture and infrastructure within the distributed environment to be continuously renewed in order to cope with the rapid changes driven by the innovative technologies. However, many problems in distributed computing are either of dynamic nature, large scale, NP complete, or a combination of any of these. In most cases, exact solutions are hardly found. As a result, a number of intelligent nature-inspired algorithms have been used recently, as these algorithms are capable of achieving good quality solutions in reasonable computational time. Among all the nature-inspired algorithms, evolutionary algorithms are considerably the most extensively applied ones. This chapter presents a systematic review of evolutionary algorithms employed to solve various problems related to distributed systems. The review is aimed at providing an insight of evolutionary approaches, in particular genetic algorithms and genetic programming, in solving problems in five different areas of network optimization: network topology, routing, protocol synthesis, network security, and parameter settings and configuration. Some interesting applications from these areas will be discussed in detail with the use of illustrative examples.

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