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

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.

Dot Net Platform for Distributed Evolutionary Algorithms with Application in Hydroinformatics

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.