<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Institute of Information and Communication Technologies, BAS, 1113 Sofia, Bulgaria

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

One of the strongest advantages of Distributed Evolutionary Algorithms (DEAs) is that they can be implemented in distributed environment of heterogeneous...

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

Published Online: Apr 09, 2016

Page range: 80 - 88

DOI: https://doi.org/10.1515/cait-2016-0006

Keywords
Distributed evolutionary algorithms, migration strategy, incident nodes participation

© 2016 Todor Balabanov et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Apr 09, 2016

Page range: 80 - 88

DOI: https://doi.org/10.1515/cait-2016-0006

KeywordsDistributed evolutionary algorithms, migration strategy, incident nodes participation

© 2016 Todor Balabanov et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Distributed evolutionary algorithms, migration strategy, incident nodes participation