Radio network design using coarse-grained parallel genetic algorithms with different neighbor topology

In this article, evolutionary algorithms (EAs) are applied to solve the radio network design problem (RND). The task is to find the best set of transmitter locations in order to cover a given geographical region at an optimal cost. Usually, parallel EAs are needed to cope with the high computational requirements of such a problem. Here, we develop and evaluate a set of sequential and parallel genetic algorithms (GAs) to solve the RND problem efficiently. The results show that our distributed steady state GA is an efficient and accurate tool for solving RND that even outperforms existing parallel solutions. The sequential algorithm performs very efficiently from a numerical point of view, although the distributed version is much faster.

Download Full-text

Parallel genetic algorithms for communication network design

Proceedings of IEEE International Symposium on Parallel Algorithms Architecture Synthesis ◽

10.1109/aispas.1997.581701 ◽

2002 ◽

Cited By ~ 5

Author(s):

Runhe Huang ◽

Jianhua Ma ◽

T.L. Kunii ◽

E. Tsuboi

Keyword(s):

Communication Network ◽

Genetic Algorithms ◽

Network Design ◽

Parallel Genetic Algorithms ◽

Communication Network Design

Download Full-text

Parallel Genetic Algorithms for University Scheduling Problem

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i2.pp1096-1102 ◽

2017 ◽

Vol 7 (2) ◽

pp. 1096

Author(s):

Artan Berisha ◽

Eliot Bytyçi ◽

Ardeshir Tershnjaku

Keyword(s):

Genetic Algorithms ◽

Execution Time ◽

Parallel Implementation ◽

Coarse Grained ◽

Scheduling Problem ◽

Evaluation Function ◽

Parallel Genetic Algorithms ◽

Hard Problems ◽

Np Hard Problems ◽

Better Than

University scheduling timetabling problem, falls into NP hard problems. Re-searchers have tried with many techniques to find the most suitable and fastest way for solving the problem. With the emergence of multi-core systems, the parallel implementation was considered for finding the solution. Our approaches attempt to combine several techniques in two algorithms: coarse grained algorithm and multi thread tournament algorithm. The results obtained from two algorithms are compared, using an algorithm evaluation function. Considering execution time, the coarse grained algorithm performed twice better than the multi thread algorithm.

Download Full-text

Research on Computer Networks Reliability Optimization by Parallel Genetic Algorithms Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2984 ◽

2014 ◽

Vol 543-547 ◽

pp. 2984-2987

Author(s):

Xu Cao ◽

Jun Pan

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Network Topology ◽

Coarse Grained ◽

Reliability Optimization ◽

Parallel Genetic Algorithm ◽

Parallel Genetic Algorithms ◽

Cost Constraints ◽

Speed Up

This paper can be asserted that the use of parallel genetic algorithm can not only effectively improve the calculation speed and optimize the quality, but also can improve a lot of advantages. Reliability optimization for computer net works, subject s to cost constraints, is a NP-hard combinational problem. Reg ar ding a known network topology, the problem of choosing links and switchers among alternatives different in reliability and cost is settled by a Coarse-grained parallel genetic algorithm, which maximize the network availablity within a fixed budget. T he simulations on a dedicated cluster demonst rate that contracting to the sequential counterpart, o ur par allel GA improves the quality of plans greatly with an evident speed-up.

Download Full-text