A study of hybrid parallel genetic algorithm model

Wang, Zhu-rong; Ju, Tao; Du-wu, Cui; Hei, Xinhong

doi:10.1109/icnc.2011.6022186

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Their study showed a significant speed-up using up to 16 processors on two computers, while, if other processors are added to the calculation, the speed-up degraded due to the increase of communications. The work in [3] presents a hybrid parallel genetic algorithm (HPGA) based on two-layer parallelism of processes and threads, and it is used to solve reference benchmark functions. The solution uses a hybrid coarse-grained master-slave PGA, integrating MPI and OpenMP parallel programming model.…”

Section: Preliminariesmentioning

confidence: 99%

An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems

Muttillo

Giammatteo

Fiorilli

et al. 2020

Proceedings of the 11th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures / 9th W

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Heterogeneous multiprocessor platforms are becoming widespread in the embedded system domain, mainly for the opportunity to improve timing performance and to minimize energy/power consumption and costs. Therefore, when using such platforms, it is important to adopt a Design Space Exploration (DSE) strategy that considers compromises among different objectives. Existing DSE approaches are generally based on evolutionary algorithms to solve Multi-Objective Optimization Problems (MOOPs) by minimizing a linear combination of weighted cost functions (i.e., Weighted Sum Method, WSM). In this way, the main issues are related to reduce timing execution while trying to improve the evolutionary algorithm performance, introducing strategies that attempt to bring better solutions. Code parallelization is one of the most used approaches in this field, but no standard methods have been released since different aspects could affect the performance. This approach leads to exploit parallel and distributed processing elements in order to implement evolutionary algorithms. In the latter case, if we consider genetic algorithms, it is possible to talk about Parallel Genetic Algorithms (PGA). Considering this context, this paper focuses on DSE for heterogeneous multi-processor embedded systems and introduces an improvement that reduces execution time using parallel programming languages (i.e., OpenMP) inside the main genetic algorithm approach, while trying to lead to better partitioning solutions. The descriptions of the adopted DSE activities and the OpenMP implementation, validated by means of a case study, represent the core of the paper. CCS CONCEPTS • Computer systems organization → Embedded systems; Embedded hardware;

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Section: Preliminariesmentioning

confidence: 99%

An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems

Muttillo

Giammatteo

Fiorilli

et al. 2020

Proceedings of the 11th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures / 9th W

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“…As an extension to the above three methods and in the style of Wang et al [29], the two modes of parallelism -OpenMP and MPI-can be combined. The MPI communication can be used to distribute the computation between the distinct cluster nodes, whilst the OpenMP style OMP XED can also be used to distribute the local population across the nodes' cores.…”

Section: Mpi Islandsmentioning

confidence: 99%

“…OMP XED uses OpenMP to distribute NSGAII's population evolution across the cores of a single host machine in the same style as Wang et al [29]. Algorithm 1 is run for each generation of the Genetic Algorithm such that a single loop iteration -or job -is passed to a core at a time and as each core finishes its current job, it is assigned the next available job in the loop iteration.…”

Section: Shared Memorymentioning

confidence: 99%

“…In this field hybrid solutions have made a significant impact. Wang et al [29] run a Hybrid PGA on benchmark single objective problems on a small system (4 nodes of 2 cores) using MPI communication between islands, whilst using the OpenMP to distribute the evaluation tasks in a master slave arrangement. They state that the main thread performs the Genetic Algorithm framework and all the genetic operators while others are responsible for the evaluation of the objective function.…”

Section: Introductionmentioning

confidence: 99%

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Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms

Cooper

John

Lewis

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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Abstract-In this paper we present a novel tool, using both OpenMP and MPI protocols, for optimising the efficiency of Urban Transportation Systems within a defined catchment, town or city. We build on a previously presented model which uses a Genetic Algorithm with novel genetic operators to optimise route sets and provide a transport network for a given problem set. This model is then implemented within a Parallel Multi-Objective Genetic Algorithm and demonstrated to be scalable to within the scope of real world, [city-wide], problems. This paper compares and contrasts three methods of parallel distribution of the Genetic Algorithm's computational workload: a job farming algorithm and two variations on an 'Islands' approach. Results are presented in the paper from both single and mixed mode strategies. The results presented are from a range of previously published academic problem sets. Additionally a real world inspired problem set is evaluated and a visualisation of the optimised output is given.

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“…Wang et al [10] implemented a hybrid parallel genetic algorithm based on two layers of parallelism: process and thread. Their proposed coarse-grained method uses a hardware processing master-slave model by integrating message-passing parallelism using Message Passing Interface (MPI) and sharedmemory parallelism using OpenMP [11].…”

Section: Introductionmentioning

confidence: 99%

A coarse-grained parallelization of genetic algorithms

Rathomi

Pulungan

2018

Int. J. Adv. Intell. Informatics

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Genetic algorithms are frequently used to solve optimization problems. However, the problems become increasingly complex and time consuming. One solution to speed up the genetic algorithm processing is to use parallelization. The proposed parallelization method is coarse-grained and employs two levels of parallelization: message passing with MPI and Single Instruction Multiple Threads with GPU. Experimental results show that the accuracy of the proposed approach is similar to the sequential genetic algorithm. Parallelization with coarse-grained method, however, can improve the processing and convergence speed of genetic algorithms.

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A study of hybrid parallel genetic algorithm model

Cited by 7 publications

References 5 publications

An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems

An OpenMP Parallel Genetic Algorithm for Design Space Exploration of Heterogeneous Multi-processor Embedded Systems

Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms

A coarse-grained parallelization of genetic algorithms

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