Parallel genetic algorithms on the graphics processing units using island model and simulated annealing

Li, Cheng-Chieh; Lin, Chu‐Hsing; Liu, Jung-Chun

doi:10.1177/1687814017707413

Cited by 19 publications

(22 citation statements)

References 10 publications

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“…During the last few years, the capability of GPU is growing much faster than that of CPU's because of greatly increasing hardware requirement for modern computer games. The GPU is also rapidly and widely used for various scientific computations in addition to graphic display [30], such as fluid dynamics [31], biophysics [32], molecular dynamics [33], and IoT sensing [34]. GPUs can provide huge performance improvement than a single CPU core for many applications.…”

Section: Cuda Gpumentioning

confidence: 99%

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good facility layout can achieve efficient production management, improve production efficiency, and create high economic values. Because FLP is an NP-hard problem, meaning it is impossible to find the optimal solution when problem becomes sufficiently large, various evolutionary algorithms (EAs) have been proposed to find a sub-optimal solution within a reasonable time interval. Recently, a genetic algorithm (GA) was proposed for unequal area FLP (UA-FLP), where the areas of facilities are not identical. More precisely, the GA is an island model based, which is called IMGA. Since EAs are still very time consuming, many efforts have been devoted to how to parallelize various EAs including IMGA. In recent work, Steffen and Dietmar proposed how to parallelize island models of EAs. However, their parallelization approaches are preliminary because they focused mainly on comparing the performances between different parallel architectures. In addition, they used one mathematical function to model the problem. To further investigate on how to parallelize the IMGA by GPU, in this paper we propose multiple parallel algorithms, for each individual step in the IMGA when solving the industrial engineering problem, UA-FLP, and conduct experiments to compare their performances. After integrating better algorithms for all steps into the IMGA, our GPU implementation outperforms the CPU counterpart and the best speedup can be as high as 84.

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Section: Cuda Gpumentioning

confidence: 99%

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

et al. 2018

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“…They perform parallel computing via assigning unused processors with the subpopulations. They separate a huge population into smaller ones and execute concurrent irregular searches among them [24].…”

Section: ) Parallel Algorithmsmentioning

confidence: 99%

“…Fig. 1 The DE steps, adapted from [31] Various recent advancements to the differential evolution have been proposed in the last 8 years [24]. For instance, differential evolution (like other evolutionary) has been parallelized to refine its speed and accuracy on various applications.…”

Section: ) Differential Evolution (A Recent Evolutionary Algorithm)mentioning

confidence: 99%

On Tackling Real-Life Optimization Problems

Abd-Alsabour¹

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Most real-world applications are concerned with minimizing or maximizing some quantity so as to enhance some result. This emphasizes the importance of optimization and subsequently the significance of the optimization methods that are able to tackle these real-life optimization problems. There are a number of practical reasons for which traditional optimization and exhaustive algorithms cannot deal with a variety of these real-life optimization applications although there are numerous optimization problems that can benefit from applying these traditional optimization algorithms to handle them. Therefore, their is a need for propsong new optimization algorithms (such as nature inspired optimization methods) and optimize the capabilities of the existing ones (such as hybridization and parallelization) as well. This paper investigates the most recent optimization directions for dealing with the real-life optimization problems with an application to one of the most common and important optimization problems in a variety of financial fields and other fields which is the portfolio optimization problem since it is considered one of the most crucial problems in the modern financial management and has a variety of applications such as asset management and building strategic asset allocation. The computational results were got utilizing benchmark data from the OR library with the use of modern optimization algorithms. In addition, the article highlights the differences and similarities among the utilized optimization methods. In addition, recent advancements to the utilized optimization methods are highlighted.

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“…Network is required for migration; hence CPU is used, but not to process genetic algorithm operations. GPU instead processes each individual in the subpopulations as [15], [16] and [17]. GPU, however, spends a lot of time to move individuals from host to device and vice versa; and this problem affects the resulting parallel genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Hou et al [16] built a parallel genetic algorithm that makes use of two parallel systems: multi-core CPU and many-core GPU. Furthermore, Li et al [17] also developed a parallel genetic algorithm that runs in GPU using island model. The last three studies, however, did not employ message passing interface to migrate the best individuals.…”

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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Parallel genetic algorithms on the graphics processing units using island model and simulated annealing

Cited by 19 publications

References 10 publications

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

On GPU Implementation of the Island Model Genetic Algorithm for Solving the Unequal Area Facility Layout Problem

On Tackling Real-Life Optimization Problems

A coarse-grained parallelization of genetic algorithms

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