Adapting MapReduce framework for genetic algorithm with large population

Khalid, Norlin; Fadzil, Ahmad Firdaus Ahmad; Manaf, Mazani

doi:10.1109/spc.2013.6735099

Cited by 7 publications

(10 citation statements)

References 19 publications

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“…MRPSO is introduced to avoid problems arisen from large-scale parallelization. Khalid et al [13] introduced the MRGA algorithm. They propose and evaluate the performance of GA using MapReduce (MR).…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Service Composition based on Hybrid Bio-Inspired Cloud-based QoS Provisioning Approach.

Bahgat¹,

Salam²,

Atwan³

et al. 2020

Preprint

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It has recently become a critical issue to provide software development in a service-based conceptual style for business companies . As a powerful technology for service-oriented computing, the composition of web services is investigated. This offered great opportunities to improve IT industries and business processes by forming new value-added services that satisfy the user’s complex requirements. Unfortunately, many challenges are facing the service composition process. These include the difficulties to satisfy the user’s complex demands, maintaining the performance to be matched with the quality of service (QoS) requirements, and search space reduction for QoS missing or changeable values. Accordingly, this paper proposes a cloud-based QoS provisioning service composition (CQPC) framework to address these challenges. To prove the concept and the applicability of the CQPC framework, a Hybrid Bio-Inspired QoS provisioning (HBIQP) technique is presented for the operation of the CQPC framework modules. The solution space is reduced via utilizing skyline concepts to have faster execution time and keep only reliable and most interesting services. The CQPC framework is equipped with two proposed algorithms: (i) the modified highly accurate prediction (MHAP) algorithm to enhance the prediction of QoS values of the services participating in the composition process, (ii) the MapReduce fruit fly Particle swarm Optimization (MR-FPSO) algorithm to handle composing web services for large scale of data in the cloud environment. The experimental results demonstrate the worthiness of the HBIQP technique to meet the performance metrics more than other state-of-the-art techniques in terms of average fitness value, accuracy, and execution time.

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Section: Literature Reviewmentioning

confidence: 99%

Towards Service Composition based on Hybrid Bio-Inspired Cloud-based QoS Provisioning Approach.

Bahgat¹,

Salam²,

Atwan³

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…We implemented the following MapReduce‐ based optimization algorithms: MR‐PSO, MR‐ABC, MR‐GA, MR‐IDPSO, and MR‐EA/G and compared the results with our MR‐MGWO. In our experiments, the population size is fixed to 30 and each experiment was executed steadily for 30 times, and mean is considered for each run.…”

Section: Performance Evaluationmentioning

confidence: 99%

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Bhaskar

Jatoth

Gangadharan

et al. 2019

Concurrency and Computation

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Summary Big services are the collection of interrelated web services across virtual and physical domains, integrating service oriented computing and big data. The rapid growth of Big services that offer similar functionality with varying QoS attributes makes the process of selection and composition of these big services as highly challenging and complex. In this paper, we develop an efficient QoS‐aware Big service composition approach by applying a MapReduce based Modified Grey Wolf Optimizer (MR‐MGWO) that explores more search space, especially in a multidimensional environment. Our approach ensures an optimal balance of exploration and exploitation that enhances the convergence rate and minimizes the computational time. The empirical analysis illustrates that the performance of MR‐MGWO is superior to other similar approaches for solving Big service composition.

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“…However, when the problem size increased, SGA solution time increased, and MapReduce has almost the same run-time for all problem sizes. Khalid et al, [3] proposed a MapReduce framework implementation for GA with a large population using island parallelization technique. TSP was used as a case study to test the algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In general, there are three main models of parallel GAs: masterslave model, fine-grained model and coarse-grained also called island model. The island model is a popular and effective parallel genetic algorithm because it does not only save time but also improves global research ability of GA [3]. Recently, the increasing volume of data requires high-performance parallel processing models for robust and speedy data analysis.…”

Section: Introductionmentioning

confidence: 99%

Hadoop MapReduce for Parallel Genetic Algorithm to Solve Traveling Salesman Problem

Alanzi¹,

Bennaceur²

2019

IJACSA

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Achieving an optimal solution for NP-complete problems is a big challenge nowadays. The paper deals with the Traveling Salesman Problem (TSP) one of the most important combinatorial optimization problems in this class. We investigated the Parallel Genetic Algorithm to solve TSP. We proposed a general platform based on Hadoop MapReduce approach for implementing parallel genetic algorithms. Two versions of parallel genetic algorithms (PGA) are implemented, a Parallel Genetic Algorithm with Islands Model (IPGA) and a new model named an Elite Parallel Genetic Algorithm using MapReduce (EPGA) which improve the population diversity of the IPGA. The two PGAs and the sequential version of the algorithm (SGA) were compared in terms of quality of solutions, execution time, speedup and Hadoop overhead. The experimental study revealed that both PGA models outperform the SGA in terms of execution time, solution quality when the problem size is increased. The computational results show that the EPGA model outperforms the IPGA in term of solution quality with almost similar running time for all the considered datasets and clusters. Genetic Algorithms with MapReduce platform provide better performance for solving large-scale problems.

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Adapting MapReduce framework for genetic algorithm with large population

Cited by 7 publications

References 19 publications

Towards Service Composition based on Hybrid Bio-Inspired Cloud-based QoS Provisioning Approach.

Towards Service Composition based on Hybrid Bio-Inspired Cloud-based QoS Provisioning Approach.

A MapReduce‐based modified Grey Wolf optimizer for QoS‐aware big service composition

Hadoop MapReduce for Parallel Genetic Algorithm to Solve Traveling Salesman Problem

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