Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system

Umbarkar, A. J.; Joshi, Madhuri S.; Hong, Wei‐Chiang

doi:10.1016/j.amc.2014.06.033

Cited by 16 publications

(11 citation statements)

References 16 publications

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“…Umbarkar et al [22] proposed the binary-encoded multithreaded parallel DPGA (MPDPGA) which solves the problems of population diversity and premature convergence on multicore system. Experiments show that the performance of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).…”

Section: Methodsmentioning

confidence: 99%

Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review

Umbarkar¹,

Joshi

Sheth³

2014

Advances in Intelligent Systems and Computing

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Maintaining population diversity is a challenge for the success of genetic algorithm. A numerous approaches have been proposed by researchers for adding diversity to the population. Dual-population genetic algorithm (DPGA) is one of them which is an effective optimization algorithm and provides diversity to the main population. Problems in GA such as premature convergence and population diversity is well addressed by DPGA. The aim of writing this review paper is to study how DPGA has been evolved. DPGA is inherently parallelizable, and hence, it can be port to parallel programming architecture for large-scale or large-dimension problems.

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Section: Methodsmentioning

confidence: 99%

Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review

Umbarkar¹,

Joshi

Sheth³

2014

Advances in Intelligent Systems and Computing

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“…Some works implemented large algorithms in parallel such as Genetic [25], PSO [26], Tabu search algorithm [27] and more.…”

Section: Discussion and Analysismentioning

confidence: 99%

A Survey on Parallel Multicore Computing: Performance & Improvement

Surakhi¹,

Khanafseh²,

Sarhan³

2018

Adv. sci. technol. eng. syst. j.

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“…Umbarkara, M.S. Joshib, and Wei-Chiang Hongd proposed multithreaded implementation of Dual Population Genetic Algorithm in Java [4]. Efficient speedup of the solution was claimed, but the algorithm was tested only on single, dual core CPU.…”

Section: A Parallel Algorithms Testingmentioning

confidence: 99%

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

Malinowski¹

2015

IJITCS

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Abstract-Parallel algorithms are popular method of increasing system performance. Apart from showing their properties using asymptotic analysis, proof-of-concept implementation and practical experiments are often required. In order to speed up the development and provide simple and easily accessible testing environment that enables execution of reliable experiments, the paper proposes a platform with multi-core computational accelerator: Intel Xeon Phi, and modern programming language: Java. The article includes the description of integration Java with Xeon Phi, as well as detailed information about all of the software components. Finally, the set of tests proves, that proposed platform is able to prepare reliable experiments of parallel algorithms implemented in modern programming language.

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Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system

Cited by 16 publications

References 16 publications

Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review

Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review

A Survey on Parallel Multicore Computing: Performance & Improvement

Modern Platform for Parallel Algorithms Testing: Java on Intel Xeon Phi

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