Multi-Core Parallel Particle Swarm Optimization for the Operation of Inter-Basin Water Transfer-Supply Systems

Peng, Yong; Peng, Anbang; Zhang, Xiaoli; Zhou, Huicheng; Zhang, Lin; Wang, Wenzhong; Zhang, Zixin

doi:10.1007/s11269-016-1506-4

Cited by 31 publications

(4 citation statements)

References 26 publications

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“…One way to profit from such powerful hardware is to run multiple swarms simultaneously. This kind of implementation can be seen in [24,47,[47][48][49][50][51][52][53][54][55][56]. SI algorithms have a non-deterministic behavior, therefore each run typically produces different results.…”

Section: Parallel and Cooperative Swarmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

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Section: Parallel and Cooperative Swarmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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show abstract

“…In line with this, in the current study we present a hybrid approach using a combination of the PSOA and GSA in order to improve optimization accuracy. The PSOA has many applications in the fields of water engineering and the optimization of reservoir operations (Peng et al 2017;Bai et al 2017;Li and Zeng 2017;Feng et al 2017). This new hybrid algorithm has a number of strengths, as follows:…”

Section: Limitations and Noveltymentioning

confidence: 99%

Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies

Karami

Farzin

Jahangiri

et al. 2019

Water Resour Manage

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The growing prevalence of droughts and water scarcity have increased the importance of operating dam and reservoir systems efficiently. Several methods based on algorithms have been developed in recent years in a bid to optimize water release operation policy, in order to overcome or minimize the impact of droughts. However, all of these algorithms suffer from some weaknesses or drawbacksnotably early convergence, a low rate of convergence, or trapping in local optimizationsthat limit their effectiveness and efficiency in seeking to determine the global optima for the operation of water systems. Against this background, the present study seeks to introduce and test a Hybrid Algorithm (HA) which integrates the Gravitational Search Algorithm (GSA) with the Particle Swarm Optimization Algorithm (PSOA) with the goal of minimizing irrigation deficiencies in a multi-reservoir system. The proposed algorithm was tested for a specific important multi-reservoir system in Iran: namely the Golestan Dam and Voshmgir Dam system. The results showed that applying the HA could reduce average irrigation deficiencies for the Golestan Dam substantially, to only 2 million cubic meters (MCM), compared to deficiency values for the Genetic Algorithm (GA), PSOA and GSA of 15.1, 6.7 and 5.8 MCM respectively. In addition, the HA performed very efficiently, reducing substantially the computational time needed to achieve the global optimal when compared with the other algorithms tested. Furthermore, the HA showed itself capable of assuring a high volumetric reliability index (VRI) to meet the pattern of water demand downstream from the dams, as well as clearly outperforming the other algorithms on other important indices. In conclusion, the proposed HA seems to offer considerable potential as an optimizer for dam and reservoir operations world-wide.

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“…This hybrid approach improves the BA's capabilities. The PSOA has been highly relevant to WRM issues [19][20][21]. The structure of the proposed hybrid algorithm (HA) is such that both algorithms (PSOA and BA) run in parallel and independently.…”

Section: Problem Statementmentioning

confidence: 99%

Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

et al. 2019

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One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.

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Multi-Core Parallel Particle Swarm Optimization for the Operation of Inter-Basin Water Transfer-Supply Systems

Cited by 31 publications

References 26 publications

Parallelization of Swarm Intelligence Algorithms: Literature Review

Parallelization of Swarm Intelligence Algorithms: Literature Review

Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies

Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

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