Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

Rico-Garcia, Hector; Sánchez-Romero, José-Luis; Jimeno-Morenilla, Antonio; Migallón, Héctor; Mora, Higinio; Rao, R. Venkata

doi:10.1109/access.2019.2941086

Cited by 10 publications

(7 citation statements)

References 34 publications

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“…In the learner phase, each individual strives to learn via contact with other individuals within the population. The following is the TLBO algorithm's behaviour [48]. Random data is used to build the population and initialize the individuals (values of the design variables).…”

Section: B Teaching Learning Based Optimizationmentioning

confidence: 99%

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Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

et al. 2022

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A smart energy management controller can improve energy efficiency, save energy costs, and reduce carbon emissions and energy consumption while accurately catering to consumer consumption habits. Having integrated various renewable energy systems (RESs) and a battery storage system (BSS), we proposed an optimization-based demand-side management (DSM) scheduler and energy management controller (SEMC) for a smart home. The suggested SEMC creates a DSM-based operational plan regarding user-centered and comfort-aware preferences. Using the generated appliances operation plan, consumers can reduce energy costs, carbon emissions, peak-to-average ratio (PAR), improve their comfort in terms of thermal, illumination, and appliances usage preferences. A schedule for residential consumers is suggested using ant colony optimization (ACO), teaching learning-based optimization (TLBO), Jaya algorithm, rainfall algorithm, firefly algorithm, and our hybrid ACO and TLBO optimization (ACTLBO) algorithm. Five existing algorithms-based frameworks validate the DSM framework that relies on ACTLBO. The results validate that the integration of RESs and BSS, and adapting our proposed algorithm and SEMC under demand response program real-time price reduced the energy bill costs, PAR and CO 2 in Case I: only external grid (EG) usage by 42.14%, 22.05%, and 28.33%, in Case II: EG with RESs by 21.79%, 11.27%, 17.02%, and in Case III: EG with RESs and BSS by 28.76%, 41.53%, 21.86%, respectively as compared to without employing SEMC. Moreover, the user comfort improvement index-ratio with scheduling using ACTLBO is 7.77%, 24.73%, 5.00%, and 3.43% in terms of average delay, indoor air quality, thermal, and visual, respectively. Simulation results show that the proposed DSM-based framework outperforms existing frameworks to reduce energy bill costs, reduce carbon emissions, mitigate peak loads, and improve user comfort.INDEX TERMS energy management controller, user-comfort, demand shifting, load scheduling, battery storage systems, demand response, solar energy, smart grid. NOMENCLATURE

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Section: B Teaching Learning Based Optimizationmentioning

confidence: 99%

“…These numbers are employed in the algorithm's two primary stages: the teacher and learner phases. In the teacher phase, the current generation has acquired teacher, X best is utilized to build a new version of each person X new using the equation [48]:…”

Section: B Teaching Learning Based Optimizationmentioning

confidence: 99%

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

et al. 2022

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“…Similar works in [36,37] used GPU and FPGA to accelerate genetic algorithm (GA). What deserves attention is that Garcia et al [38] achieved parallel implementation and comparison of teaching-learning based optimization (TLBO) and Jaya on many-core GPU. As for WOA, Khalil et al [39] proposed a simple and robust distributed WOA using Hadoop Map-Reduce, reaching a promising speedup.…”

Section: Related Workmentioning

confidence: 99%

Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator

et al. 2020

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Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.

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“…Authors used unconstrained benchmark functions to test the proposed approach. Authors also analysed the utilization of GPUs by each approach [48]. García-Monzó et al developed a shared memory-based and message-passing based parallel TLBO algorithm.…”

Section: Related Workmentioning

confidence: 99%

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Mane

Adamuthe

Omane³

2018

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system's computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.

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Comparison of High Performance Parallel Implementations of TLBO and Jaya Optimization Methods on Manycore GPU

Cited by 10 publications

References 34 publications

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

Demand Response Program for Efficient Demand-Side Management in Smart Grid Considering Renewable Energy Sources

Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

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