Fast Hybrid BSA-DE-SA Algorithm on GPU

Brevilliers, Mathieu; Abdelkafi, Omar; Lepagnot, Julien; Idoumghar, Lhassane

doi:10.1007/978-3-319-50307-3_6

Cited by 2 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the same context, population based metaheuristics have been widely exploited to solve high dimensional continuous problems. Besides iteration level, in most of works, as [16,36,42,49,[51][52][53], dimensions of individuals are computed in parallel (solution level), which increases the acceleration of the algorithms. Quality has been preserved, and even improved as it is the case in [36,42,44,49,90].…”

Section: Discussionmentioning

confidence: 99%

“…Finally, the common point of the works in designing a GPU based algorithm is trying to maximize data parallelism. Works like [16,21,42,50,51] represent solutions as thread blocks where threads evaluate dimensions, or perform specific operators, such as crossover, mutation or to generate the neighborhood. Moreover, GPU impact becomes clear when big instances are addressed.…”

Section: Discussionmentioning

confidence: 99%

“…A hybridization between DE and Backtracking Search Optimization Algorithm (BSA) and Simulated Annealing (SA) has been carried out in [16]. The algorithm consists mainly in two stages.…”

Section: Differential Evolution (De)mentioning

confidence: 99%

See 2 more Smart Citations

GPU parallelization strategies for metaheuristics: a survey

Essaid

Idoumghar

Lepagnot

et al. 2018

International Journal of Parallel, Emergent and Distributed Sys

Self Cite

View full text Add to dashboard Cite

Metaheuristics have been showing interesting results in solving hard optimization problems. However, they become limited in terms of effectiveness and runtime for high dimensional problems. Thanks to the independency of metaheuristics components, parallel computing appears as an attractive choice to reduce the execution time and to improve solution quality. By exploiting the increasing performance and programability of graphics processing units (GPUs) to this aim, GPU-based parallel metaheuristics have been implemented using different designs. Recent results in this area show that GPUs tend to be effective co-processors for leveraging complex optimization problems. In this survey, mechanisms involved in GPU programming for implementing parallel metaheuristics are presented and discussed through a study of relevant research papers.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

GPU parallelization strategies for metaheuristics: a survey

Essaid

Idoumghar

Lepagnot

et al. 2018

International Journal of Parallel, Emergent and Distributed Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is proposed to solve constrained optimization problems and overcome some drawbacks of the previous evolutionary algorithms: e.g., high sensitivity to the control parameter and time-consuming computation [12]. BSA's structure is simple; it has a powerful global exploration and local exploitation due to TELKOMNIKA Telecommun Comput El Control  Improved backtracking search optimization algorithm for PV/Wind/FC system (Jihane Kartite) 459 controlling the search direction by scale factor parameter [13,14]. Figures 2 (a) and (b) shows power as a function of current and the voltage as a function of current for the FC used in this study.…”

Section: Back-tracking Search Optimization Algorithm (Bsa)mentioning

confidence: 99%

“…In each iteration, BSA determine Old P by comparing two numbers generated randomly a and b. This comparison is shown in (13):…”

Section: Back-tracking Search Optimization Algorithm (Bsa)mentioning

confidence: 99%

Improved backtracking search optimization algorithm for PV/Wind/FC system

Kartite

Cherkaoui

2020

TELKOMNIKA

View full text Add to dashboard Cite

This paper uses a novel optimization method based on the improved backtracking search optimization algorithm (IBSA). The study is conducted for a hybrid stand-alone system composed of photovoltaic panel (PV), wind turbine generator and fuel cell electrolyzer (FC). To demonstrate the effectiveness of the IBSA, four benchmark functions are used. The result shows the better exploration and exploitation of the improved backtracking search optimization algorithm in terms of convergence and speed for system comprinsing PV panel wind, turbine generator and fuel cell. The proposed algorithm is used to optimize the annual total cost (ATC) of the energy produced and feed up the load demand. The economic evaluation of the Hybrid PV/Wind/FC system is done throughout hourly demand and daily wind speed and insulation. The simulation results justify the robustness of the IBSA.

show abstract

Fast Hybrid BSA-DE-SA Algorithm on GPU

Cited by 2 publications

References 16 publications

GPU parallelization strategies for metaheuristics: a survey

GPU parallelization strategies for metaheuristics: a survey

Improved backtracking search optimization algorithm for PV/Wind/FC system

Contact Info

Product

Resources

About