Estimation of operating blast furnace reactor invisible interior surface using Differential Evolution

Bhattacharya, Arya K.; Aditya, Debjani; Sambasivam, Debjani

doi:10.1016/j.asoc.2012.10.010

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…At the basic algorithm level, Differential Evolution (DE) was formulated as an alternate approach to GA by Storn and Price [14]. The present authors have applied both GA and DE in a few complex industrial processes [15][16][17]; the latter work also provides a comparison in computational efficiency for that industrial process between GA and DE demonstrating that DE comes out favourably. Due to these developments the authors decided to use their version of DE as the baseline algorithm for the current multi-objective problem.…”

Section: A Differential Evolutionmentioning

confidence: 93%

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Parallel differential evolution approach for cloud workflow placements under simultaneous optimization of multiple objectives

Balouek-Thomert

Bhattacharya

Caron

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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The recent rapid expansion of Cloud computing facilities triggers an attendant challenge to facility providers and users for methods for optimal placement of workflows on distributed resources, under the oftencontradictory impulses of minimizing makespan, energy consumption, and other metrics. Evolutionary Optimization techniques that from theoretical principles are guaranteed to provide globally optimum solutions, are among the most powerful tools to achieve such optimal placements. Multi-Objective Evolutionary algorithms by design work upon contradictory objectives, gradually evolving across generations towards a converged Pareto front representing optimal decision variables-in this case the mapping of tasks to resources on clusters. However the computation time taken by such algorithms for convergence makes them prohibitive for real time placements because of the adverse impact on makespan. This work describes parallelization, on the same cluster, of a Multi-Objective Differential Evolution method (NSDE-2) for optimization of workflow placement, and the attendant speedups that bring the implicit accuracy of the method into the realm of practical utility. Experimental validation is performed on a real-life testbed using diverse Cloud traces. The solutions under different scheduling policies demonstrate significant reduction in energy consumption with some improvement in makespan.

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Section: A Differential Evolutionmentioning

confidence: 93%

“…The present work generates the mutant vector according to the alternate scheme (proposed in [14] and also used by current authors in [15][16][17] where it is found to work better than other DE variants)…”

Section: A Baseline Differential Evolutionmentioning

confidence: 99%