Optimization of Oscillation Parameters in Continuous Casting Process of Steel Manufacturing: Genetic Algorithms versus Differential Evolution

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

“…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.…”

“…Differential Evolution (DE) belongs to the broad class of evolutionary optimization techniques that developed as distinctive variants of classical Genetic Algorithms (GA). DE was selected as the evolutionary method of choice on the basis of the authors' prior studies on the relative efficiency and merits of this against GA, as reported in [17].…”

“…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)…”