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What is Stochastic Optimization (SO)

Handbook of Research on Natural Computing for Optimization Problems
This specific optimization approach tends to produce and utilize indiscriminate variables and are so termed as Stochastic optimization (SO) techniques. Certain techniques in presence of the information data set which comprises specific dimensions; are very unique and thereby, commence sort of randomness into the investigation process to hasten the progression. Such arbitrariness tends to lead to a phenomena that occurs less susceptible to errors or fallacies caused due to protein modeling phenomena. Moreover, the injected arbitrariness might provide a phenomena to flee a local optimum and finally to draw near to a global optimum. Indeed, this randomization principle is soundly recognized to be a lucid and efficient approach to acquire algorithms with approximately certain good presentation consistently through several data sets, for varieties of problems. Simulated Annealing is one of such kinds of stochastic optimization methods.
Published in Chapter:
Adaptive Simulated Annealing Algorithm to Solve Bio-Molecular Optimization
Sujay Ray (University of Kalyani, India)
DOI: 10.4018/978-1-5225-0058-2.ch020
Abstract
Energy minimization is a paramount zone in the field of computational and structural biology for protein modeling. It helps in mending distorted geometries in the folded functional protein by moving its atoms to release internal constraints. It attempts to hold back to zero value for the net atomic force on every atom. But to overcome certain disadvantages in energy minimization, Simulated Annealing (SA) can be helpful. SA is a molecular dynamics technique, where temperature is gradually reduced during the simulation. It provides the best configuration of bio-molecules in shorter time. With the advancement in computational knowledge, one essential but less sensitive variant of SA: Adaptive Simulated Annealing (ASA) algorithm is beneficial, because it automatically adjusts the temperature scheme and abrupt opting of step. Therefore it benefits to prepare stable protein models and further to investigate protein-protein interactions. Thus, a residue-level study can be analyzed in details for the benefit of the entire biota.
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More Results
Evolutionary Computing to Examine Variation in Proteins with Evolution
Stochastic Optimization (SO) are the methods that optimizes by the production and utilization abrupt selected variables. When the data set comprises specific dimensions, certain specific techniques initiate abruptness into the exploration phenomena to aggravate the progress. Such abruptness can also prepare the technique to be less susceptible to fallacies during to modeling. Additionally, the inherent abruptness may be capable to allow the technique to leave a local optimum and subsequently to advance its step towards a global optimum. Undeniably, this randomization principle is studied to be a lucid and competent technique to study algorithms with specific good presentation uniquely throughout several information data, for varied problems.
Full Text Chapter Download: US $37.50 Add to Cart
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