Automatic termination of parallel optimization runs of stochastic global optimization methods in consensus or stagnation cases

Sulins, Jurijs; Mednis, Martins

doi:10.11592/bit.120501

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Global stochastic optimization methods are often used to solve both kind of tasks (Banga, 2008;Rodriguez-Fernandez et al, 2006). Some disadvantages of global stochastic optimization methods are 1) the hardly predictable duration of optimization (Mozga and Stalidzans, 2011a;Nikolaev, 2010) and 2) possible stagnation in local optima (Mozga and Stalidzans, 2011b;Sulins and Mednis, 2012).…”

Section: Introductionmentioning

confidence: 99%

Determinition of best set of adjustable parameters with full search and limited search methods

Mozga

2013

Bit-Journal

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In case of optimization of biochemical networks the best combination of adjustable parameters has to be found keeping low the costs of modification of biochemical network and reducing the risk of unpredicted side effects of processes outside the scope of the model. Dynamic models of biochemical networks usually are in form of system of differential equations and therefore can not be optimized analytically. Usually they are optimized using computationally demanding global stochastic optimization methods which may have hardly predictable duration of optimization. Optimization of all the possible combinations is necessary to find the best set of adjustable parameters per number of parameters in combination. Due to the combinatorial explosion the full search often is not feasible approach if computational and time resources are limited. In this study the performance of full search is compared with forward selection and three metabolic control based methods which need significantly less combinations to be examined. It is found that forward selection is a good compromise in case of larger number of adjustable parameters where the number of possible combinations exceeds the available resources needed for full search in the adjustable parameter space. It is proposed to combine full search for small number of adjustable parameters with forward selection at larger number of adjustable parameters per combination.

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Section: Introductionmentioning

confidence: 99%

Determinition of best set of adjustable parameters with full search and limited search methods

Mozga

2013

Bit-Journal

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Two stage optimization of biochemical pathways using parallel runs of global stochastic optimization methods

Stalidzāns

Kostromins

Sulins

2012

2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI)

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Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network

Stalidzāns

Mozga

Sulins

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Selecting an efficient small set of adjustable parameters to improve metabolic features of an organism is important for a reduction of implementation costs and risks of unpredicted side effects. In practice, to avoid the analysis of a huge combinatorial space for the possible sets of adjustable parameters, experience-, and intuition-based subsets of parameters are often chosen, possibly leaving some interesting counter-intuitive combinations of parameters unrevealed. The combinatorial scan of possible adjustable parameter combinations at the model optimization level is possible; however, the number of analyzed combinations is still limited. The total optimization potential (TOP) approach is proposed to assess the full potential for increasing the value of the objective function by optimizing all possible adjustable parameters. This seemingly unpractical combination of adjustable parameters allows assessing the maximum attainable value of the objective function and stopping the combinatorial space scanning when the desired fraction of TOP is reached and any further increase in the number of adjustable parameters cannot bring any reasonable improvement. The relation between the number of adjustable parameters and the reachable fraction of TOP is a valuable guideline in choosing a rational solution for industrial implementation. The TOP approach is demonstrated on the basis of two case studies.

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Automatic termination of parallel optimization runs of stochastic global optimization methods in consensus or stagnation cases

Abstract: Dynamic models give detailed information about the influence of many parameters on the behaviour of the biochemical process of interest. Parameter optimization of dynamic models is used in parameter estimation tasks and in design tasks

Cited by 4 publications

References 19 publications

Determinition of best set of adjustable parameters with full search and limited search methods

Determinition of best set of adjustable parameters with full search and limited search methods

Two stage optimization of biochemical pathways using parallel runs of global stochastic optimization methods

Search for a Minimal Set of Parameters by Assessing the Total Optimization Potential for a Dynamic Model of a Biochemical Network

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