Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods

Mucherino, Antonio; Şeref, Onur

doi:10.1007/978-0-387-75181-8_19

Cited by 12 publications

(9 citation statements)

References 38 publications

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“…The relationships b ( T , P ) and n ( T ) can be expressed by the following empirical models and described in terms of these equations: where C 0 , C 1 , C 2 , C 3 , C 4 , and C 5 are constant coefficients calculated with the Simulated Annealing Algorithm (Mucherino and Seref 2008).…”

Section: Resultsmentioning

confidence: 99%

“…where C 0 , C 1 , C 2 , C 3 , C 4 , and C 5 are constant coefficients calculated with the Simulated Annealing Algorithm (Mucherino and Seref 2008). Combining the contribution of pressure and temperature on the b parameter and the contribution of temperature on n parameter, the empirical model obtained for the inactivation of microbiota in apple juice in our experiments could be written as: log 10 S (t) = − log e [−1.662 + 4.446 × exp (KT × (T − T C )) − 0.887 · exp (K P × (P − P C ))] × t (−1.6 × 10 −5 × T 2 + 0.006 × T − 0.015) (19) Table 3, 4, and 5 report the fitted values of the survivor numbers in terms of log 10 reduction, the observed values and the residuals (observed value -fitted value) for all the treatment conditions.…”

Section: Resultsmentioning

confidence: 99%

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Microbial Inactivation Kinetics during High‐Pressure Carbon Dioxide Treatment: Nonlinear Model for the Combined Effect of Temperature and Pressure in Apple Juice

Ferrentino

Ferrari

Poletto

et al. 2008

Journal of Food Science

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Isobaric and isothermal semi-logarithmic survival curves of natural microflora in apple juice treated with high-pressure carbon dioxide at 7, 13, and 16 MPa pressures and 35, 50, and 60 degrees C temperatures were fitted with a nonlinear equation to find the values of the coefficient b(P), b(T), n(P), and n(T). Profiles of the model parameters were obtained as a function of pressure and temperature. The model fitted with good agreement(R(2) > 0.945), the survival curves. An empirical equation was proposed to describe the combined effects of pressure and temperature. The equation, derived from a power law model, was written in the form: log(10) S(t) = -log(e) [C(0)+C(1) x exp (K(T) x (T-T(C))-C(2) x exp (K(P) x (P-P(C))) x t (C(3)xT(2)+C(4)xT+C(5)). The proposed model fitted the experimental data well. At 7 MPa and 50 and 60 degrees C, 13 MPa and 35 and 60 degrees C, 16 MPa and 35 degrees C, the model provided (log)10 reduction residual values (observed value-fitted value) lower than 0.284 showing a good agreement between the experimental and the predicted survival levels.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Microbial Inactivation Kinetics during High‐Pressure Carbon Dioxide Treatment: Nonlinear Model for the Combined Effect of Temperature and Pressure in Apple Juice

Ferrentino

Ferrari

Poletto

et al. 2008

Journal of Food Science

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“…The metaheuristic optimization techniques have different advantages makes them the first choice for solving optimization problems. Their simplicity comes from being reveal of natural phenomena, animals behaviors, or evolutionary concepts [4]. Moreover they can be apply simply to different optimization problems without any change in algorithm structure, and they are the most suitable optimization techniques for real problems.…”

Section: Introductionmentioning

confidence: 99%

Design static VAR compensator controller using artificial neural network optimized by modify Grey Wolf Optimization

Mohamed

El-Gaafary

Mossa

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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This paper introduce a novel design of the static V AR compensator (SVC) controller for damping power system oscillations. A multi layer neural network model tuned by Grey Wolf Optimization algorithm (GWO) is investigated and presented. GWO search algorithm is used to optimized all the connection of weights and biases for the artificial neural network. The proposed approach depends up on the expected wide range of the effective operating conditions of the SVc. Modification is introduced in the proposed optimizer exploration-exploitation balance to enhance its rate of convergence over the original algorithm. The robustness of the proposed controller successfully testing for damping oscillations of two-axis nonlinear single machine infinite bus system. A comparative study for the controller based the classical PI controller have been presented.

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“…On the other hand, in the recent years, heuristic methods based on the natural phenomena of evolution, such as simulated annealing algorithm, genetic algorithms, differential evolution, harmony or tabu search ideas, swarm-inspired methods, etc. have been developed and applied to model and solve real-life global optimization problems [3]. Furthermore, this kind of optimization algorithms has long been applied for tuning values of unknown parameters of different types of models [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Concept of Automatic Tuning of Longwall Scraper Conveyor Model

Przystałka

Katunin

2016

Annals of Computer Science and Information Systems

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Abstract-The modeling of machines and their operation modes is a key approach for optimization of their performance as well as for avoiding unwanted operational states which may lead to the occurrence of faults, and finally, to the breakdown. The developed model of a machine should be always parametrized, i.e. the certain number of parameters should be selected in the certain ranges. The most of the parameters can be selected based on engineering documentation and experts' knowledge, however, for some of them this knowledge cannot be directly acquired which leads to the parameter uncertainty. One of the approaches allowing selection of these uncertain parameters is a tuning procedure of a model. The paper deals with a concept of heuristic optimization method for automatic tuning of key parameters of longwall scraper conveyor model. In the first part of the paper, the evolutionary algorithm for tuning this model is proposed. In the case study, the merits and limitations of the evolutionary approach are analysed. The obtained results prove that the proposed approach of tuning of the considered model has high practical potential and it may be applied in real mining conditions.

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Modeling and Solving Real-Life Global Optimization Problems with Meta-heuristic Methods

Cited by 12 publications

References 38 publications

Microbial Inactivation Kinetics during High‐Pressure Carbon Dioxide Treatment: Nonlinear Model for the Combined Effect of Temperature and Pressure in Apple Juice

Microbial Inactivation Kinetics during High‐Pressure Carbon Dioxide Treatment: Nonlinear Model for the Combined Effect of Temperature and Pressure in Apple Juice

Design static VAR compensator controller using artificial neural network optimized by modify Grey Wolf Optimization

A Concept of Automatic Tuning of Longwall Scraper Conveyor Model

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