• Journal of applied mathematics & informatics
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• v.26 no.3_4
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• pp.811-818
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• 2008

Biological systems with both protein-protein and protein-gene interactions can be modeled by differential equations for concentrations of the proteins with time-delay terms because of the time needed for DNA transcription to mRNA and translation of mRNA to protein. Values of some parameters in the mathematical model can not be measured owing to the difficulty of experiments. Also values of some parameters obtained in a normal stress condition can be changed under pathological stress stimuli. Thus it is important to find the effective way of determining parameters values. One approach is to use optimization algorithms. Here we construct an optimal system used to find optimal parameters in the equations with nonnegative time delays and apply this optimization result to the Nuclear factor-${\kappa}B$ pathway.

• Smart Structures and Systems
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• v.22 no.4
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• pp.469-479
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• 2018

The present research study utilizes a multi-objective optimization method for Pareto optimization of an eight-degree of freedom full vehicle vibration model, adopting a non-dominated sorting genetic algorithm II (NSGA-II). In this research, a full set of ride comfort as well as ride safety parameters are considered as objective functions. These objective functions are divided in to two groups (ride comfort group and ride safety group) where the ones in one group are in conflict with those in the other. Also, in this research, a special optimizing technique and combinational method consisting of weighted sum method and Pareto optimization are applied to transform Pareto double-objective optimization to Pareto full-objective optimization which can simultaneously minimize all objectives. Using this technique, the full set of ride parameters of three dimensional vehicle model are minimizing simultaneously. In derived Pareto front, unique trade-off design points can selected which are non-dominated solutions of optimizing the weighted sum comfort parameters versus weighted sum safety parameters. The comparison of the obtained results with those reported in the literature, demonstrates the distinction and comprehensiveness of the results arrived in the present study.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.4
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• pp.434-443
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• 2014

A turbo fan engine performance analysis and the optimization using particle swarm optimization(PSO) algorithm have been conducted to investigate the effects of major performance design parameters of an aircraft gas turbine engine. The FJ44-2C turbofan engine, which is widely used in the small business jet, CJ2 has been selected as the basic model. The design parameters consists of the bypass ratio, burner exit temperature, HP compressor ratio, fan inlet mass flow, and nozzle cooling air ratio. The sensitivity analysis of the parameters has been evaluated and the optimization of the parameters has been performed to achieve high net thrust or low specific fuel consumption.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.5
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• pp.121-128
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• 2012

The optimization of cooling system parameters for designing injection mold is very important to acquire the highest part quality. In this paper, the integration of computer simulations of injection molding and Analysis of Variance(ANOVA) with orthogonal array was used as a design tool to optimize the cooling system parameters aimed at minimizing the part warpage. The design optimizer was applied to find the optimum levels of cooling system parameters for a dustpan. This optimization resulted in more uniform temperature distribution over the part and significant reduction of a part warpage, showing the capability of present method as an effective design tool. The whole optimization process was performed systematically in a proper number of cooling simulations. The design optimizer can be utilized effectively in the industry practice for designing mold cooling system with less cost and time.

• Structural Engineering and Mechanics
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• v.65 no.6
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• pp.761-769
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• 2018

In this work, an optimization method of Friction Tuned Mass Damper (FTMD) parameters is presented. Friction tuned mass dampers (FTMD) are attached to mechanical structures to reduce their vibrations with dissipating the vibratory energy through friction between both bodies. In order to exploit the performances of FTMD, the determination of the optimum parameters is recommended. However, the presence of Coulomb's friction force requires the resolution of a non-linear stick-slip problem. First, this work aims at determining the responses of the vibratory system. The responses of the main mass and of the FTMD are determined analytically in the sticking and sliding phase using the equivalent damping method. Second, this work aims to optimize the FTMD parameters; the friction coefficient and the tuned frequency. The optimization formulation based on the Ricciardelli and Vickery method at the resonance frequencies, this method is reformulated for a system with a viscous damping. The inverse problem of finding the FTMD parameters given the magnitude of the force and the maximum acceptable displacement of the primary system is also considered; the optimization of parameters leads to conclude on the favorable FTMD giving significant vibration decrease, and to advance design recommendations.

• Smart Structures and Systems
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• v.32 no.6
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• pp.383-391
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• 2023

Energy harvesting in trams may become a prevalent source of passive energy generation due to the high density of vibrational energy, and this may help power structural health monitoring systems for the trams. This paper presents a broadband vibrational energy harvesting device design that utilizes a varied frequency from a tram vehicle using a 2 DOF vibrational system combined with electromagnetic energy conversion. This paper will demonstrate stepwise optimization processes to determine mechanical parameters for frequency tuning to adjust to the trams' operational conditions, and electromagnetic parameters for the whole system design to maximize power output. The initial optimization will determine 5 important design parameters in a 2 DOF vibrational system, namely the masses (m₁, m₂ (and spring constants (k₁, k₂, k₃). The second step will use these parameters as initial guesses for the second optimization which will maintain the ratios of these parameters and present electrical parameters to maximize the power output from this system. The obtained values indicated a successful demonstration of design optimization as the average power generated increased from 1.475 mW to 17.44 mW (around 12 times).

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1462-1469
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• 1997

This study presents a genetic algorithm-based method for optimizing control parameters in fluid power systems. Genetic algorithms are general-purpose optimization methods based on natural evolution and genetics. A genetic algorithm seeks control parameters maximizing a measure that evaluates system performance. Five control gains of the PID-PD cascade controller fr an electrohydraulic speed control system with a variable displacement hydraulic motor are optimized using a genetic algorithm in the experiment. Optimized gains are confirmed by inspecting the fitness distribution which represents system performance in gain spaces. It is shown that optimization of the five gains by manual tuning should be a task of great difficulty and that a genetic algorithm is an efficient scheme giving economy of time and in labor in optimizing control parameters of fluid power systems.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.2
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• pp.88-95
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• 2008

With society focusing more and more on environmental issues, the recycling of materials of all types has become an important concern. In this paper, optimization method is developed for reducing cost and improving quality in mill scale recycling. An experimental investigation into the process parameter effects is presented to determine the optimum configuration of parameters for performance, quality and cost. Taguchi's optimization approach was used to obtain the optimal parameters. The significant parameters were identified and their effects on mill scale recycling were studied. As a results, a confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the Taguchi method.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.71-75
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• 1994

This paper presents a method of optimal design of an automatic transfer system which is controlled by the electro-pneumatic servo scheme. The electro-pneumatic automatic transfer system can move parts to desired points or displace defective parts. The dynamic performance of the system can be examined by observing the behavior of the output. The output of the servo control system is the motion of the cylinder, pneumatic actuator. The dynamic performance of the cylinder is governed by the parameters of the components of the entire system. The optimal design can be accomplished by selecting of the parameters such that the desired dynamic performance of the cylinder is obtained. The optimal set of parameters might be obtained through the repeated simulations. Repeated simulations, however, is not effective to determine the optimal set of parameters since the set of parameters is large. This paper presents modeling, application of an optimization method, and the numerical results. The optimization algorithm utilizes the concept of the conjugate gradient method. The results show that the suggested optimization scheme can render faster convergence of iteration compared to other method based on an algebraic optimization method and can reduce the design efforts.

• Structural Engineering and Mechanics
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• v.70 no.1
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• pp.81-96
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• 2019

In this paper, a hybrid uncertain model is applied to system reliability based design optimization (RBDO) of trusses. All random variables are described by random distributions but some key distribution parameters of them which lack information are defined by variation intervals. For system RBDO of trusses, the first order reliability method, as well as monotonicity analysis and the branch and bound method, are utilized to determine the system failure probability; and Improved (${\mu}+{\lambda}$) constrained differential evolution (ICDE) is employed for the optimization process. System reliability assessment of several numerical examples and system RBDO of different truss structures are proposed to verify our results. Moreover, the effect of different classes of interval distribution parameters on the optimum weight of the structure and the reliability index are also investigated. The results indicate that the weight of the structure is increased by increasing the uncertainty level. Moreover, it is shown that for a certain random variable, the optimum weight is more increased by the translation interval parameters than the rotation ones.