• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.679-682
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• 2003

In this paper, we propose a Genetic Algorithm (GA) using symbiotic evolutionary viruses. Our GA is based on both the building block hypothesis and the virus theory of evolution. The proposed GA aims to control a destruction of building blocks by discovering, keeping, and propagating of building blocks based on virus operation. Concretely, we prepare the group of individuals and the group of viruses. In our GA, the group of individuals searches solutions and the group of viruses searches building blocks. These searches done based on the symbiotic relation of both groups. Also, our GA has two types of virus evaluation techniques. One is that each virus is evaluated by the difference of the fitness of an individual between before and after infection of virus. Another is that all viruses aye evaluated by the difference of the fitness of an individual between before and after infection of all viruses. Furthermore, we applied the proposed GA to the minimum value search problem of a test function which has some local solutions far from the optimal solution. And, we discuss a difference of behaviors of the proposed GA based on each virus evaluation techniques.

• Geomechanics and Engineering
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• v.30 no.6
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• pp.551-564
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• 2022

Fragmenting the rock mass is considered as the most important work in open-pit mines. Ground vibration is the most hazardous issue of blasting which can cause critical damage to the surrounding structures. This paper focuses on developing an explicit model to predict the ground vibration through an multi objective evolutionary polynomial regression (MOGA-EPR). To this end, a database including 79 sets of data related to a quarry site in Malaysia were used. In addition, a gene expression programming (GEP) model and several empirical equations were employed to predict ground vibration, and their performances were then compared with the MOGA-EPR model using the mean absolute error (MAE), root mean square error (RMSE), mean (𝜇), standard deviation of the mean (𝜎), coefficient of determination (R²) and a20-index. Comparing the results, it was found that the MOGA-EPR model predicted the ground vibration more precisely than the GEP model and the empirical equations, where the MOGA-EPR scored lower MAE and RMSE, 𝜇 and 𝜎 closer to the optimum value, and higher R² and a20-index. Accordingly, the proposed MOGA-EPR model can be introduced as a useful method to predict ground vibration and has the capacity to be generalized to predict other blasting effects.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.5
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• pp.482-490
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• 2009

In this paper, the author proposes a new method for acoustic radiation optimum design to minimize noise from a vibrating panel-like structure using a collaborative population-based search method called the particle swarm optimization algorithm(PSOA). The PSOA is a parallel evolutionary computation technique initially developed by Kennedy and Eberhart. The acoustic radiation optimization method based on the PSOA consists of two processes. In the first process, the acoustic radiation analysis by an integrated p-version FEM/BEM, which was developed by using MATLAB, is performed to evaluate the exterior acoustic radiation field of the panel. The second process is to search the optimum design variables: 1) Shape of Bezier curves and 2) Shape and position of ribs, to minimize noise from the panel using the PSOA. The optimization method based on the PSOA is compared to that based on the steady state genetic algorithm(SSGA) in order to verify the effectiveness and validity of the optimal solution by PSOA. Finally, it is shown that the optimal designs of the panel obtained by using the PSOA can achieve effective reductions in radiated sound power.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.376-383
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• 2012

A number of scientific researches are currently being conducted on the potential health hazards of power frequency electric and magnetic field (EMF). There exists a non-objective and psychological belief that they are harmful, although no scientific and objective proof of such exists. This possible health risk from ELF magnetic field (MF) exposure, especially for children under 17 years of age, is currently one of Korea's most highly contested social issues. Therefore, to assess the magnetic field exposure levels of those children in their general living environments, the personal MF exposure levels of 436 subjects were measured for about 6 years using government funding. Using the measured database, estimation formulas were developed to predict personal MF exposure levels. These formulas can serve as valuable tools in estimating 24-hour personal MF exposure levels without directly measuring the exposure. Three types of estimation formulas were developed by applying evolutionary computation methods such as genetic algorithm (GA) and genetic programming (GP). After tuning the database, the final three formulas with the smallest estimation error were selected, where the target estimation error was approximately 0.03 ${\mu}T$. The seven parameters of each of these three formulas are gender (G), age (A), house type (H), house size (HS), distance between the subject's residence and a power line (RD), power line voltage class (KV), and the usage conditions of electric appliances (RULE).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2514-2520
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• 2010

The objective of this paper is to propose the methodology for automated generation of the optimal security defense strategies using evolutionary techniques. As damages by penetration exploiting vulnerability in computer systems and networks are increasing, security techniques have been researched actively. However it is difficult to generate optimal defense strategies because it needs to consider various situations on network environment according to countermeasures. Thus we have adopted a genetic algorithm in order to generate an optimal defense strategy as combination of countermeasures. We have represented gene information with countermeasures. And by using simulation technique, we have evaluated fitness through evaluating the vulnerability of system having applied various countermeasures. Finally, we have examined the feasibility by experiments on the system implemented by proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.538-542
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• 2001

DNA computing has been applied to the problem of getting an optimal soluting since Adleman's experiment. DNA computing uses strings with various length and four-type bases that makes more useful for finding a global optimal solutions of the complex multi-modal problems This paper presents DNA coding method finding optimal solution of the multi-modal function and compares the efficiency of this method with the genetic algorithms(GA). GA searches efffectively an optimal solution via the artificial evolution of individual group of binary string and DNA coding method uses DNA molecules and four-type bases denoted by the A(Ademine) C(Gytosine);G(Guanine)and T(Thymine). The selection, crossover, mutation operators are applied to both DNA coding algorithm and genetic algorithms and the comparison has been performed. The results show that the DNA based algorithm performs better than GA.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.413-419
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• 2007

The optimal power flow (OPF) problem was introduced by Carpentier in 1962 as a network constrained economic dispatch problem. Since then, it has been intensively studied and widely used in power system operation and planning. In the past few decades, many stochastic optimization methods such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm Optimization (PSO) have been applied to solve the OPF problem. In particular, PSO is a newly proposed population based stochastic optimization algorithm. The main idea behind it is based on the food-searching behavior of birds and fish. Compared with other stochastic optimization methods, PSO has comparable or even superior search performance for some hard optimization problems in real power systems. Nowadays, some modifications such as breeding and selection operators are considered to make the PSO superior and robust. In this paper, we propose the Modified PSO (MPSO), in which the mutation operator of GA is incorporated into the conventional PSO to improve the search performance. To verify the optimal solution searching ability, the proposed approach has been evaluated on an IEEE 3D-bus test system. The results showed that performance of the proposed approach is better than that of the standard PSO.

• Journal of Mechanical Science and Technology
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• v.20 no.3
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• pp.426-434
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• 2006

The impact of leakage was incorporated into the transfer functions of the complex head and discharge. The impedance transfer functions for the various leaking pipeline systems were also derived. Hydraulic transients could be efficiently analyzed by the developed method. The simulation of normalized pressure variation using the method of characteristics and the impulse response method shows good agreement to the condition of turbulent flow. The leak calibration could be performed by incorporation of the impulse response method with Genetic Algorithm (GA) and Harmony Search (HS). The objective functions for the leakage detection can be made using the pressure-head response at the valve, or the pressure-head or the flow response at a certain point of the pipeline located upstream from the valve. The proposed method is not constrained by the Courant number to control the numerical dissipation of the method of characteristics. The limitations associated with the discreteness of the pipeline system in the inverse transient analysis can be neglected in the proposed method.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.3
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• pp.1-10
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• 2011

Proportional.Integral.Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. However PID controller is poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This research comes up with a soft computing approach involving Genetic Algorithm, Evolutionary Programming, and Particle Swarm Optimization and Ant colony optimization. The proposed algorithm is used to tune the PID parameters and its performance has been compared with the conventional methods like Ziegler Nichols and Lambda method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. This research addresses comparison of tuning of the PID controller using soft computing techniques on Machine Direction of basics weight control in pulp and paper industry. Compared to other conventional PID tuning methods, the result shows that better performance can be achieved with the soft computing based tuning method. The ability of the designed controller, in terms of tracking set point, is also compared and simulation results are shown.

• Industrial Engineering and Management Systems
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• v.11 no.3
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• pp.288-298
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• 2012

Lot sizing and shipment scheduling are two interrelated decisions made by a manufacturing plant and a third-party logistics distribution center. This paper analyzes a dynamic inbound ordering problem and shipment problem with a freight container cost, in which the order size of multiple products and single container type are simultaneously considered. In the problem, each ordered product placed in a period is immediately shipped by some freight containers in the period, and the total freight cost is proportional to the number of containers employed. It is assumed that the load size of each product is equal and backlogging is not allowed. The objective of this study is to simultaneously determine the lot-sizes and the shipment schedule that minimize the total costs, which consist of production cost, inventory holding cost, and freight cost. Because the problem is NP-hard, we propose three meta-heuristic algorithms: a simulated annealing algorithm, a genetic algorithm, and a new population-based evolutionary meta-heuristic called self-evolution algorithm. The performance of the meta-heuristic algorithms is compared with a local search heuristic proposed by the previous paper in terms of the average deviation from the optimal solution in small size problems and the average deviation from the best one among the replications of the meta-heuristic algorithms in large size problems.