• Structural Engineering and Mechanics
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• v.52 no.4
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• pp.815-827
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• 2014

Buckling optimization of laminated composite plates is significant as they fail because of buckling under in-plane compressive loading. The plate is usually modeled without cutout so that the buckling strength is found analytically using classical laminate plate theory (CLPT). However in real world applications, the composite plates are modeled with cutouts for getting them assembled and to offer the provisions like windows, doors and control system. Finite element analysis (FEA) is used to analyze the buckling strength of the plate with cutouts and it leads to high computational cost when the plate is optimized. In this article, a genetic algorithm based optimization technique is used to optimize the composite plate with cutout. The computational time is highly reduced by replacing FEA with artificial neural network (ANN). The effectiveness of the proposed method is explored with two numerical examples.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.278-278
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• 2000

In this paper, we introduce a new genetic reordering operator based on the concept of schema to solve optimization problems such as the Traveling Salesman Problem(TSP) and maximizing or minimizing functions. In particular, because TSP is a well-known combinational optimization problem andbelongs to a NP-complete problem, there is huge solution space to be searched. For robustness to local minima, the operator separates selected strings into two parts to reduce the destructive probability of good building blocks. And it applies inversion to the schema part to prevent the premature convergence. At the same time, it searches new spaces of solutions. Additionally, the non-schema part is applied to inversion for robustness to local minima. By doing so, we can preserve diversity of the distributions in population and make GA be adaptive to the dynamic environment.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.623-625
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• 1998

This paper focuses on the fast convergence in nonlinear parameter optimization which is necessary for the fitting of nonlinear models to data. The simulated annealing(SA) and genetic algorithm(GA), which are widely used for combinatorial optimization problems, are stochastic strategy for search of the ground state and a powerful tool for optimization. However, their main disadvantage is the long convergence time by unnecessary extra works. It is also recognised that gradient-based nonlinear programing techniques would typically fail to find global minimum. Therefore, this paper develops a modified SA which is the SDS(Stochastic deterministic stochastic) algorithm can minimize cost function of optimal problem.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.624-636
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• 2006

This paper proposes a hybrid approach involving Genetic Algorithm (GA) and Bacterial Foraging (BF) for tuning the PID controller of an AVR. Recently the social foraging behavior of E. coli bacteria has been used to solve optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the life time of the bacteria. Further, the proposed algorithm is used for tuning the PID controller of an AVR. Simulation results are very encouraging and this approach provides us a novel hybrid model based on foraging behavior with a possible new connection between evolutionary forces in social foraging and distributed non-gradient optimization algorithm design for global optimization over noisy surfaces.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.587-592
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by simplex method in reconstructive search space. Two numerical examples are also presented in this paper to comparing with conventional methods.

• Structural Engineering and Mechanics
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• v.70 no.5
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.1
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• pp.20-31
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• 2014

This paper deals with the development of a realistic shape optimization of damaged columns that are subjected to conservative and non-conservative forces, using the Genetic Algorithm (GA). The analysis is based on the design of the most optimized shape of the column under the constraint of constant weight, considering the Static, Vibrational, and Flutter characteristics. Under the action of conservative and non-conservative longitudinal forces, an elastic column loses its stability. A numerical analysis based on FEM has been performed on a uniform damaged column, to compute the fundamental buckling load, vibration frequency, and flutter load, under various end restraints. An optimization search based on the Genetic Algorithm is then executed, to find the optimal shape design of the column. The optimized column references the one having the highest buckling load, highest vibration frequency, and highest flutter load, among all the possible shapes of the column, for a given volume. A comparison is then made between the values obtained for the optimized damaged column, and those obtained for the optimized undamaged column. The comparison reveals that the incorporation of damage in the column alters its optimal shape to only a certain extent. Also, the critical load and frequency values for the optimized damaged column are comparatively low, compared with those obtained for the optimized undamaged column. However, these results hold true only for moderate-intensity damage cases. For high intensity damage, the optimal shape may not remain the same, and may vary, according to the severity of damage.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1699-1708
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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, the OPF problem has been intensively studied and widely used in power system operation and planning. In these days, OPF is becoming more and more important in the deregulation environment of power pool and there is an urgent need of faster solution technique for on-line application. To solve OPF problem, many heuristic optimization methods have been developed, such as Genetic Algorithm(GA), Evolutionary Programming(EP), Evolution Strategies(ES), and Particle Swarm Optimization(PSO). Especially, PSO algorithm is a newly proposed population based heuristic optimization algorithm which was inspired by the social behaviors of animals. However, population based heuristic optimization methods require higher computing time to find optimal point. This shortcoming is overcome by a straightforward parallel processing of PSO algorithm. The developed parallel PSO algorithm is implemented on a PC cluster system with 6 Intel Pentium IV 2GHz processors. The proposed approach has been tested on the IEEE 30-bus system. The results showed that computing time of parallelized PSO algorithm can be reduced by parallel processing without losing the quality of solution.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.175-184
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• 2009

In this paper, a method for optimal measurement placement in the problem of static harmonic state estimation in power systems is proposed. At first, for achieving to a suitable method by considering the precision factor of the estimation, a procedure based on Genetic Algorithm (GA) for optimal placement is suggested. Optimal placement by regarding the precision factor has an evident solution, and the proposed method is successful in achieving the mentioned solution. But, the previous applied method, which is called the Sequential Elimination (SE) algorithm, can not achieve to the evident solution of the mentioned problem. Finally, considering both precision and economic factors together in solving the optimal placement problem, a practical method based on GA is proposed. The simulation results are shown an improvement in the precision of the estimation by using the proposed method.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.732-735
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• 1999

A GA-based optimization technique is adopted in the paper to obtain optimal future control inputs for predictive control systems. For reliable future predictions of a process, we identify the underlying process with an NNARX model structure and investigate to reduce the volume of neural network based on the Lipschitz index and a criterion. Since most industrial processes are subject to their constraints, we deal with the input-output constraints by modifying some genetic operators and/or using a penalty strategy in the GAPC. Some computer simulations are given to show the effectiveness of the GAPC method compared with the adaptive GPC algorithm.