• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.406-417
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• 2014

This paper presents a numerical optimization method to improve the performance of the propeller with Turbo-Ring using real-coded genetic algorithm. In the presented method, Unimodal Normal Distribution Crossover (UNDX) and Minimal Generation Gap (MGG) model are used as crossover operator and generation-alternation model, respectively. Propeller characteristics are evaluated by a simple surface panel method "SQCM" in the optimization process. Blade sections of the original Turbo-Ring and propeller are replaced by the NACA66 a = 0.8 section. However, original chord, skew, rake and maximum blade thickness distributions in the radial direction are unchanged. Pitch and maximum camber distributions in the radial direction are selected as the design variables. Optimization is conducted to maximize the efficiency of the propeller with Turbo-Ring. The experimental result shows that the efficiency of the optimized propeller with Turbo-Ring is higher than that of the original propeller with Turbo-Ring.

• Journal of Navigation and Port Research
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• v.33 no.9
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• pp.635-644
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• 2009

The optimum design of power yacht belongs to the nonlinear constrained optimization problems. The determination of scantlings for the bow structure is a very important issue with in the whole structural design process. The derived design results are obtained by the use of real-coded micro-genetic algorithm including evaluation from Lloyd's Register small craft guideline, so that the nominal limiting stress requirement can be satisfied. In this study, the minimum volume design of bow structure on the power yacht was carried out based on the finite element analysis. The target model for optimum design and local structural analysis is the bow structure of a power yacht. The volume of bow structure and the main dimensions of structural members are chosen as an objective function and design variable, respectively. During optimization procedure, finite element analysis was performed to determine the constraint parameters at each iteration step of the optimization loop. optimization results were compared with a pre-existing design and it was possible to reduce approximately 19 percents of the total steel volume of bow structure from the previous design for the power yacht.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.55-62
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• 2004

Even though FOPDT(First-Order Plus Dead-Time) process is most widely applied in the industrial control field, it is difficult to figure out a in precise process model because of the long dead-time problem. Also, control performance may be deteriorated due to the mismatch problem of plant and model. Thus, the accuracy of process identification is the most important problem in FOPDT process control. In this paper, the proposed method using real-coded genetic algorithm outperforms the existing estimation methods that use step-test responses. The proposed strategy obtained useful result through a number of simulation examples.

• Journal of Biosystems Engineering
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• v.38 no.2
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• pp.149-162
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• 2013

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.341-349
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• 2013

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.168-175
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• 2003

A new algorithm based on Genetic Algorithms is proposed f3r solving process optimization problems formulated in MINLP, GDP and hybrid MINLP/GDP. This work is focused especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is experimented and many heuristics are adopted. Real and binary coded Genetic Algorithm initiates the global search in the entire search space and at every stage Simulated Annealing makes the candidates to climb up the local hills. Multi-Niche Crowding method is adopted as the multimodal function optimization technique. and the adaptation of probabilistic parameters and dynamic penalty systems are also implemented. New strategies to take the logical variables and constraints into consideration are proposed, as well. Various test problems selected from many fields of process systems engineering are tried and satisfactory results are obtained.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.11
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• pp.989-996
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• 2016

This paper intends to develop the rotor track and balance (RTB) algorithm using the nonlinear RTB models and a real-coded hybrid genetic algorithm. The RTB response data computed using the trim solutions with variation of the adjustment parameters have been used to build nonlinear RTB models based on the quadratic interpolation functions. Nonlinear programming problems to minimize the track deviations and the airframe vibration responses have been formulated to find optimum settings of balance weights, trim-tab deflections, and pitch-link lengths of each blade. The results are efficiently resolved using the real-coded genetic algorithm hybridized with the particle swarm optimization techniques for convergence acceleration. The nonlinear RTB models and the optimized RTB parameters have been compared with those computed using the linear models to validate the proposed techniques. The results showed that the nonlinear models lead to more accurate models and reduced RTB responses than the linear counterpart.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.11a
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• pp.111-116
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• 2004

In this paper, parameters of a given DC motor system are estimated using the model adjustment technique and the real coded genetic algorithm(RCGA) technique. A number of tuning methods, based on experience and experiment, such as Ziegler-Nichols, Cohen-Coon, IMC, L-ITAE Method have been proposed to obtain parameters for the PID controller. This paper proposes estimating parameters of PID controller using RCGA. The performance of the proposed algorithm is demonstrated through simulations and experiences.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.832-839
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• 2009

In this paper, we introduce an design of fuzzy relation-based fuzzy neural networks with multi-output. Fuzzy relation-based fuzzy neural networks comprise the network structure generated by dividing the entire input space. The premise part of the fuzzy rules of the network reflects the relation of the division space for the entire input space and the consequent part of the fuzzy rules expresses three types of polynomial functions such as constant, linear, and modified quadratic. For the multi-output structure the neurons in the output layer were connected with connection weights. The learning of fuzzy neural networks is realized by adjusting connections of the neurons both in the consequent part of the fuzzy rules and in the output layer, and it follows a back-propagation algorithm. In addition, in order to optimize the network, the parameters of the network such as apexes of membership functions, learning rate and momentum coefficient are automatically optimized by using real-coded genetic algorithm. Two examples are included to evaluate the performance of the proposed network.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.652-658
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• 1998

We introduce the concept of a bi-population scheme for real-coded GAs consisting of an explorer sub-Ga and an exploiter sub-GA. The explorer sub-GA mainly performs global exploration of the search space, and incorporates a restart mechanism to help avoid being trapped at local optima. The exploiter sub-GA performs exploitation of fit local areas of the search space around the neighborhood of the best-so-far solution. Thus the search function of the algorithm is divided. the proposed technique exhibits performance significantly superior to standard GAs on two complex highly multimodal problems.