• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.04a
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• pp.125-133
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• 1993

Preflex composit girder is intended for a better use on both steel and concrete by introducting prestress into the lower flange concrete with preflection. In Korea, recently preflex bridges are widely used especially for urban construction but the design method depends on the conventional ASD(Allowable Stress Design). This paper suggests an optimization model for the design of preflex composite bridges based on LIFD(Load Resistance Factor Design). The optimization algorithm adopted for the NLP model proposed in the paper is the FTM(Flexible Tolerance Method) which is very efficient for the approximate continuous optimization. For the discrete optimum results, a pesudo discrete optimization is used for the economical round-up to the available dimensions. The economic effectiveness of optimum design based on the LRFD method is investigation by comparing the results with those of the ASD method. Based on applications to the actual design examples, it may be concluded that the optimization model suggested in the paper provides economical but reliable design. And the suggested in the paper provides economical but reliable design. And the computer code for the automatic optimum design of preflex bridges developed in the paper for a CAD system may be successfully used in practice.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1000-1013
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• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.203-210
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• 2008

Because BSP(Basic Support Protocol) of NEMO(Network Mobility) has important limitation of not providing route optimization, several route optimization schemes have been proposed. By analyzing and improving the limitations of the existing schemes. we Propose an advanced integrated route optimization scheme for the communication through both the internal and external routing of nested NEMO. Our proposal includes a secure route optimization protocol which connects TLMR directly to an external node CN without passing through any HAs. and allows TLMR to control the internal path without passing through the internet. Thus, our scheme can strengthen the security as well as improve the path and delay of NEMO communication.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.33-40
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• 2021

The traveling salesman problem (TSP) is one of the well-known and extensively studied NPC problems in combinatorial optimization. To solve it effectively and efficiently, various optimization algorithms have been developed by scientists and researchers. However, most optimization algorithms are designed based on the concept of improving route in the iterative improvement process so that the optimal solution can be finally found. In contrast, there have been relatively few algorithms to find the optimal solution using route construction mechanism. In this paper, we propose a route construction optimization algorithm to solve the symmetric TSP with the help of ratio value. The proposed algorithm starts with a set of sub-routes consisting of three cities, and then each good sub-route is enhanced step by step on both ends until feasible routes are formed. Before each subsequent expansion, a ratio value is adopted such that the good routes are retained. The experiments are conducted on a collection of benchmark symmetric TSP datasets to evaluate the algorithm. The experimental results demonstrate that the proposed algorithm produces the best-known optimal results in some cases, and performs better than some other route construction optimization algorithms in many symmetric TSP datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.2060-2073
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• 2022

Cloud Computing permits users to access vast amounts of services of computing power in a virtualized environment. Providing secure services is essential. There are several problems to real-world optimization that are dynamic which means they tend to change over time. For these types of issues, the goal is not always to identify one optimum but to keep continuously adapting to the solution according to the change in the environment. The problem of scheduling in Cloud where new tasks keep coming over time is unique in terms of dynamic optimization problems. Until now, there has been a large majority of research made on the application of various Evolutionary Algorithms (EAs) to address the issues of dynamic optimization, with the focus on the maintenance of population diversity to ensure the flexibility for adapting to the changes in the environment. Generally, trust refers to the confidence or assurance in a set of entities that assure the security of data. In this work, a dynamic optimization technique is proposed to find an optimal trust weights in cloud during scheduling.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2420-2426
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• 2015

This paper proposes an evolutionary optimization approach for optimal hopping of humanoid robots. In the proposed approach, the hopping trajectory is generated by a central pattern generator (CPG). The CPG is one of the biologically inspired approaches, and it generates rhythmic signals by using neural oscillators. During the hopping motion, the disturbance caused by the ground reaction forces is compensated for by utilizing the sensory feedback in the CPG. Posture control is essential for a stable hopping motion. A posture controller is utilized to maintain the balance of the humanoid robot while hopping. In addition, a compliance controller using a virtual spring-damper model is applied for stable landing. For optimal hopping, the optimization of the hopping motion is formulated as a minimization problem with equality constraints. To solve this problem, two-phase evolutionary programming is employed. The proposed approach is verified through computer simulations using a simulated model of the small-sized humanoid robot platform DARwIn-OP.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2262-2267
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• 2018

In electric machine design, there is a large computation cost for finite element analyses (FEA) when analyzing nonlinear characteristics in the machine Therefore, for the optimal design of an electric machine, designers commonly use an optimization algorithm capable of excellent convergence performance. However, robustness consideration, as this factor can guarantee machine performances capabilities within design uncertainties such as the manufacturing tolerance or external perturbations, is essential during the machine design process. Moreover, additional FEA is required to search robust optimum. To address this issue, this paper proposes a computationally efficient robust optimization algorithm. To reduce the computational burden of the FEA, the proposed algorithm employs a useful technique which termed static analysis assisted technique (SAAT). The proposed method is verified via the effective robust optimal design of electric machine to reduce cogging torque at a reasonable computational cost.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1417-1426
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• 2017

Power system analysis, Non-Convex Economic Dispatch (NED) is considered as an open and demanding optimization problem. Despite the fact that realistic ED problems have non-convex cost functions with equality and inequality constraints, conventional search methods have not been able to effectively find the global answers. Considering the great potential of meta-heuristic optimization techniques, many researchers have started applying these techniques in order to solve NED problems. In this paper, a new and efficient approach is proposed based on imperialist competitive algorithm (ICA). The proposed algorithm which is named multi-operator ICA (MuICA) merges three operators with the original ICA in order to simultaneously avoid the premature convergence and achieve the global optimum answer. In this study, the proposed algorithm has been applied to different test systems and the results have been compared with other optimization methods, tending to study the performance of the MuICA. Simulation results are the confirmation of superior performance of MuICA in solving NED problems.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.97-104
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• 2016

In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.8
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• pp.344-351
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• 2002

The proposed immune algorithm has an uncomplicated structure and memory-cell mechanism as the optimization algorithm which imitates the principle of humoral immune response. We use the proposed algorithm to solve parameter optimization problems. Up to now, the applications of immune algorithm have been optimization problems with non-varying system parameters. Therefore the usefulness of memory-cell mechanism in immune algorithm is without. This paper proposes the immune algorithm using a memory-cell mechanism which can be the application of system with nonlinear varying parameters. To verified performance of the proposed immune algorithm, the speed control of nonlinear DC motor are performed. The results of Computer simulations represent that the proposed immune algorithm shows a fast convergence speed and a good control performances under the varying system parameters.