• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1100-1122
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• 2023

Scheduling user-submitted cloud tasks to the appropriate virtual machine (VM) in cloud computing is critical for cloud providers. However, as the demand for cloud resources from user tasks continues to grow, current evolutionary algorithms (EAs) cannot satisfy the optimal solution of large-scale cloud task scheduling problems. In this paper, we first construct a large- scale multi-objective cloud task problem considering the time and cost functions. Second, a multi-objective optimization algorithm based on multi-factor optimization (MFO) is proposed to solve the established problem. This algorithm solves by decomposing the large-scale optimization problem into multiple optimization subproblems. This reduces the computational burden of the algorithm. Later, the introduction of the MFO strategy provides the algorithm with a parallel evolutionary paradigm for multiple subpopulations of implicit knowledge transfer. Finally, simulation experiments and comparisons are performed on a large-scale task scheduling test set on the CloudSim platform. Experimental results show that our algorithm can obtain the best scheduling solution while maintaining good results of the objective function compared with other optimization algorithms.

• Journal of the Korean Home Economics Association
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• v.42 no.3
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• pp.145-158
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• 2004

Experimental conditions for evaluating chitosan antibacterial activities were established. The chitosan antibacterial activities were measured against the Staphylococcus aureus and evaluated for their application to antibacterial textile finishing. The strain of Staphylococcus aureus used in this experiments was KCTC 1916. The chitosan antibacterial activities were estimated from the bacterial densities or ％reduction of bacteria in chitosan solutions and bacterial culture mixtures after incubation under specific conditions. Six parameters as follows were evaluated to optimize the experimental conditions for measuring antibacterial activities. The different combinations of mixtures according to the different ratios of chitosan solutions to the bacterial cultures showed different antibacterial activities. However, the chitosan antibacterial activities could be evaluated by comparing the data obtained from the same combinations of mixtures. The solvent influence on the chitosan solution antibacterial activities could be eliminated using control solution containing the same concentration of acetic acid. The initial pH of the chitosan -bacterial mixtures also affected the chitosan antibacterial activity; at a higher pH, higher activity in terms of ％reduction of bacteria was observed. In case of the bacterial solution without either the acetic acid or chitosan, the initial pH of the solution did not significantly affect bacterial growth. The ％ reduction of bacteria increased when contact times of bacteria with chitosan in the chitosan -bacterial mixture were expended upto 24 hours. However, the chitosan antibacterial activities could be successfully evaluated at contact time 0 where the chitosan-bacterial mixture was plated immediately after mixing and incubated to measure the bacterial number to 24 hours. Evaluating ％reduction of bacteria in the test mixtures after incubation were not changed when the inoculated bacterial concentrations were 2.3${\times}$10$\^$0/ml to 2.3${\times}$10$\^$6/ml. The optimal range of incubation time of the petri-Dish after plating the chitosan-bacterial mixture was 24 to 72 hours depending on the antibacterial activities of the test solutions.

• Journal of the Korean Geotechnical Society
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• v.20 no.3
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• pp.151-159
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• 2004

This paper describes a systematic way of identifying the horizontal coefficient of consolidation of clayey soil by applying an optimization technique to the early part of dissipation data measured from the self-boring pressuremeter strain holding test. An analytical solution developed by Randolph ＆ Wroth (1979) was implemented in normalized form to express the build-up of excess pore pressures as a function of the rigidity index and subsequent dissipation of excess pore pressures around a pressuremeter Horizontal coefficient of consolidation was determined by minimizing the differences between theoretical and measured excess pore pressure curves over 50% degree of dissipation range using optimization technique. The effectiveness of the proposed back-analysis method was examined against the real fled performances obtained from pressuremeter strain holding tests at Gimje and Yangsan site. It is shown that the proposed back-analysis method can evaluates the rational horizontal coefficient of consolidation, which is similar to those obtained from the piezocone dissipation test. Furthermore, proposed method can evaluate appropriate coefficient of consolidation for soil under partially drained condition.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.36-40
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• 2015

In Korea, new total energy-saving solution has planned to build test-bed in underground subway station. Breaking energy is one of the most energy saving method in railway, but it has not be fully used up for economical purpose. This paper demonstrates on energy saving technology application including breaking energy and heating energy to underground subway station. It also offer solution of optimization of power energy flow. Moreover, economic feasibility analysis performed for undergound test bed constuction.

• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.339-350
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• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.11-21
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• 2015

The purpose of this research is to develop algorithms using the Differential Evolution Algorithm (DE) to solve a multi-objective, sources and stages location-allocation problem. The development process starts from the design of a standard DE, then modifies the recombination process of the DE in order improve the efficiency of the standard DE. The modified algorithm is called modified DE. The proposed algorithms have been tested with one real case study (large size problem) and 2 randomly selected data sets (small and medium size problems). The computational results show that the modified DE gives better solutions and uses less computational time than the standard DE. The proposed heuristics can find solutions 0 to 3.56% different from the optimal solution in small test instances, while differences are 1.4-3.5% higher than that of the lower bound generated by optimization software in medium and large test instances, while using more than 99% less computational time than the optimization software.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.113-116
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• 1995

In this study, we formulate production costing problem with environmental and operational constraints into an optimization problem of LP form. In the process of formulation, auxiliary constraints on which reflect unit loading order are constructed to reduce the size of optimization problem by economic operation rules. As a solution of the optimization problem in LP form, we use Karmarkar's method which performs much faster than simplex method in solving large scale LP problem. The proposed production costing algorithm is applied to IEEE Reliability Test System, and performs production simulation under environmental and operational constraints. Test and computer results are given to show the accuracy and usefulness of the proposed algorithm in the field of power system planning.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.197-205
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• 2013

In this study, we developed a program of optimizing stand-level carbon stock using a stand-level yield model and the Simulated Annealing (SA) heuristic method to derive a optimized forest treatment solution. The SA is one of the heuristic algorithms that can provide a desirable management solution when dealing with various management purposes. The SA heuristic algorithm applied 'thermal equilibrium test', a thresholds approach to solve the phenomenon that does not find an optimum solution and stays at a local optimum value during the process. We conducted a sensitivity test for the temperature reduction rate, the major parameter of the thermal equilibrium test, to analyze its influence on the objective function value and the total iteration of the optimization process. Using the developed program, three scenarios were compared: a common treatment in forestry (baseline), the optimized solution of maximizing the amount of harvest(alternative 1), and the optimized solution of maximizing the amount of carbon stocks(alternative 2). As the results, we found that the alternative 1 showed provide acceptable solutions for the objectives. From the sensitivity test, we found that the objective function value and the total iteration of the process can be significantly influenced by the temperature reduction rate. The developed program will be practically used for optimizing stand-level carbon stock and developing optimized treatment solutions.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.2
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• pp.544-556
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• 1991

The optimization of many engineering design problems requires a nonlinear programming algorithm that is robust and efficient. A general-purpose nonlinear optimization program IDOL (Interactive Design Optimization Library) is developed based on the Augmented Lagrange Mulitiplier (ALM) method. The ideas of selecting a good initial design point, using resonable initial values for Lagrange multipliers, constraints scaling, descent vector restarting, and dynamic stopping criterion are employed for computational enhancement to the ALM method. A descent vector is determined by using the Broydon-Fletcher-Goldfarb-Shanno (BFGS) method. For line search, the Incremental-Search method is first used to find bounds on the solution, then the bounds are reduced by the Golden Section method, and finally a cubic polynomial approximation technique is applied to locate the next design point. Seven typical test problems are solved to show IDOL efficient and robust.