• Structural Engineering and Mechanics
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• v.45 no.1
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• pp.111-127
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• 2013

In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and $CO_2$ emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.

• Smart Structures and Systems
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• v.18 no.3
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• pp.425-448
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• 2016

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2005.04a
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• pp.273-280
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• 2005

The design of structural engineering optimization is to minimize the cost. This problem has many objective functions formulating section and shape as a function of the included discrete variables. simulated annealing, genetic algerian and TABU algerian are searching methods for optimum values. The object of this reserch Is comparing the result of TABU algorithm, and verifying the efficiency of TABU algorithm in structural optimization design field. For the purpose, this study used a solid truss of 25 elements having 10 nodes, and size optimization for each constraint and load condition of Geodesic ome, and shape optimization of Cable Dome for verifying spatial structures by the application of TABU algorithm.

• Journal of Biosystems Engineering
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• v.24 no.5
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• pp.445-452
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• 1999

This study was conducted to develop a searching algorithm for optimal daily temperature setpoint greenhouse. An algorithm using crop growth and energy models was developed to determine optimum crop growth environment. The results of this study were as follows: 1. Mathematical models for crop growth and energy consumption were derived to define optimal daily temperature setpoint. 2. Optimum temperature setpoint, which could maximize performance criterion, was determined by using Pontryagin maximum principle. 3. Dynamic control of daily temperature using the developed algorithm showed higher performance criterion than static control with fixed temperature setpoint. Performance criteria for dynamic control models were with simulated periodic weather data and with real weather data, increased by 48% and 60%, respectively.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.2
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• pp.33-44
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• 1986

This research is concerned with group scheduling problems in multi-stage manufacturing system with dependent setup time. The objective of the research is to develop and evaluate a heuristic algorithm for determining group sequence and job sequence within each group to minimize total tardiness in multi-stage manufacturing systems with sequence dependent group setup time. The group scheduling heuristic algorithm is developed and evaluated by comparisons with twenty-seven problems with the known optimum solutions and 144,000 random schedules of a large variety problems. The results indicate that the proposed heuristic algorithm gets the same optimum solutions for the problems and also provides the good solutions in comparison with the random schedules of the large variety problems. A numerical example is given to illustrate the heuristic algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.612-615
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• 2002

The objective of this study is to develop an integrated process planning system which can flexibly cope with the status changes in a shop floor by utilizing the concept of Non-Linear and Closed-Loop Process Planning(NCPP). In this paper, Genetic Algorithm(GA) is employed in order to quickly generate feasible setup sequences for minimizing the makespan and tardiness under an NCPP. The genetic algorithm developed in this study for getting the machine load utilizes differentiated mutation rate and method in order to increase the chance to avoid a local optimum and to reach a global optimum. Also, it adopts a double gene structure for the sake of convenient modeling of the shop floor. The last step in this system is a simulation process which selects a proper process plan among alternative process plans.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.4
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• pp.284-290
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• 2003

It is very important to minimize the weight of shaft from the viewpoint of economics and manufacture. For minimizing effectively the diameter of shaft in torsional shafting, authors developed computer program using the real-coded genetic algorithm which is one of optimizing techniques and based on real coding representation of genetic algorithm. In order to confirm the accuracy and effectiveness of the developed computer program, the computational results by the developed program were compared with those of conventional strength, stiffness and vibration designs for a generator shafting.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.466-471
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• 2006

This raper presents a new optimization method combining the genetic algorithm with the response surface method for the optimum design of a Brushless Direct Current motor. The method utilizes a regression function approximating an objective function and the window moving and zoom-in method so as to complement disadvantages of both the genetic algorithm and response surface method. The results verify that the proposed method is powerful and effective in reducing cogging torque by optimizing only a few decisive design factors compared with the conventional stochastic methods.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.357-360
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. In this research, we tried the back-propagation algorithm of artificial neural network to find an optimum solution in the cooling system design of injection mold. The cooling system optimization problem that was once solved by a response surface method with 4 design variables was solved by applying the back-propagation algorithm, resulting in a solution with a sufficient accuracy. Furthermore the number of training points was much reduced by applying the fractional factorial design without losing solution accuracy.

• Advances in robotics research
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• v.2 no.1
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• pp.13-31
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• 2018

This paper presents the performance of Teaching-Learning-Based Optimization (TLBO) algorithm for optimum static balancing of a robot manipulator. Static balancing of robot manipulator is an important aspect of the overall robot performance and the most demanding process in any robot system to match the need for the production requirements. The average force on the gripper in the working area is considered as an objective function. Length of the links, angle between them and stiffness of springs are considered as the design variables. Three robot manipulator configurations are optimized. The results show the better or competitive performance of the TLBO algorithm over the other optimization algorithms considered by the previous researchers.