• Management Science and Financial Engineering
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• v.13 no.1
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• pp.115-119
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• 2007

In [1], robust combinatorial optimization problem is introduced, where a positive integer $\Gamma$ is used to control the degree of robustness. The proposed algorithm needs solutions of n+1 nominal problems. In this paper, we show that the number of problems needed reduces to $n+1-\Gamma$.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.115-125
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• 2021

Simulation-based hull form optimization is a typical HEB (high-dimensional, expensive computationally, black-box) problem. Conventional optimization algorithms easily fall into the "curse of dimensionality" when dealing with HEB problems. A recently proposed Cross-Entropy (CE) optimization algorithm is an advanced stochastic optimization algorithm based on a probability model, which has the potential to deal with high-dimensional optimization problems. Currently, the CE algorithm is still in the theoretical research stage and rarely applied to actual engineering optimization. One reason is that the Monte Carlo (MC) method is used to estimate the high-dimensional integrals in parameter update, leading to a large sample size. This paper proposes an improved CE algorithm based on quasi-Monte Carlo (QMC) estimation using high-dimensional truncated Sobol subsequence, referred to as the QMC-CE algorithm. The optimization performance of the proposed algorithm is better than that of the original CE algorithm. With a set of identical control parameters, the tests on six standard test functions and a hull form optimization problem show that the proposed algorithm not only has faster convergence but can also apply to complex simulation optimization problems.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.82-96
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• 2019

A semi-submersible structure has been widely used for offshore drilling and production of oil and gas. The small water plane area makes the structure very sensitive to weight increase in terms of payload and stability. Therefore, it is necessary to lighten the substructure from the early design stage. This study aims at an optimization of hull structure based on a sophisticated yield and buckling strength in accordance with classification rules. An in-house strength assessment system is developed to automate the procedure such as a generation of buckling panels, a collection of required panel information, automatic buckling and yield check and so on. The developed system enables an automatic yield and buckling strength check of all panels composing the hull structure at each iteration of the optimization. Design variables are plate thickness and stiffener section profiles. In order to overcome the difficulty of large number of design variables and the computational burden of FE analysis, various methods are proposed. The steepest descent method is selected as the optimization algorithm for an efficient search. For a reduction of the number of design variables and a direct application to practical design, the stiffener section variable is determined by selecting one from a pre-defined standard library. Plate thickness is also discretized at 0.5t interval. The number of FE analysis is reduced by using equations to analytically estimating the stress changes in gradient calculation and line search steps. As an endeavor to robust optimization, the number of design variables to be simultaneously optimized is divided by grouping the scantling variables by the plane. A sequential optimization is performed group by group. As a verification example, a central column of a semi-submersible structure is optimized and compared with a conventional optimization of all design variables at once.

• Computational Structural Engineering : An International Journal
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• v.1 no.2
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• pp.89-96
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• 2001

A study on the topology optimization of a Hard-Disk-Driver(HDD) actuator arm is presented. The purpose of the present wert is to increase the natural frequency of tole first lateral mode of the HDD actuator arm under the constraint of total moment of inertia, so as to facilitate the position control of the high speed actuator arm. The first lateral mode is an important factor in the position control process. Thus the topology optimization for 2-D model of the HDD actuator arm is considered. A new objective function corresponding to multieigenvalue optimization is suggested to improve the solution of the eigenvalue optimization problem. The material density of the structure is treated as the design variable and the intermediate density is penalized. The effects of different element types and material property functions on the final topology are studied. When the problem is discretized using 8-node element of a uniform density, tole smoothly-varying density field is obtained without checker-board patterns incurred. AS a result of 7he study, an improved design of the HDD actuator arm is suggested. Dynamic characteristics of the suggested design are compared computationally with those of the old design. With the same amount of the moment of inertia, the natural frequency of the first lateral mode of the suggested design is subsequently increased over the existing one.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.174-181
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• 2011

This paper proposes a novel binary ant colony optimization (NBACO) method. The proposed NBACO is based on the concept and principles of ant colony optimization (ACO), and developed to solve the binary and combinatorial optimization problems. The concept of conventional ACO is similar to Heuristic Dynamic Programming. Thereby ACO has the merit that it can consider all possible solution sets, but also has the demerit that it may need a big memory space and a long execution time to solve a large problem. To reduce this demerit, the NBACO adopts the state probability matrix and the pheromone intensity matrix. And the NBACO presents new updating rule for local and global search. The proposed NBACO is applied to test power systems of up to 100-unit along with 24-hour load demands.

• Journal of the Society of Naval Architects of Korea
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• v.47 no.1
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• pp.58-66
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• 2010

Topology design optimization is a method to determine the optimal distribution of material that yields the minimal compliance of structures, satisfying the constraint of allowable material volume. The method is easy to implement and widely used so that it becomes a powerful design tool in various disciplines. In this paper, a large-scale topology design optimization method is developed using the efficient adjoint sensitivity and optimality criteria methods. Parallel computing technique is required for the efficient topology optimization as well as the precise analysis of large-scale problems. Parallelized finite element analysis consists of the domain decomposition and the boundary communication. The preconditioned conjugate gradient method is employed for the analysis of decomposed sub-domains. The developed parallel computing method in topology optimization is utilized to determine the optimal structural layout of human powered vessel.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.859-865
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• 2014

Optimization of an electric machine is mainly a nonlinear multi-modal problem. For the optimization of the multi-modal problem, many function calls are required with much consumption of time. To address this problem, this paper proposes a novel hybrid algorithm in which function calls are less than conventional methods. Specifically, the proposed method uses the kriging metamodel and the fill-blank technique to find an approximated solution in a whole problem region. To increase the convergence speed in local peaks, a parallel gradient assisted simplex method is proposed and combined with the kriging meta-model. The correctness and usefulness of the proposed hybrid algorithm is verified through a mathematical test function and applied into the practical optimization as the cogging torque minimization for an interior permanent magnet synchronous machine.

• Computers and Concrete
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• v.30 no.2
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• pp.127-141
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• 2022

In this paper, a numerical procedure is proposed for achieving the minimum cost design of reinforced concrete polygonal column cross-sections under compression and biaxial bending. A methodology is developed to integrate the metaheuristic algorithm Quantum Particle Swarm Optimization (QPSO) with an algorithm for the evaluation of the strength of reinforced concrete cross-sections under combined axial load and biaxial bending, according to the design criteria of Brazilian Standard ABNT NBR 6118:2014. The objective function formulation takes into account the costs of concrete, reinforcement, and formwork. The cross-section dimensions, the number and diameter of rebar and the concrete strength are taken as discrete design variables. This methodology is applied to polygonal cross-sections, such as rectangular sections, rectangular hollow sections, and L-shaped cross-sections. To evaluate the efficiency of the methodology, the optimal solutions obtained were compared to results reported by other authors using conventional methods or alternative optimization techniques. An additional study investigates the effect on final costs for an alternative parametrization of rebar positioning on the cross-section. The proposed optimization method proved to be efficient in the search for optimal solutions, presenting consistent results that confirm the importance of using optimization techniques in the design of reinforced concrete structures.

• Structural Engineering and Mechanics
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• v.81 no.1
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• pp.93-102
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• 2022

The optimum design of reinforced concrete cantilever retaining walls subjected to seismic loads is an extremely important challenge in structural and geotechnical engineering, especially in seismic zones. This study proposes an adaptive sperm swarm optimization algorithm (ASSO) for economic design of retaining structure under static and seismic loading. The proposed ASSO algorithm utilizes a time-varying velocity damping factor to provide a fine balance between the explorative and exploitative behavior of the original method. In addition, the new method considers a reasonable velocity limitation to avoid the divergence of the sperm movement. The proposed algorithm is benchmarked with a set of test functions and the results are compared with the standard sperm swarm optimization (SSO) and some other robust metaheuristic from the literature. For seismic optimization of retaining structures, Mononobe-Okabe method is employed for dynamic loading conditions and total construction cost of the structure is considered as the single objective function. The optimization constraints include both geotechnical and structural restrictions and the design variables are the geometrical dimensions of the wall and the amount of steel reinforcement. Finally, optimization of two benchmark retaining structures under static and seismic loads using the ASSO algorithm is presented. According to the numerical results, the ASSO may provide better optimal solutions, and the designs obtained by ASSO have a lower cost by up to 20% compared with some other methods from the literature.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.769-774
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• 2024

The availability of PMT(Project Management Tool) in the market has been increasing rapidly in recent years and Significant advancements have been made for project managers to use for planning, monitoring, and control. Recently, studies applying the Reinforcement-Learning Based Construction Schedule Simulation algorithm for construction project process planning/management are increasing. When reinforcement learning is applied, the agent recognizes the current state and learns to select the action that maximizes the reward among selectable actions. However, if the action of global optimal points is not selected in simulation selection, the local optimal resource may receive continuous compensation (+), which may result in failure to reach the global optimal point. In addition, there is a limitation that the optimization time can be long as numerous iterations are required to reach the global optimal point. Therefore, this study presented a method to improve optimization performance by increasing the probability that a resource with high productivity and low unit cost is selected, preventing local optimization, and reducing the number of iterations required to reach the global optimal point. In the performance evaluation process, we demonstrated that this method leads to closer approximation to the optimal value with fewer iterations.