• 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.

• Special Issue of the Society of Naval Architects of Korea
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• 2015.09a
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• pp.93-95
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• 2015

In case of crude oil tanker, loading condition must be satisfied MARPOL damage stability criteria (Reg.28). But some specific demands are hard to content the criteria. So, it takes a lot of time and efforts to make loading condition reflecting these demands. In this study, PSO (Particle Swarm Optimization) is used to make loading condition to be satisfied the criteria. Study result is applied 'CROSSWAY (160,000 DWT Crude Oil Tanker)' in NAPA. The result shows that satisfy the criteria and other constraints and limitation.

• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.72-77
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• 2007

A game starting, the process of running has some loading steps. In general, textures use corresponding images saved externally and the file format of images affects the loading time of game. We propose some ideas for the texture using method by comparison with loading time according to some file formats.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.3
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• pp.367-372
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• 2018

The objective of this paper is to optimize the cross-section of aluminum decking units used in the bass boats under operating conditions, and to verify the optimized model from the results via by ANSYS software. Aluminum decking unit is needed to endure specific loading while leisure activity and sailing. For a stiffer and more cost-neutral aluminum decking unit, optimization is often considered in the naval and marine industries. This optimization of the aluminum decking unit is performed using the ANSYS program, which is based on the topology optimization method. The generation of finite element models and stress evaluations are conducted using the ANSYS Multiphysics module, which is based on the Finite Element Method (FEM). Through such a series of studies, it was possible to determine the most suitable case for satisfying the structural strength found among the phase-optimized aluminum deck units in bass boats. From these optimization results, CASE 1 shows the best solution in comparison with the other cases for this optimization. By linking the topology optimization with the structural strength analysis, the optimal solution can be found in a relatively short amount of time, and these procedures are expected to be applicable to many fields of engineering.

• Computational Structural Engineering
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• v.9 no.3
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• pp.179-186
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• 1996

A simple nonlinear programming(NLP) formulation for the optimal topology problem of structures is developed and examined. The NLP formulation is general, and can handle arbitrary objective functions and arbitrary stress, displacement constraints under multiple loading conditions. The formulation is based on simultaneous analysis and design approach to avoid stiffness matrix singularity resulting from zero sizing variables. By embedding the equilibrium equations as equality constraints in the nonlinear programming problem, we avoid constructing and factoring a system stiffness matrix, and hence avoid its singularity. The examples demonstrate that the formulation is effective for finding an optimal solution, and shown to be robust under a variety of constraints.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.490-498
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• 2013

This paper discusses the application of evolutionary multi-objective optimization algorithms namely Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Modified NSGA-II (MNSGA-II) for solving the Combined Economic Emission Dispatch (CEED) problem with valve-point loading. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a non-smooth optimization problem. IEEE 57-bus and IEEE 118-bus systems are taken to validate its effectiveness of NSGA-II and MNSGA-II. To compare the Pareto-front obtained using NSGA-II and MNSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Furthermore, three different performance metrics such as convergence, diversity and Inverted Generational Distance (IGD) are calculated for evaluating the closeness of obtained Pareto-fronts. Numerical results reveal that MNSGA-II algorithm performs better than NSGA-II algorithm to solve the CEED problem effectively.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.423-432
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• 2014

Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is applied for solving Combined Economic Emission Dispatch (CEED) problem with valve-point loading of thermal generators. This CEED problem with valve-point loading is a nonlinear, constrained multi-objective optimization problem, with power balance and generator capacity constraints. The valve-point loading introduce ripples in the input-output characteristics of generating units and make the CEED problem as a nonsmooth optimization problem. To validate its effectiveness of NSGA-II, two benchmark test systems, IEEE 30-bus and IEEE 118-bus systems are considered. To compare the Pareto-front obtained using NSGA-II, reference Pareto-front is generated using multiple runs of Real Coded Genetic Algorithm (RCGA) with weighted sum of objectives. Comparison with other optimization techniques showed the superiority of the NSGA-II approach and confirmed its potential for solving the CEED problem. Numerical results show that NSGA-II algorithm can provide Pareto-front in a single run with good diversity and convergence. An approach based on Technique for Ordering Preferences by Similarity to Ideal Solution (TOPSIS) is applied on non-dominated solutions obtained to determine Best Compromise Solution (BCS).

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.467-475
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• 2009

Congestion management is one of the technical challenges in power system deregulation. This paper presents single objective and multi-objective optimization approaches for optimal choice, location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in deregulated power system to improve branch loading (minimize congestion), improve voltage stability and reduce line losses. Though FACTS controllers offer many advantages, their installation cost is very high. Hence Independent System Operator (ISO) has to locate them optimally to satisfy a desired objective. This paper presents optimal location of FACTS controllers considering branch loading (BL), voltage stability (VS) and loss minimization (LM) as objectives at once using GA. It is observed that the locations that are most favorable with respect to one objective are not suitable locations with respect to other two objectives. Later these competing objectives are optimized simultaneously considering two and three objectives at a time using multi-objective Strength Pareto Evolutionary Algorithms (SPEA). The developed algorithms are tested on IEEE 30 bus system. Various cases like i) uniform line loading ii) line outage iii) bilateral and multilateral transactions between source and sink nodes have been considered to create congestion in the system. The developed algorithms show effective locations for all the cases considered for both single and multiobjective optimization studies.

• Journal of Ocean Engineering and Technology
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• v.24 no.1
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• pp.153-160
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• 2010

This paper deals with the topology optimized design of the riser support structures for floating production storage and offloading units (FPSOs) under global and local loading conditions. For a preliminary study and validation of the numerical approach, a simplified plate under static loading is first evaluated with the representative topology optimization methods, the Homogenization Design Method (HDM) and Density Method (DM) or Simple Isotropic Material with Penalization (SIMP). In the context of the corresponding riser support structures, the design problem is formulated such that structure shapes based on design domain variables are determined by minimizing the compliance subject to a mass target, considering the stress criterion. An initial design model is generated based on an actual FPSO riser support configuration. The topology optimization results present improved design performances under various loading conditions, while staying within the allowable limit of the offshore area.

• Structural Engineering and Mechanics
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• v.7 no.4
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• pp.401-412
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• 1999

The purpose of this paper is to show how the Evolutionary Structural Optimization (ESO) algorithm developed by Xie and Steven can be extended to optimal design problems of thin shells subjected to thermal loading. This extension simply incorporates an evolutionary iterative process of thermoelastic thin shell finite element analysis. During the evolution process, lowly stressed material is gradually eliminated from the structure. This paper presents a number of examples to demonstrate the capabilities of the ESO algorithm for solving topology optimization and thickness distribution problems of thermoelastic thin shells.