• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.335-352
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• 2023

When solving multi-objective optimization problems, the blindness of the evolution direction of the population gradually emerges with the increase in the number of objectives, and there are also problems of convergence and diversity that are difficult to balance. The many- objective optimization problem makes some classic multi-objective optimization algorithms face challenges due to the huge objective space. The sine cosine algorithm is a new type of natural simulation optimization algorithm, which uses the sine and cosine mathematical model to solve the optimization problem. In this paper, a knee-driven many-objective sine-cosine algorithm (MaSCA-KD) is proposed. First, the Latin hypercube population initialization strategy is used to generate the initial population, in order to ensure that the population is evenly distributed in the decision space. Secondly, special points in the population, such as nadir point and knee points, are adopted to increase selection pressure and guide population evolution. In the process of environmental selection, the diversity of the population is promoted through diversity criteria. Through the above strategies, the balance of population convergence and diversity is achieved. Experimental research on the WFG series of benchmark problems shows that the MaSCA-KD algorithm has a certain degree of competitiveness compared with the existing algorithms. The algorithm has good performance and can be used as an alternative tool for many-objective optimization problems.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.2
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• pp.9-22
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• 2008

This paper presents an integrated optimal design technique of a hybrid structure-damper system for improving the seismic performance of the structure. The proposed technique corresponds to the optimal distribution of the stiffness and dampers. The multi-objective optimization technique is introduced to deal with the optimal design problem of the hybrid system, which is reformulated into the multi-objective optimization problem with a constraint of target reliability in an efficient manner. An illustrative example shows that the proposed technique can provide a set of Pareto optimal solutions embracing the solutions obtained by the conventional sequential design method and single-objective optimization method based on weighted summation scheme. Based on the stiffness and damping capacities, three representative designs are selected among the Pareto optimal solutions and their seismic performances are investigated through the parametric studies on the dynamic characteristics of the seismic events. The comparative results demonstrate that the proposed approach can be efficiently applied to the optimal design problem for improving the seismic performance of the structure.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.750-769
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• 2015

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.409-416
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• 2009

This paper presents a tool called Quality-Cost optimization system (QCOS), which integrates Multi-Objective Genetic Algorithm (MOGA) and Quality Function Deployment (QFD), for tradeoff between quality and cost of the unitized metal curtain-wall unit. A construction owner as the external customer pursues to maximize the quality of the curtain-wall unit. However, the contractor as the internal customer pursues to minimize the cost involved in designing, manufacturing and installing the curtain-wall unit. It is crucial for project manager to find the tradeoff point which satisfies the conflicting interests pursued by the both parties. The system would be beneficial to establish a quality plan satisfying the both parties. Survey questionnaires were administered to the construction owner who has an experience of curtain-wall project, the architects who are the independent assessor, and the contractors who were involved in curtain-wall design and installation. The Customer Requirements (CRs) and their importance weights, the relationship between CRs and Technical Attributes (TAs) consisting of a curtain-wall unit, and the cost ratios of each components consisting curtain-wall unit are obtained from the three groups mentioned previously. The data obtained from the surveys were used as the QFD input to compute the Owner Satisfaction (OS) and Contractor Satisfaction (CS). MOGA is applied to optimize resource allocation under limited budget when multi-objectives, OS and CS, are pursued at the same time. The deterministic multi-objective optimization method using MOGA and QFD is extended to stochastic model to better deal with the uncertainties of QFD input and the variability of QFD output. A case study demonstrates the system and verifies the system conformance.

• Structural Engineering and Mechanics
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• v.63 no.3
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• pp.303-315
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• 2017

This paper presents a review on getting a Weighted Multi-Objective Optimization (WMO) of Tuned Mass Damper (TMD) parameters based on Response Surface Methodology (RSM) coupled central composite design and Weighted Desirability Function (WDF) to attenuate the earthquake vibration of a jacket supported Offshore Wind Turbine (OWT). To optimize the parameters (stiffness and damping coefficient) of damper, the frequency ratio and damping ratio were considered as a design variable and the top displacement and frequency response were considered as objective functions. The optimization has been carried out under only El Centro earthquake results and after obtained the optimal parameters, more two earthquakes (California and Northridge) has been performed to investigate the performance of optimal damper. The obtained results also compared with the different conventional TMD's designed by Den Hartog's, Sadek et al.'s and Warburton's method. From the results, it was found that the optimal TMD based on RSM shows better response than the conventional damper. It is concluded that the proposed response model offers an efficient approach regarding the TMD optimization.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.614-622
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• 2018

Based on army operating road profile, the endurance test of military vehicle aims to reproduce the similar loading conditions with mixture of proving ground tracks. It is so called as endurance test mode and its optimal generation is important to meet high reliability of endurance test. In this paper, proving ground optimization is proposed to achieve a close match to the target profile. Several performance measures such as torque-revolution counts or transmission ratio for the powertrain system can be considered as one of the objective functions. However, the one-side optimal endurance test mode may give the poor solution in the whole system point of view. To incorporate several goals simultaneously, this paper employs multi-objective optimization technique to generate endurance test mode. One of the most widely used method, weighted-sum method is applied here and the case study is discussed.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.10 s.241
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• pp.1163-1171
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• 2005

The shape optimization of an airfoil has been performed for an incompressible viscous flow. In this study, Pareto frontier sets, which are global and non-dominated solutions, can be obtained without various weighting factors by using the multi-objective genetic algorithm An NACA0012 airfoil is considered as a baseline model, and the profile of the airfoil is parameterized and rebuilt with four Bezier curves. Two curves, front leading to maximum thickness, are composed of five control points and the rest, from maximum thickness to tailing edge, are composed of four control points. There are eighteen design variables and two objective functions such as the lift and drag coefficients. A generation is made up of forty-five individuals. After fifteenth evolutions, the Pareto individuals of twenty can be achieved. One Pareto, which is the best of the . reduction of the drag furce, improves its drag to $13\%$ and lift-drag ratio to $2\%$. Another Pareto, however, which is focused on increasing the lift force, can improve its lift force to $61\%$, while sustaining its drag force, compared to those of the baseline model.

• International Journal of Automotive Technology
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• v.8 no.3
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• pp.337-342
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• 2007

A multi-objective optimization technique was implemented to obtain optimal topologies of the inner reinforcement for a vehicle's hood simultaneously considering the static stiffness of bending and torsion and natural frequency. In addition, a smoothing scheme was used to suppress the checkerboard patterns in the ESO method. Two models with different curvature were chosen in order to investigate the effect of curvature on the static stiffness and natural frequency of the inner reinforcement. A scale factor was employed to properly reflect the effect of each objective function. From several combinations of weighting factors, a Pareto-optimal topology solution was obtained. As the weighting factor for the elastic strain efficiency went from 1 to 0, the optimal topologies transmitted from the optimal topology of a static stiffness problem to that of a natural frequency problem. It was also found that the higher curvature model had a larger static stiffness and natural frequency than the lower curvature model. From the results, it is concluded that the ESO method with a smoothing scheme was effectively applied to topology optimization of the inner reinforcement of a vehicle's hood.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.495-504
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• 2013

Evolutionary algorithms have been applied to multi-objective optimization problems by approximation methods using computational intelligence. Those methods have been improved gradually in order to generate more exactly many approximate Pareto optimal solutions. The paper introduces a new method using support vector machine to find an approximate Pareto frontier in multi-objective optimization problems. Moreover, this paper applies an evolutionary algorithm to the proposed method in order to generate more exactly approximate Pareto frontiers. Then a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by the proposed method. Finally, a few examples will be demonstrated for the effectiveness of the proposed method.