• Advances in Computational Design
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• 제1권1호
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• pp.87-103
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• 2016

This paper adopts and investigates the non-dominated sorting approach for extending the single-objective Cuckoo Search (CS) into a multi-objective framework. The proposed approach uses an archive composed of primary and secondary population to select and keep the non-dominated solutions at each generation instead of pairwise analogy used in the original Multi-objective Cuckoo Search (MOCS). Our simulations show that such a low computational complexity approach can enrich CS to incorporate multi-objective needs instead of considering multiple eggs for cuckoos used in the original MOCS. The proposed MOCS is tested on a set of multi-objective optimization problems and two well-studied engineering design optimization problems. Compared to MOCS and some other available multi-objective algorithms such as NSGA-II, our approach is found to be competitive while benefiting simplicity. Moreover, the proposed approach is simpler and is capable of finding a wide spread of solutions with good coverage and convergence to true Pareto optimal fronts.

• 한국정보통신학회논문지
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• 제16권1호
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• pp.65-71
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• 2012

순서통계에 근거한 CFAR(Constant False Alarm) 검파기(이하 OS-CFAR)는 다중 타깃(Target) 환경의 차량용 레이더에 아주 유용 사용되는 알고리즘이다. 그러나 정렬 알고리즘을 사용하기 때문에 일반적인 셀-평균 CFAR 검파기(이하 CA-CFAR)에 비해 계산량이 많아 실시간 구현에 어려운 점이 있다. 본 논문에서는 보다 낮은 계산량을 가지는 OS-CFAR 구조를 제안하였다. 제안된 방법에서는 정렬 알고리즘이 단 한번 만 수행되기 때문에 이를 통해 많은 계산량을 줄일 수 있다. 특히 고속 정렬 알고리즘을 사용하는 경우 통상적인 OS-CFAR 구조와 비교하여 데이터양에 상관없이 항상 계산속도가 빠름을 확인 할 수 있다. 또한 본 논문에서는 실제 레이더 수신 데이터를 이용하여 제안된 방법에 적용한 결과도 제시하였다.

• 한국정보과학회논문지:시스템및이론
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• 제36권2호
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• pp.68-72
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• 2009

접미사 배열은 주어진 문자열 내의 모든 접미사를 사전식 순서로 저장하는 자료 구조로, 많은 저장 공간을 사용하는 접미사 트리를 대체하면서 여러 가지 문자열 관련 문제에 사용되고 있다. 이를 O(n) 시간 내에 생성하는 것과 더불어, 실세계 입력에 대하여 작은 시간과 공간을 사용하여 구성하는 알고리즘들 역시 제안되어 왔다. 본 논문은 Maniscalco와 Puglisi[1]가 제안한 접미사 정렬 알고리즘을 분석하고, 프로그램의 수행 시간을 개선한 새로운 알고리즘을 제안한다.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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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.

• Smart Structures and Systems
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• 제22권4호
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• pp.469-479
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• 2018

The present research study utilizes a multi-objective optimization method for Pareto optimization of an eight-degree of freedom full vehicle vibration model, adopting a non-dominated sorting genetic algorithm II (NSGA-II). In this research, a full set of ride comfort as well as ride safety parameters are considered as objective functions. These objective functions are divided in to two groups (ride comfort group and ride safety group) where the ones in one group are in conflict with those in the other. Also, in this research, a special optimizing technique and combinational method consisting of weighted sum method and Pareto optimization are applied to transform Pareto double-objective optimization to Pareto full-objective optimization which can simultaneously minimize all objectives. Using this technique, the full set of ride parameters of three dimensional vehicle model are minimizing simultaneously. In derived Pareto front, unique trade-off design points can selected which are non-dominated solutions of optimizing the weighted sum comfort parameters versus weighted sum safety parameters. The comparison of the obtained results with those reported in the literature, demonstrates the distinction and comprehensiveness of the results arrived in the present study.

• Journal of Electrical Engineering and Technology
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• 제9권2호
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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).

• Earthquakes and Structures
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• 제7권3호
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• pp.271-294
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• 2014

In recent years, along with the advances made in performance-based design optimization, the need for fast calculation of response parameters in dynamic analysis procedures has become an important issue. The main problem in this field is the extremely high computational demand of time-history analyses which may convert the solution algorithm to illogical ones. Two simplifying strategies have shown to be very effective in tackling this problem; first, simplified nonlinear modeling investigating minimum level of structural modeling sophistication, second, wavelet analysis of earthquake records decreasing the number of acceleration points involved in time-history loading. In this paper, we try to develop an efficient framework, using both strategies, to solve the performance-based multi-objective optimal design problem considering the initial cost and the seismic damage cost of steel moment-frame structures. The non-dominated sorting genetic algorithm (NSGA-II) is employed as the optimization algorithm to search the Pareto optimal solutions. The constraints of the optimization problem are considered in accordance with Federal Emergency Management Agency (FEMA) recommended design specifications. The results from numerical application of the proposed framework demonstrate the capabilities of the framework in solving the present multi-objective optimization problem.

• 한국미생물·생명공학회지
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• 제49권3호
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• pp.367-373
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• 2021

The antimicrobial resistance of Staphylococcus spp. and Gram negative strains present in air samples from waste sorting facilities was assessed. Phenotypic studies have revealed a high percentage of strains of Staphylococcus spp. resistant to methicillin. Genotypically and by RT-PCR, it was found that the mecA gene usually associated with methicillin resistance was present in 8% of the Staphylococcus strains isolated. About 30% of the Gram negative strains from the same samples also displayed resistance to meropenem and 79% of these were resistant to multiple antibiotics from different classes, namely cephalosporins and β-lactams. The results suggest that in professional activities with high levels of exposure to biological agents, the quantification and identification of the microbial flora in the work environment, with the determination of the presence of potential agents displaying multi-resistances is of relevance to the risk assessment. The personal protection of workers is particularly important relevance in these cases, since many of the strains that exhibit multi-resistance are potential opportunistic agents.

• 한국기계가공학회지
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• 제18권4호
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• pp.56-61
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• 2019

In this country, mackerel landing, sorting, and packing are mostly performed manually, which is time consuming and labor intensive. An unloading automation system saves time and labor by automating the landing, sorting, and packing processes. Casers are devices for manufacturing packing boxes for fish used by unloading automation systems. The caser design in this study is for mackerel packing boxes. This caser makes a packing box based on a press using the caser's slide crank. When the caser makes a packing box, the manufacturing sequence is determined by the caser's production guide and assisting rod. The caser design in this study is simulated using a multi-body dynamics program. The simulation is used to analyze the caser and to visualize the box-making sequence.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권4호
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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.