• 한국전산구조공학회논문집
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• 제16권3호
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• pp.281-290
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• 2003

Co Evolutionary Structural Design(CESD) Framework is presented, which can deal with the load design and structural topology design simultaneously. The load design here is the exploration algorithm that finds the critical load patterns of the given structure. In general, the load pattern is a crucial factor in determining the structural topology and being selected from the experts어 intuition and experience. However, if any of the critical load patterns would be excluded during the process of problem formation, the solution structure might show inadequate performance under the load pattern. Otherwise if some reinforcement method such as safety factor method would be utilized, the solution structure could result in inefficient conservativeness. On the other hand, the CESD has the ability of automatically finding the most critical load patterns and can help the structural solution evolve into the robust design. The CESD is made up of a load design discipline and a structural topology design discipline both of which have the fully coupled relation each other. This coupling is resolved iteratively until the resultant solution can resist against all the possible load patterns and both disciplines evolve into the solution structure with the mutual help or competition. To verify the usefulness of this approach, the 10 bar truss and the jacket type offshore structure are presented. SORA(Sequential Optimization & Reliability Assessment) is adopted in CESD as a probabilistic optimization methodology, and its usefulness in decreasing the computational cost is verified also.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 추계학술대회논문집A
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• pp.699-704
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• 2000

The present paper deals with a multiobjective optimization method based on the co-evolutionary genetic strategy. The co-evolutionary strategy carries out the multiobjective optimization in such way that it optimizes individual objective function as compared with each generation's value while there are more than two genetic evolutions at the same time. In this study, the designs that are out of the given constraint map compared with other objective function value are excepted by the penalty. The proposed multiobjective genetic algorithms are distinguished from other optimization methods because it seeks for the optimized value through the simultaneous search without the help of the single-objective values which have to be obtained in advance of the multiobjective designs. The proposed strategy easily applied to well-developed genetic algorithms since it doesn't need any further formulation for the multiobjective optimization. The paper describes the co-evolutionary strategy and compares design results on the simple structural optimization problem.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2003년도 봄 학술발표회 논문집
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• pp.335-347
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• 2003

본 논문에서는 설계 하중에 지배되는 구조물에 있어서, 입력 파라미터들의 불확실성을 표준편차와 패턴의 변동, 두 차원에서 접근, 처리할 수 있는 방안을 제시하기 위해서 구조물에 입력으로 작용하는 하중 패턴의 결정과 구조물의 형상의 진화를 동시에 고려할 수 있는 Co-Evolutionary Structural Design framework라 명명한 새로운 구조 설계 방식을 개발하였다. 공학자의 직관과 경험 의존적인 하중을 대상으로 최적화된 구조물은, 성능에 완벽한 안전을 보장해 줄 수 없으며, 이에 관한 문제를 해결하기 위해서 주어진 상황 속에서 다양한 하중이 작용하더라도 안전할 수 있는 구조물의 설계 방식에 관해서 설명한다. 본 프레임워크는 연성을 가지는 두 Disciplinary Modules, 즉 구조 형상설계와 하중설계로 이루어지며 하중에 관한 DB로 연결되어 순차적인 MDO 설계과정을 거치게 된다. 두 Discipline은 설계과정을 거치면서 상호 견제의 틀 속에서 진화하며 기존 방식과 달리 극한 하중 패턴을 스스로 찾아서 설계 반영하는 특징을 가진다. 본 접근 방식의 유용성을 평가하기 위해서 10-bar truss 구조물과 Jacket-Type 구조물로 테스트해 보았다.

• 한국지진공학회논문집
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• 제15권2호
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• pp.1-9
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• 2011

불확실성을 가지는 하중의 변동성을 고려한 구조제어시스템의 최적설계방법에 관하여 연구하였다. 일반적인 제어시스템의 설계 문제가 구조물과 제어시스템간의 상호작용 고려하여 구조-제어 시스템을 최적화이나, 이 연구에서는 하중-구조물-제어 시스템간의 상호작용에 대한 최적설계방법에 관하여 다루었다. 구조물의 응답을 최대화하는 하중과, 이를 최소화하는 구조제어시스템을 동시에 구하는 최대-최소문제(Min-max Problem)를 정식화하고, 최적설계변수를 효율적으로 찾는 방법으로 병렬진화 알고리즘을 이용하여 불확실성이 존재하는 선형구조제어시스템의 최적설계방법을 제시하였다. 지진하중을 받는 구조물의 제진시스템 설계 예 및 수치해석을 통하여 연구한 방법의 타당성을 검증하였다.

• Steel and Composite Structures
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• 제15권4호
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• pp.451-466
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• 2013

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.

• 대한조선학회논문집
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• 제48권4호
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• pp.325-329
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• 2011

Floating, production, storage and offloading (FPSO) is a production facility that refines and saves the drilled crude oil from a drilling facility in the ocean. The flare system in the FPSO is a major part of the pressure relieving system for hydrocarbon processing plants. The flare system consists of a number of pipes and complicated connection systems. Decision of pipe support types is important since the load on the support and the stress in the pipe are influenced by the pipe support type. In this study, we optimally determined the pipe support types that minimized the support cost while satisfying the design constraints on maximum support load, maximum nozzle load and maximum pipe stress ratio. Performance indices included in the design constraints for a specified design were evaluated by pipe structural analysis using CAESAR II. Since pipe support types were all discrete design variables, an evolutionary algorithm (EA) was used as an optimizer. We successfully obtained the optimal solution that reduced the support cost by 27.2% compared to the initial support cost while all the design requirements were satisfied.

• Structural Engineering and Mechanics
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• 제11권5호
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• pp.485-502
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• 2001

Modem soft computing methods, such as neural networks, evolutionary models and fuzzy logic, are mainly inspired by the problem solving strategies the biological systems use in nature. As such, the soft computing methods are fundamentally different from the conventional engineering problem solving methods, which are based on mathematics. In the author's opinion, these fundamental differences are the key to the full understanding of the soft computing methods and in the realization of their full potential in engineering applications. The main theme of this paper is to discuss the fundamental differences between the soft computing methods and the mathematically based conventional methods in engineering problems, and to explore the potential of soft computing methods in new ways of formulating and solving the otherwise intractable engineering problems. Inverse problems are identified as a class of particularly difficult engineering problems, and the special capabilities of the soft computing methods in inverse problems are discussed. Soft computing methods are especially suited for engineering design, which can be considered as a special class of inverse problems. Several examples from the research work of the author and his co-workers are presented and discussed to illustrate the main points raised in this paper.

• Structural Engineering and Mechanics
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• 제69권1호
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• pp.21-31
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• 2019

Optimizing composite structures to exploit their maximum potential is a realistic application with promising returns. In this research, simultaneous maximization of the fundamental frequency and frequency separation between the first two modes by optimizing the fiber angles is considered. A high-fidelity design optimization methodology is developed by combining the high-accuracy of finite element method with iterative improvement capability of metaheuristic algorithms. Three powerful nature-inspired optimization algorithms viz. a genetic algorithm (GA), a particle swarm optimization (PSO) variant and a cuckoo search (CS) variant are used. Advanced memetic features are incorporated in the PSO and CS to form their respective variants-RPSOLC (repulsive particle swarm optimization with local search and chaotic perturbation) and CHP (co-evolutionary host-parasite). A comprehensive set of benchmark solutions on several new problems are reported. Statistical tests and comprehensive assessment of the predicted results show CHP comprehensively outperforms RPSOLC and GA, while RPSOLC has a little superiority over GA. Extensive simulations show that the on repeated trials of the same experiment, CHP has very low variability. About 50% fewer variations are seen in RPSOLC as compared to GA on repeated trials.

• 한국식품위생안전성학회지
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• 제36권5호
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• pp.363-375
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• 2021

COVID-19와 같은 전염병 감염 시나리오 전반에 걸쳐 펩타이드 기반 치료법을 발견하고 설계하는 개발 시대의 추세는 보다 효율적이고 저렴한 치료 환경으로 발전할 수 있습니다. 결과적으로, 그들의 단백질 분해 약화는 천연펩타이드 약물의 단점 중 하나입니다. 펩티도미메틱스는 이 단점을 해결하는 데 도움이 될 수 있습니다. 이 리뷰에서 펩타이드 및 펩타이드 기반 약물 발견은 숙주 안지오텐신 전환 효소-2(ACE2) 수용체 및 바이러스 스파이크 (S)단백질의 연관성을 포함하는 중증 코로나바이러스 폐색전 증후군(SARS-CoV-2)의 주요 진입 기전 중 하나를 표적으로 요약했습니다. 또한, 펩타이드 기반의 새로운 치료법을 통해 COVID-19에 대해 연구된 단백질, 펩타이드 및 기타 가능한 조치의 이점을 다룹니다. 그리고 펩타이드 기반 약물 치료 환경의 개요는 진화적 관점, 구조적 특성, 작동 한계값 및 치료 영역에 대한 설명으로 구성된다