• Steel and Composite Structures
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• 제47권2호
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• pp.147-165
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• 2023

The paper contrasts conventional seismic design with a design that incorporates buckling-restrained bracing in three-dimensional reinforced concrete buildings (BRBs). The suboptimal structures may be found using the multi-objective chimp optimization algorithm (MEN-ChOA). Given the constraints and dimensions, ChOA suffers from a slow convergence rate and tends to become stuck in local minima. Therefore, the ChOA is improved by niching and evolutionary operators to overcome the aforementioned problems. In addition, a new technique is presented to compute seismic and dead loads that include all of a structure's parts in an algorithm for three-dimensional frame design rather than only using structural elements. The performance of the constructed multi-objective model is evaluated using 12 standard multi-objective benchmarks proposed in IEEE congress on evolutionary computation. Second, MEN-ChOA is employed in constructing several reinforced concrete structures by the Mexico City building code. The variety of Pareto optimum fronts of these criteria enables a thorough performance examination of the MEN-ChOA. The results also reveal that BRB frames with comparable structural performance to conventional moment-resistant reinforced concrete framed buildings are more cost-effective when reinforced concrete building height rises. Structural performance and building cost may improve by using a nature-inspired strategy based on MEN-ChOA in structural design work.

• Journal of the Korean Data and Information Science Society
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• 제24권3호
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• pp.495-504
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• 2013

진화 알고리즘 계산 지능을 이용한 예측 방법이 다목적 최적화 문제에서 많이 이용되고 있고, 이러한 방법들은 많은 근사 파레토 최적해들을 좀 더 정확하게 생성하기 위해서 개선되고 있다. 본 논문은 다목적 최적화 문제에서 서포트 벡터기계를 이용하여 근사 파레토 프런티어를 찾는 방법을 제안한다. 또한 제안된 방법과 진화 알고리즘을 결합한 것이 파레토 프런티어를 더 잘 근사시킨다는 것과 두 개혹은 세 개의 목적함수를 가진 의사결정은 제안된 방법으로 파레토 프런티어를 시각화한 것에 근거하여 더 쉽게 수행된다는 것을 보인다. 마지막으로 몇 개의 수치예제를 통해 제안된 방법의 효율성에 대해 보일 것이다.

• Ocean Systems Engineering
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• 제5권2호
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• pp.91-107
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• 2015

Ship hull optimization is categorized as a bound, multi variable, multi objective problem with nonlinear constraints. In such analysis, where the objective function representing the performance of the ship generally requires computationally involved hydrodynamic interaction evaluation methods, the objective functions are not smooth. Hence, the evolutionary techniques to attain the optimum hull forms is considered as the most practical strategy. In this study, a parametric ship hull form represented by B-Spline curves is optimized for multiple performance criteria using Genetic Algorithm. The methodology applied to automate the hull form generation, selection of optimization solvers and hydrodynamic parameter calculation for objective function and constraint definition are discussed here.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.93.3-93
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• 2002

$\textbullet$ Co-evolutionary algorithms $\textbullet$ Nash Genetic Algorithms $\textbullet$ Multi-objective Optimization $\textbullet$ Distance dependent mutation $\textbullet$ Pareto Optimality

• 한국지능시스템학회논문지
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• 제14권3호
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• pp.253-261
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• 2004

게임 이론은 의사 결정 문제와 관련 된 연구와 함께 정립 된 수학적 분석법으로써 1928년 Von Neumann이 유한개의 순수전략이 존재하는 2인 영합게임은 결정적(deterministic)이라는 것을 증명함으로써 수학적 기반을 정립하였고 50년대 초, Nash는 Von Neumann의 이론을 일반화하는 개념을 제안함으로써 현대적 게임이론의 장을 열었다. 이후 진화 생물학 연구자들에 의해 고전적인 게임 이론의 가정에 해당하는 참가자들의 합리성(rationality) 대신 다윈 선택(Darwinian selection)에 의해 게임의 해를 탐색하는 것이 가능하다는 것이 밝혀지게 되었고 진화 생물학자 Maynard Smith에 의해 진화적 안정 전략(Evolutionary Stable Strategy: ESS)의 개념이 정립되면서 현대적 게임 이론으로써 진화적 게임 이론이 체계화 되었다. 한편 이와 같은 진화적 게임 이론에 관한 연구와 함께 생태계의 공진화를 이용한 컴퓨터 시뮬레이션이 1991년 Hillis에 의해 처음으로 시도되었으며 Kauffman은 다른 종들 간의 공진화적 동역학(dynamics)을 분석하기 위한 NK 모델을 제안하였다. Kauffman은 이 모델을 이용하여 공진화 현상이 어떻게 정적 상태(static state)에 이르며 이 상태들은 게임 이론에서 소개되어진 내쉬 균형이나 ESS에 해당한다는 것을 보여주었다. 이후, 몇몇 연구자들 게임 이론과 진화 알고리즘에 기반한 연산 모델들을 제시해 왔으나 실용적인 문제의 적용에 대한 연구는 아직 미흡한 편이다. 이에 본 논문에서는 게임 이론에 기반 한 공진화 알고리즘을(Game theory based Co-Evolutionary Algorithm: GCEA) 제안하고 이 알고리즘을 이용하여 공진화적인 문제들을 효과적으로 해결할 수 있음을 확인하는 것을 목표로 한다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2011년도 춘계학술대회
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• pp.216-217
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• 2011

The problem of automated stacking cranes (ASC) dispatching in container terminals is addressed in this paper. We propose a heuristic-based ASC dispatching approach which adopts multi-criteria decision strategy. By aggregating different criteria the proposed strategy can consider multiple aspects of the dispatching situation and make robust decision in various situations. A multi-objective evolutionary algorithm (MOEA) is adopted to tune the weights associated to each criteria to minimize both the quay crane delay and external truck delay. The proposed approach is validated by comparison with different dispatching heuristics and simulation results obtained confirms its effectiveness.

• Advances in Computational Design
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• 제6권1호
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• pp.31-42
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• 2021

This research aims to demonstrate the optimal geometrical design of splayed multiple cross-sectional pin fin heat sinks (SMCSPFHS), which are a type of side-inlet-side-outlet heat sink (SISOHS). The optimiser strength Pareto evolutionary algorithm2 (SPEA2)is employed to explore a set of Pareto optimalsolutions. Objective functions are the fan pumping power and junction temperature. Function evaluations can be accomplished using computational fluid dynamics(CFD) analysis. Design variablesinclude pin cross-sectional areas, the number of fins, fin pitch, thickness of heatsink base, inlet air speed, fin heights, and fin orientations with respect to the base. Design constraints are defined in such a way as to make a heat sink usable and easy to manufacture. The optimum results obtained from SPEA2 are compared with the straight pin fin design results obtained from hybrid population-based incremental learning and differential evolution (PBIL-DE), SPEA2, and an unrestricted population size evolutionary multiobjective optimisation algorithm (UPSEMOA). The results indicate that the splayed pin-fin design using SPEA2 issuperiorto those reported in the literature.

• Journal of Power Electronics
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• 제12권1호
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• pp.104-112
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• 2012

In this paper a new method for the design of a simple PI controller is presented and it has been applied in the control of a Boost based PFC rectifier. The Strength Pareto evolutionary algorithm, which is based on the Pareto Optimality concept, used in Game theory literature is implemented as a multi-objective optimization approach to gain a good transient response and a high quality input current. In the proposed method, the input current harmonics and the dynamic response have been assumed as objective functions, while the PI controller's gains of the PFC rectifier (Kpi, Tpi) are design variables. The proposed algorithm generates a set of optimal gains called a Pareto Set corresponding to a Pareto Front, which is a set of optimal results for the objective functions. All of the Pareto Front points are optimum, but according to the design priority objective function, each one can be selected. Simulation and experimental results are presented to prove the superiority of the proposed design methodology over other methods.

• International Journal of Fluid Machinery and Systems
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• 제9권3호
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• pp.265-276
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• 2016

Energy systems working coherently in different conditions may not have a specific design which can provide optimal performance. A system working for a longer period at lower efficiency implies higher energy consumption. In this effort, a methodology demonstrated by a jet pump design and optimization via numerical modeling for fluid dynamics and implementation of an evolutionary algorithm for the optimization shows a reduction in computational costs. The jet pump inherently has a low efficiency because of improper mixing of primary and secondary fluids, and multiple momentum and energy transfer phenomena associated with it. The high fidelity solutions were obtained through a validated numerical model to construct an approximate function through surrogate analysis. Pareto-optimal solutions for two objective functions, i.e., secondary fluid pressure head and primary fluid pressure-drop, were generated through a multi-objective genetic algorithm. For the jet pump geometry, a design space of several design variables was discretized using the Latin hypercube sampling method for the optimization. The performance analysis of the surrogate models shows that the combined surrogates perform better than a single surrogate and the optimized jet pump shows a higher performance. The approach can be implemented in other energy systems to find a better design.

• Journal of Electrical Engineering and Technology
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• 제4권4호
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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.