• 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).

• Structural Engineering and Mechanics
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• 제84권4호
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.

• 대한임베디드공학회논문지
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• 제19권2호
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• 한국생산제조학회지
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• 제19권6호
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• pp.826-833
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• 2010

Electric-thermal-structural actuated compliant mechanisms are mechanisms onto which electric voltage drop is applied as input instead of force. This mechanism is based on thermal expansion of material while being heated. Compliant mechanisms are designed subjected to electric charge input using BESO(bi-directional evolutionary structural optimization) method. Reliability-based topology optimization (RBTO) is applied to the topology design of actuators. performance measure approach (PMA), which has probabilistic constraints that are formulated in terms of the reliability index, is adopted to evaluate the probabilistic constraints. In this study, BESO method is used to obtain optimal topology of compliant mechanisms from initial design domain. PMA approach is used to evaluate reliability index. The procedure has been tested in numerical applications and compared with the results obtained by other methods to validate these approaches.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.651-652
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• 2008

This research presents a multi-objective optimal design employing topological approach to maximize magnetic energy while minimizing structural deformation which is caused by magnetic reluctance force and magnetostriction. A design sensitivity formula is derived by employing the adjoint variable method (AVM) to avoid numerous sensitivity evaluations for a coupled magneto-mechanical analysis. The sensitivity analysis is verified using the finite difference method (FDM) in a C-shape actuator. A linear actuator used in a home appliance is examined for optimal design and demonstrates the strength of the proposed topology optimization approach.

• 한국방재학회 논문집
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• 제10권1호
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• pp.73-80
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• 2010

상수도관망의 최적설계는 단목적함수와 고정된 수리학적 변수로 구성된 비용최소화의 문제로 시작되었다. 하지만, 미래의 불확실한 수요량의 변동과 같이 상수도관망 내에 존재하는 여러 불확실성을 고려하여 설계하는 것이 실제 상수도관망의 거동을 보다 적절히 예측하는 것이다. 따라서 상수도관망 내 존재하는 불확실성을 양적으로 고려하는 다양한 방법이 연구되어 상수도관망의 최적설계에 반영되었고, 다목적함수를 사용한 최적화문제도 다루게 되었다. 본 연구에서는 관망의 절점에서의 수요량과 관의 조도계수를 불확실성을 가진 변수로 두고, 비용 최소화와 관망의 강건성 (Robustness)을 최대화 하는 두 가지 목적함수를 가진 다목적함수 최적화 문제를 다루었다. 최적화 과정은 비용최소화와 불확실성을 고려한 최종 최적화의 두 과정으로 나뉜다. 각 절점에서의 수요량과 관의 조도계수는 베타확률밀도함수 (Beta PDF)를 사용, Latin Hypercube 샘플링 방법으로 불확실성을 고려하였고, 다목적함수의 최적화는 유전자 알고리듬 (Multi-objective Genetic Algorithms, MOGA)을 사용하였다. 제안된 방법은 New York Tunnels이라는 실제 상수도관망에 적용하여 적용성을 검증 하였고 그 결과를 분석하였다. 다목적 최적화 문제에서 최적화가 진행될 수 록 초기 값에 모여 있던 점들이 그 점 주위를 시작으로 해 공간에 최적 해를 찾아 오른쪽 아래 부분으로 탐색해 나가는 것을 확인할 수 있었고 최적설계의 해는 해 공간에서 Pareto Front를 구성하며 파레토 최적해를 구하였다.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2006년도 학술발표회 논문집
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• pp.401-408
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• 2006

An optimum design method for hybrid control systems is proposed in this study. By considering both active and passive control systems as a combined or a hybrid system, the optimization of the hybrid system can be achieved simultaneously. In the proposed approach, we consider design parameters of active control devices and the elements of the feedback gain matrix as design variables for the active control system. Required quantity of the added dampers are also treated as design variables for the passive control system. In the proposed method, the cost of both active and passive control devices, the required control efforts and dynamic responses of a target structure are selected as objective functions to be minimized. To effectively address the multi-objective optimization problem, we adopt a preference-based optimization model and apply a genetic algorithm as a numerical searching technique. As an example to verify the validity of the proposed optimization technique, a wind-excited 20-storey building with hybrid control systems is used and the results are presented.

• Steel and Composite Structures
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• 제17권5호
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• pp.705-719
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• 2014

Composite steel-concrete box girders are frequently used in bridge construction for their economic and structural advantages. An integrated metaheuristic based optimization procedure is proposed for discrete size optimization of straight multi-span steel box girders with the objective of minimizing the self-weight of girder. The metaheuristic algorithm of choice is the Cuckoo Search (CS) algorithm. The optimum design of a box girder is characterized by geometry, serviceability and ultimate limit states specified by the American Association of State Highway and Transportation Officials (AASHTO). Size optimization of a practical design example investigates the efficiency of this optimization approach and leads to around 15% of saving in material.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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• pp.2690-2695
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• 2007

This work presents numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis. Reynolds-averaged Navier-Stokes (RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.