• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1882-1890
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• 2014

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of $NO_x$, $SO_2$, and $CO_2$ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.

• Advances in Energy Research
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• 제4권4호
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• pp.255-274
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• 2016

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

• Structural Engineering and Mechanics
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• 제47권2호
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• pp.227-245
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• 2013

The term "constructability" in regard to cast-in-place concrete construction refers mainly to the ease of reinforcing steel placement. Bar congestion complicates steel placement, hinders concrete placement and as a result leads to improper consolidation of concrete around bars affecting the integrity of the structure. In this paper, a multi-objective approach, based on the non-dominated sorting genetic algorithm (NSGA-II) is developed for optimal design of reinforced concrete cantilever retaining walls, considering minimization of the economic cost and reinforcing bar congestion as the objective functions. The structural model to be optimized involves 35 design variables, which define the geometry, the type of concrete grades, and the reinforcement used. The seismic response of the retaining walls is investigated using the well-known Mononobe-Okabe analysis method to define the dynamic lateral earth pressure. The results obtained from numerical application of the proposed framework demonstrate its capabilities in solving the present multi-objective optimization problem.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2008년도 춘계학술대회논문집
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• pp.113-116
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• 2008

This work presents numerical optimization for design of staggered arrays of dimples printed on opposite surfaces of a cooling channel with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by three non-dimensional geometric design variables composed of dimpled channel height, dimple print diameter, dimple spacing and dimple depth to maximize heat transfer rate compromising with pressure drop. Twenty designs generated by Latin hypercube sampling were evaluated by Reynolds-averaged Navier-Stokes solver and the evaluated objectives were used to construct Pareto optimal front through hybrid multi-objective evolutionary algorithm. The optimum designs were grouped by k-mean clustering technique and some of the clustered points were evaluated by flow analysis. With increase in dimple depth, heat transfer rate increases and at the same time pressure drop also increases, while opposite behavior is obtained for the dimple spacing. The heat transfer performance is related to the vertical motion of the flow and the reattachment length in the dimple.

• International Journal of Naval Architecture and Ocean Engineering
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• 제10권6호
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.

• Nuclear Engineering and Technology
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• 제56권5호
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• pp.1654-1666
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• 2024

The goal of this research is to propose a way to maximize small modular reactor (SMR) utilization to gain better market feasibility in support of carbon neutrality. For that purpose, a comprehensive tool was developed, combining off-design thermohydraulic models, economic objective models (levelized cost of electricity, annual profit), non-economic models (saved CO2), a parameter input sampling method (Latin hypercube sampling, LHS), and a multi-objective evolutionary algorithm (Non-dominated Sorting Algorithm-2, NSGA2 method) for optimizing a SMR-combined heat and power cycle (CHP) system design. Considering multiple objectives, it was shown that NSGA2+LHS method can find better optimal solution sets with similar computational costs compared to a conventional weighted sum (WS) method. Out of multiple multi-objective optimal design configurations for a 105 MWe design generation rating, a chosen reference SMR-CHP system resulted in its levelized cost of electricity (LCOE) below $60/MWh for various heat prices, showing economic competitiveness for energy market conditions similar to South Korea. Examined economic feasibility may vary significantly based on CHP heat prices, and extensive consideration of the regional heat market may be required for SMR-CHP regional optimization. Nonetheless, with reasonable heat market prices (e.g. district heating prices comparable to those in Europe and Korea), SMR can still become highly competitive in the energy market if coupled with a CHP system.

• Industrial Engineering and Management Systems
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• 제15권4호
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• pp.374-384
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• 2016

The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권7호
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• pp.3501-3523
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• 2017

It is a classical integer optimization difficulty to design an optimal selection scheme in cooperative relay networks considering co-channel interference (CCI). In this paper, we solve single-objective and multi-objective relay selection problem. For the single-objective relay selection problem, in order to attain optimal system performance of cooperative relay network, a novel quantum differential evolutionary algorithm (QDEA) is proposed to resolve the optimization difficulty of optimal relay selection, and the proposed optimal relay selection scheme is called as optimal relay selection based on quantum differential evolutionary algorithm (QDEA). The proposed QDEA combines the advantages of quantum computing theory and differential evolutionary algorithm (DEA) to improve exploring and exploiting potency of DEA. So QDEA has the capability to find the optimal relay selection scheme in cooperative relay networks. For the multi-objective relay selection problem, we propose a novel non-dominated sorting quantum differential evolutionary algorithm (NSQDEA) to solve the relay selection problem which considers two objectives. Simulation results indicate that the proposed relay selection scheme based on QDEA is superior to other intelligent relay selection schemes based on differential evolutionary algorithm, artificial bee colony optimization and quantum bee colony optimization in terms of convergence speed and accuracy for the single-objective relay selection problem. Meanwhile, the simulation results also show that the proposed relay selection scheme based on NSQDEA has a good performance on multi-objective relay selection.

• 한국구조물진단유지관리공학회 논문집
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• 제8권4호
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• pp.153-163
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• 2004

본 연구에서는 구조물의 최적설계에서 나타나는 여러 가지 불확실성을 고려하기 위해 퍼지이론을 도입한 다목적 강구조 퍼지 최적설계에 관한 연구로, 다목적 퍼지 최적설계가 단일 목적의 최적설계보다 합리적인 설계가 됨을 보이는데 목적이 있다. 최적설계에 시용된 목적함수는 구조물의 중량과 처짐을 최소화하는 다목적 함수로 취하였고, 제약조건은 하중저항계수설계법(AISC-LRFD, 1994)의 설계규준을 따랐으며, 구조해석은 기하학적인 비선형을 고려한 유한요소해석법을 이용하였다. 성질이 서로 다른 다목적을 가지는 최적문제를 다루기 위하여 소속중요도 적용법과 목적중요도 적용법을 이용하여 다양한 최적해를 구함으로써, 사용자에게 현실여건에 알맞는 합리적인 최적 해를 선택할 수 있는 가능성을 제시하였다.

• 한국수자원학회논문집
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• 제40권9호
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• pp.687-696
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• 2007

본 연구는 다목적 유전자알고리즘을 이용하여 Tank 모형의 매개변수를 추정하는데 있어서 선호적순서화(preference ordering)를 적용한 연구로써, 목적함수의 개수가 여러 개인 경우에 발생할 수 있는 파레토최적화의 단점을 해결하기 위한 것이다. 최적화를 위한 목적함수는 모두 4가지를 사용하였으며, 선호적순서화를 통해서 구한 2차 효율성(2nd order efficiency)을 가지면서 정도(degree)가 3인 4개의 해 중에서 1개의 해만을 최우선해로 선정하였다. NSGA-II로 도출된 최우선해의 적합성을 살펴보기 위해서, 자동보정방법인 Powell 방법과 SGA(simple genetic algorithm)를 매개변수 자동보정 방법으로 이용하고 하나의 단일목적함수로 사용해서 최적화한 결과와 비교해보았으며, 비교결과 다목적 유전자 알고리즘을 4개의 목적함수에 모두 적용해서 한번에 도출된 매개변수를 이용한 결과가 보정기간뿐만 아니라 검정기간에 대해서도 비교적 양호한 결과를 나타내는 것으로 나타났다.