• IE interfaces
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• v.24 no.3
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• pp.220-230
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• 2011

The multi-reservoir operation problem for efficient utilization of water resources involves conflicting objectives, and the problem can be solved by varying weight coefficient on objective functions. Accordingly, decision makers need to choose appropriate weight coefficients balancing the trade-offs among multiple objectives. Although the appropriateness of the weight coefficients may depend on the total amount of water inflow, reservoir operating policy may not be changed to a certain degree for different hydrological conditions on inflow. Therefore, we propose to use fuzzy Data Envelopment Analysis (DEA) to rank the weight coefficients in consideration of the inflow variation. In this approach, we generate a set of Paretooptimal solutions by applying different weight coefficients on Coordinated Multi-reservoir Operating Model. Then, we rank the Pareto-optimal solutions or the corresponding weight coefficients by using Fuzzy DEA model. With the proposed approach, we can suggest the best weight coefficients that can produce the appropriate Pareto-optimal solution considering the uncertainty of inflow, whereas the general DEA model cannot pinpoint the best weight coefficients.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1471-1480
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• 2020

This research presents a case study on the remediation of a radioactive waste (uranium: U) utilizing a multi-objective fuzzy optimization in an electrocoagulation process for the iron-stainless steel and aluminum-stainless steel anode/cathode systems. The incorporation of the cumulative uncertainty of result, operational cost and energy consumption are essential key elements in determining the feasibility of the developed model equations in satisfying specific maximum contaminant level (MCL) required by stringent environmental regulations worldwide. Pareto-optimal solutions showed that the iron system (0 ㎍/L U: 492 USD/g-U) outperformed the aluminum system (96 ㎍/L U: 747 USD/g-U) in terms of the retained uranium concentration and energy consumption. Thus, the iron system was further carried out in a multi-objective analysis due to its feasibility in satisfying various uranium standard regulatory limits. Based on the 30 ㎍/L MCL, the decision-making process via fuzzy logic showed an overall satisfaction of 6.1% at a treatment time and current density of 101.6 min and 59.9 mA/㎠, respectively. The fuzzy optimal solution reveals the following: uranium concentration - 5 ㎍/L, cumulative uncertainty - 25 ㎍/L, energy consumption - 461.7 kWh/g-U and operational cost based on electricity cost in the United States - 60.0 USD/g-U, South Korea - 55.4 USD/g-U and Finland - 78.5 USD/g-U.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.66-71
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• 2007

To adapt environment changing high speed and improve rapidly response ability for variation of environment and reduce delay time of decision making inlet agents, the derivation of user's preference and alternative are required. In this paper, we propose an efficient coordination method of multi-agents based on fuzzy decision making with the solution proposed by agents in the view of Pareto optimality. Our method generates the optimal alternative by using weighted value. We compute importance of attributes of winner agent, then can obtain the priorities lot attributes. The result of our method is analyzed that of Yager's method.

• Journal of Power Electronics
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• v.16 no.2
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• pp.610-620
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• 2016

The compound active clamp zero voltage soft switching (CACZVS) three-phase power factor correction (PFC) converter has many advantages, such as high efficiency, high power factor, bi-directional energy flow, and soft switching of all the switches. Triple closed-loop PI controllers are used for the three-phase power factor correction converter. The control objectives of the converter include a fast transient response, high accuracy, and unity power factor. There are six parameters of the controllers that need to be tuned in order to obtain multi-objective optimization. However, six of the parameters are mutually dependent for the objectives. This is beyond the scope of the traditional experience based PI parameters tuning method. In this paper, an improved chaotic particle swarm optimization (CPSO) method has been proposed to optimize the controller parameters. In the proposed method, multi-dimensional chaotic sequences generated by spatiotemporal chaos map are used as initial particles to get a better initial distribution and to avoid local minimums. Pareto optimal solutions are also used to avoid the weight selection difficulty of the multi-objectives. Simulation and experiment results show the effectiveness and superiority of the proposed method.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.324-333
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• 2013

The purpose of this paper is to improve the efficiency of multi-objective topology optimization using a genetic algorithm (GA) with bar-system representation. We proposed a new GA using an elite initial population obtained from a Solid Isotropic Material with Penalization (SIMP) using a weighted sum method. SIMP with a weighted sum method is one of the most established methods using sensitivity analysis. Although the implementation of the SIMP method is straightforward and computationally effective, it may be difficult to find a complete Pareto-optimal set in a multi-objective optimization problem. In this study, to build a more convergent and diverse global Pareto-optimal set and reduce the GA computational cost, some individuals, with similar topology to the local optimum solution obtained from the SIMP using the weighted sum method, were introduced for the initial population of the GA. The proposed method was applied to a structural topology optimization example and the results of the proposed method were compared with those of the traditional method using standard random initialization for the initial population of the GA.

• Journal of Magnetics
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• v.21 no.4
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• pp.660-665
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• 2016

The electromagnetic apparatus plays an important role in high power electrical systems. It is of great importance to provide an effective approach for the optimization of the high power electromagnetic apparatus. However, premature convergence and few Pareto solution set of the optimization for electromagnetic apparatus always happen. This paper proposed a modified multi-objective particle swarm optimization algorithm based on the niche sorting strategy. Applying to the modified algorithm, this paper guarantee the better Pareto optimal front with an enhanced distribution. Aiming at shortcomings in the closing bounce and slow breaking velocity of electromagnetic apparatus, the multi-objective optimization model was established on the basis of the traditional optimization. Besides, by means of the improved multi-objective particle swarm optimization algorithm, this paper processed the model and obtained a series of optimized parameters (decision variables). Compared with other different classical algorithms, the modified algorithm has a satisfactory performance in the multi-objective optimization problems in the electromagnetic apparatus.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Wind and Structures
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• v.36 no.2
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• pp.105-120
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• 2023

The present study is performed to find the effect of corner recession on a square plan-shaped tall building. A series of numerical simulations have been carried out to find the two orthogonal wind force coefficients on various model configurations using Computational Fluid Dynamics (CFD). Numerical analyses are performed by using ANSYS-CFX (k-ℇ turbulence model) considering the length scale of 1:300. The study is performed for 0° to 360° wind angle of attack. The CFD data thus generated is utilised to fit parametric equations to predict alongwind and crosswind force coefficients, C_fx and C_fy. The precision of the parametric equations is validated by employing a wind tunnel study for the 40% corner recession model, and an excellent match is observed. Upon satisfactory validation, the parametric equations are further used to carry out multiobjective optimization considering two orthogonal force coefficients. Pareto optimal design results are presented to propose suitable percentages of corner recession for the study building. The optimization is based on reducing the alongwind and crosswind forces simultaneously to enhance the aerodynamic performance of the building.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.99-123
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• 2006

In an effect to composite an optimal supply chain, this study has introduced an agent-based negotiation as a method to assign a lot of orders to a large number of participants. As a resources allocation mechanism to form a strategic cooperation based on information sharing between supply chain members(buyers, manufacturers, suppliers), this agent negotiation provides coordination functions allowing all participants to make a profit and accomplishing Pareto optimum solution from the viewpoint of a whole supply chain. A SET model-based scheduling takes into consideration both earliness production cost and tardiness production cost, along with a competitive relationship between multiple participants. This study has tried to prove that the result of an agent-based negotiation is a Pareto optimal solution under the dynamic supply chain environment, establishing the mathematical formulation for a performance test, and making a comparison with the heuristic Branch & Bound method.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.