• International Journal of High-Rise Buildings
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• v.7 no.2
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• pp.161-170
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• 2018

Rapid urbanization, resource depletion, and limited land are further increasing the need for skyscrapers in city centers; therefore, it is imperative to enhance tall building performance efficiency and energy-generative capability. Potential performance improvements can be explored using parametric multi-objective optimization, aided by evaluation tools, such as computational fluid dynamics and energy analysis software, to visualize and explore skyscrapers' multi-resource, multi-system generative potential. An optimization-centered, software-based design platform can potentially enable the simultaneous exploration of multiple strategies for the decreased consumption and large-scale production of multiple resources. Resource Generative Skyscrapers (RGS) are proposed as a possible solution to further explore and optimize the generative potentials of skyscrapers. RGS can be optimized with waste-energy-harvesting capabilities by capitalizing on passive features of integrated renewable systems. This paper describes various resource-generation technologies suitable for a synergetic integration within the RGS typology, and the software tools that can facilitate exploration of their optimal use.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.45-54
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• 2022

In this study, a multi-objective robust job-shop scheduling (JSS) model was developed. The model considered multi-jobs and multi-machines. The model also considered uncertain processing times for all tasks. Each job was assigned a specific due date and a tardiness penalty to be paid if the job was not delivered on time. If any job was completed early, holding expenses would be assigned. In addition, the model added idling penalties to accommodate the idling of machines while waiting for jobs. The problem assigned was to determine the optimal start times for each task that would minimize the expected penalties. A numerical problem was solved to minimize both the makespan and the total penalties, and a comparison was made between the results. Analysis of the results produced a prescription for optimizing penalties that is important to be accounted for in conjunction with uncertainties in the job-shop scheduling problem (JSSP).

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.3
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• pp.262-266
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• 2005

In this paper, in order to enhance thrust of slotless type Permanent Magnet Linear Synchronous Motor, an optimal design is achieved by combining a genetic algorithm with 3D space harmonic method. In the case of multi-objective functions, the ratio of thrust/weight and thrust/volume are increased by $\7.56[%]l\;and\;7.98\[%]$, respectively. Thus, miniaturization and lightweight were realized at the same time.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.368-373
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• 2000

In this paper, a control algorthm suitable for multi-objective control is proposed based on the orthogonal array which is normally used in statics and industrial engineering. And a newly defined Nthcertainty factor is suggested, which can effectively exclude the less confident rule. The Nth-certainty factor is defined by the F-values of the ANOVA(analysis of variance) table. It is shown that the algorithm can be successfully adopted to the design of controller for an active magnetic bearing system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.63-68
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• 2000

In this paper, a control algorithm suitable for multi-objective control problems is proposed based on the orthogonal array which is normally used in statistics and industrial engineering. And a newly defined Nth-certainty factor is suggested, which can effectively exclude the less confident rules. The Nth-certainty factor is defined by the F-values of the ANOVA(analysis of variance) table. It is shown that the algorithm can be successfully adopted to the design of controller for an active magnetic bearing system.

• Proceedings of the KIEE Conference
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• 2003.07b
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• pp.891-893
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• 2003

This paper Presents the optimum design of Brushless DC Motor using multi-objective function Neural Network to maximize efficiency and Ratio of Torque Per unit weight.

• Proceedings of the KOR-KST Conference
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• 2007.05a
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• pp.469-476
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• 2007

• Smart Structures and Systems
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• v.22 no.4
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• pp.469-479
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• 2018

The present research study utilizes a multi-objective optimization method for Pareto optimization of an eight-degree of freedom full vehicle vibration model, adopting a non-dominated sorting genetic algorithm II (NSGA-II). In this research, a full set of ride comfort as well as ride safety parameters are considered as objective functions. These objective functions are divided in to two groups (ride comfort group and ride safety group) where the ones in one group are in conflict with those in the other. Also, in this research, a special optimizing technique and combinational method consisting of weighted sum method and Pareto optimization are applied to transform Pareto double-objective optimization to Pareto full-objective optimization which can simultaneously minimize all objectives. Using this technique, the full set of ride parameters of three dimensional vehicle model are minimizing simultaneously. In derived Pareto front, unique trade-off design points can selected which are non-dominated solutions of optimizing the weighted sum comfort parameters versus weighted sum safety parameters. The comparison of the obtained results with those reported in the literature, demonstrates the distinction and comprehensiveness of the results arrived in the present study.

• 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).

• Structural Engineering and Mechanics
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• v.63 no.4
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• pp.537-549
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• 2017

One of the main concerns of civil engineering researchers is developing or modifying an energy dissipation system that can effectively control structural vibrations, and keep the structural response within tolerable limits during unpredictable events like earthquakes, wind and any kind of thrust load. This article proposes a new type of mass damper system for controlling wideband earthquake vibrations, called Multiple Wall Dampers (MWD). The basic principle of the Tuned Mass Damper (TMD) was used to design the proposed wall damper system. This passive energy dissipation system does not require additional mass for the damping system because the boundary wall mass of the building was used as a damper mass. The multi-mode approach was applied to determine the location and design parameters of the dampers. The dampers were installed based on the maximum amplitude of modes. To optimize the damper parameters, the multi-objective optimization Response Surface Methodology was used, with frequency response and maximum displacement as the objective functions. The obtained structural responses under different earthquake forces demonstrated that the MWD is one of the most capable tools for reducing the responses of multi-storied buildings, and this system can be practically used for new and existing building structures.