• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.442-460
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• 2021

Nowadays, recommendation systems (RSs) are applied to all aspects of online life. In order to overcome the problem that individuals who do not meet the constraints need to be regenerated when the many-objective evolutionary algorithm (MaOEA) solves the hybrid recommendation model, this paper proposes a many-objective particle swarm optimization algorithm based on multiple criteria (MaPSO-MC). A generation-based fitness evaluation strategy with diversity enhancement (GBFE-DE) and ISDE⁺ are coupled to comprehensively evaluate individual performance. At the same time, according to the characteristics of the model, the regional optimization has an impact on the individual update, and a many-objective evolutionary strategy based on bacterial foraging (MaBF) is used to improve the algorithm search speed. Experimental results prove that this algorithm has excellent convergence and diversity, and can produce accurate, diverse, novel and high coverage recommendations when solving recommendation models.

• Journal of Korean Ophthalmic Optics Society
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• v.18 no.3
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• pp.347-356
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• 2013

Purpose: To evaluate the visual acuity and visual performance after implantation of a aspheric multifocal (ReSTOR$^{(R)}$ SN6AD3) intraocular lens (IOL). Methods: Nineteen cataract patients (30 eyes) implanted with an aspheric multifocal IOL (ReSTOR$^{(R)}$ SN6AD3) either unilaterally or bilaterally were participated. Visual acuity (VA) and objective optical performance were evaluated at the time of preoperation, 1 week, 1 month, and 3 month after operation. At 3 month of post-operation, objective visual performance were measured and compared with the 38 eyes of 20 age-matched normal control. Distance VA was measured by using the ETDRS LCD chart and intermediate and near visual acuity were measured using Jaeger chart. Objective visual performance was assessed preoperatively and 1 week, 1 month and 3 month postoperatively using a double-pass system (Optical Quality Analysis System) with a 4-mm pupil diameter, the OSI (objective scatter index), MTF (modulation transfer function) cut off and strehl ratio. At 3 month of post-operation, visual acuity and visual performance compared with age matched normal control. Results: The uncorrected distance VA, OSI, MTF cut off and strehl ratio were significantly improved (p<0.05) until 1 month postoperatively. Visual performance of MTF cut off and strehl ratio after 3 month of operation were significantly improved compared to the normal control (p=0.063, p=0.103 respectively), however, OSI was higher than normal control. Patients implanted with aspheric multifocal IOL were satisfied with distance and near VA however, were unsatisfied with intermediate VA and reported glare and halos. Conclusions: The visual performance reaches to a stable condition in 1 month of implantation of aspheric multifocal IOL and improved to the level of age-mated normal patients. Also patients were satisfied with their quality of vision, however, intermediate VA, glare and halos were reported as complications.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3565-3583
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• 2022

The task scheduling problem has received a lot of attention in recent years as a crucial area for research in the cloud environment. However, due to the difference in objectives considered by service providers and users, it has become a major challenge to resolve the conflicting interests of service providers and users while both can still take into account their respective objectives. Therefore, the task scheduling problem as a bi-objective game problem is formulated first, and then a task scheduling model based on the bi-objective game (TSBOG) is constructed. In this model, energy consumption and resource utilization, which are of concern to the service provider, and cost and task completion rate, which are of concern to the user, are calculated simultaneously. Furthermore, a many-objective evolutionary algorithm based on a partitioned collaborative selection strategy (MaOEA-PCS) has been developed to solve the TSBOG. The MaOEA-PCS can find a balance between population convergence and diversity by partitioning the objective space and selecting the best converging individuals from each region into the next generation. To balance the players' multiple objectives, a crossover and mutation operator based on dynamic games is proposed and applied to MaPEA-PCS as a player's strategy update mechanism. Finally, through a series of experiments, not only the effectiveness of the model compared to a normal many-objective model is demonstrated, but also the performance of MaOEA-PCS and the validity of DGame.

• Korean Management Science Review
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• v.18 no.1
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• pp.89-105
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• 2001

The performance of Cellular Manufacturing (CM) systems has been rigorously investigated during the last two decades, but the extent of empirical research on CM is limited. A major objective of this study is to examine the relationship between critical success factors, employees' implementation attitudes and performance of CM systems. Two hypothesis were formulated &d Tested: (1) The impact of critical success factors on performance and to what extent does certain critical success factors correlate with performance\ulcorner (2) How does the relationship between critical success factors and performance differ by employees' implementation attitudes\ulcorner Results from the study provide partial support for relationship between critical success factors (infrastructure, organizational immersion, autonomous management) and performance. The results also show that differences in performance of organizations grouped by degree of employees' attitudes could be found.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1294-1299
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• 2009

Best value in value engineering has relation to cost and performance. But a severe problem in VE study of a project is to reduce value due to loss of performance, caused by focusing on cost reduction. Also a lack of understanding performance concept, no trial VE workshop as well as cost saving-based policy have not satisfied customer needs. A efficient and practical methodology for accomplishing best value in construction projects is proposed. This study developed a more objective approach for performance measurement approach of mega projects and suggested a systematic process of performance quantitative analysis verifying value improvement. The proposed performance measurement method would be very useful for better communication and consensus between stakeholders and VE team especially through value engineering.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2A
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• pp.223-231
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• 2008

This research presents a new maintenance planning method for deteriorating bridges considering simultaneously the minimization of the maintenance cost and maximization of the bridge performance. Optimal maintenance planning is formulated as a multi-objective optimization problem that treats the maintenance cost as well as the bridge performance such as the condition grade of the bridge deck, girder and pier. To effectively address the multi-objective optimization problem and decision making process for the obtained solution set, we apply a genetic algorithm as a numerical searching technique and adopt a preference-based optimization method. A numerical example for a typical 5-span prestressed concrete girder bridge shows that the maintenance cost and the performance of the bridge can be balanced reasonably without severe trade-offs between each objectives.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1234-1242
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• 2003

In this paper we present a simple and efficient robust optimal design formulation for MEMS structures and its application to a resonant-type micro probe. The basic idea is to use the gradient index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-effective and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation. This method, although shown for MEMS structures, may as well be easily applied to conventional mechanical structures where information on uncertainties is lacking but robustness is highly important.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1254-1261
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• 2003

In this paper we present a simple and efficient robust optimal design formulation and its application to a resonant-type micro probe. The basic idea is to use the Gradient Index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-efficient and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation.