• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.2
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• pp.254-260
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• 2017

The service life of civil infrastructure needs to be maintained or extended through appropriate inspections and maintenance planning, which results from the optimization process. A multi-objective optimization process can lead to more rational and flexible trade-off solutions rather than a single-objective optimization for the service life management of civil infrastructure. Recent investigations on the service life management of civil infrastructure were generally based on minimizing the life-cycle cost analysis and maximizing the structural performance. Various objectives for service life management have been developed using novel probabilistic concepts and methods over the last few decades. On the other hand, an increase in the number of objectives in a multi-objective optimization problem can lead to difficulties in computational efficiency, visualization, and decision making. These difficulties can be overcome using the objective reduction approach to identify the redundant and essential objectives. As a result, the efficiency in computational efforts, visualization, and decision making can be improved. In this paper, the multi-objective optimization using the objective reduction approach was applied to the service life management of concrete bridges. The results showed that four initial objectives can be reduced by two objectives for the optimal service life management.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.444-455
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• 2007

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.

• Journal of Navigation and Port Research
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• v.33 no.4
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• pp.271-275
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• 2009

A strategic decision making on location selection for product transportation includes many tangible and untangible factors. To choose the best locations is a difficult job in the sense that objectives usually conflict with each other. In this paper, we consider a multi stage multi criteria transshipment problem with different types of items to be transported from the sources to the destination points. For the optimization of the problem, a goal programming formulation will be presented in which the location selection for each product type will be determined under the multi objective criteria. In the study, we generalize the transshipment model with a variety of product types and finite number of different intermediate nodes between origins and destinations. For the selection of the criteria we selected the costs(fixed cost and transportation cost), location numbers, and unsatisfied demand for each type of products in multi stage transportation, which are the main goals in transshipment modelling problems. The related conditions are also modelled through linear formats.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.203-211
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• 2022

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.36-44
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• 2007

This paper is focused on the optimum design for decreasing volume and increasing reliability of multi-stage gear drive. For the optimization on volume and reliability, multi-objective optimization is used. The genetic algorithm is introduced to multi-objective optimization method and it is used to develop the optimum design program using exterior penalty function method to solve the complicated subject conditions. A 5 staged gear drive(geared motor) is chosen to compare the result of developed optimum design method with the existing design. Each of the volume objective, reliability objective, and volume-reliability multi-objectives are performed and compared with existing design. As a result, optimum solutions are produced, which decrease volume and increase reliability. It is shown that the developed design method is good for multi-stage gear drive design.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

• International Journal of High-Rise Buildings
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• v.7 no.4
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• pp.363-374
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• 2018

The façade is an important, complex, and costly part of a building, performing multiple objectives of value to the occupants, like protecting from wind, rain, sunlight, heat, cold, and sound. But the frequency of façade fires in large buildings is alarming, and has multiplied by seven times worldwide over the last three decades, to a current rate of 4.8 fires per year. High-performing polymer based materials allow for a significant improvement across several objectives of a facade (e.g., thermal insulation, weight, and construction time) thereby increasing the quality of a building. However, all polymers are flammable to some degree. If this safety problem is to be tackled effectively, then it is essential to understand how different materials, and the façade as a whole, perform in the event of a fire. This paper discusses the drivers for flammability in facades, the interaction of facade materials, and current gaps in knowledge. In doing so, it aims to provide an introduction to the field of façade fires, and to show that because of the drive for thermal efficiency and sustainability, façade systems have become more complex over time, and they have also become more flammable. We discuss the importance of quantifying the flammability of different façade systems, but highlight that it is currently impossible to do so, which hinders research progress. We finish by putting forward an integral framework of design that uses multi-objective optimization to ensure that flammability is minimized while considering other objectives, such as maximizing thermal performance or minimizing weight.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.1
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• pp.95-103
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• 1998

This paper is concentraed on a study of operation for multiple objectives in a automated manufacturing system with multiple machining cells . Largest Sum Processing-time First(LSPF) was developed in order to minimize makespan, mean flowtime and maximize mean utilization and compare with Ho and Chang's algorithm(HC) and Hunsucker and Shah's algorithm(HS). The results show that LSPF provides better soutions than HC at 78.2% and than HS at 67.8% of total problems to frequency . LSPF reduces 5.8% of makespan by HC and 22.1% of the value by HS and curtails 15.8% , 7.5% of mean flowtime by receptive algorithms(HC, HS). And mean utilization is also higher about 5.5% than HC and HS.

• Proceedings of the Korean Society for Quality Management Conference
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• 1998.11a
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• pp.680-695
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• 1998

The objective of this study is to optimize the responses of the creep feed ceramic grinding process simultaneously by an off-1ine multicriteria optimization methodology. The responses considered as objectives are material removal rate, flexural strength, normal grinding force, workpiece surface roughness and grinder power. Alumina material was ground by the creep feed grinding mode using superabrasive grinding wheels. The process variables optimized for the above objectives include grinding wheel specification, such as bond type, mesh size, and grit concentration, and grinding process parameters, such as depth of cut and feed rate. A weighting method transforms the multi-objective problem into a single-objective programming format and then, by parametric variation of weights, the set of non-dominated optimum solutions are obtained. Finally, the multi-objective optimization methodology was tested by a sensitivity analysis to check the stability of the model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.10
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• pp.584-592
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• 2003

This paper presents a fuzzy-GA method for the allocation and operation of dispersed generator systems(DGs) based on load model in composite distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The problem formulation considers an objective to reduce power loss of distribution systems and the constraints such as the number or total capacity of DGs and the deviation of the bus voltage. The main idea of solving fuzzy goal programming is to transform the original objective function and constraints into the equivalent multi-objectives functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithm. The method proposed is applied to IEEE 12 bus and 33 bus test systems to demonstrate its effectiveness. .