• Journal of Distribution Science
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• v.15 no.2
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• pp.15-20
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• 2017

Purpose - This paper aims to suggest a delivery constrained internet shopping optimization problem (DISOP) which must be solved for online recommendation system to provide a customized service considering cost and delivery conditions at the same time. Research design, data, and methodology - To solve a (DISOP), we propose a multi-objective formulation and a solution approach. By using a commercial optimization software (LINDO), a (DISOP) can be solved iteratively and a pareto optimal set can be calculated for real-sized problem. Results - We propose a new research problem which is different with internet shopping optimization problem since our problem considers not only the purchasing cost but also delivery conditions at the same time. Furthermore, we suggest a multi-objective mathematical formulation for our research problem and provide a solution approach to get a pareto optimal set by using numerical example. Conclusions - This paper proposes a multi-objective optimization problem to solve internet shopping optimization problem with delivery constraint and a solution approach to get a pareto optimal set. The results of research will contribute to develop a customized comparison and recommendation system to help more easy and smart online shopping service.

• Journal of Power Electronics
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• v.15 no.1
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• pp.160-176
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• 2015

This paper presents a method of designing optimal integer- and fractional-order proportional-integral-derivative (FOPID) controllers for a boost converter to gain a set of favorable characteristics at various operating points. A Pareto-based multi-objective optimization approach called strength Pareto evolutionary algorithm (SPEA) is used to obtain fast and low overshoot start-up and dynamic responses and switching stability. The optimization approach generates a set of optimal gains called Pareto set, which corresponds to a Pareto front. The Pareto front is a set of optimal results for objective functions. These results provide designers with a trade-off look-up table, in which they can easily choose any of the optimal gains based on design requirements. The SPEA also overcomes the difficulties of tuning the FOPID controller, which is an extension to the classic integer-order PID controllers and potentially promises better results. The proposed optimized FOPID controller provides an excellent start-up response and the desired dynamic response. This paper presents a detailed comparison of the optimum integer- and the fractional-order PID controllers. Extensive simulation and experimental results prove the superiority of the proposed design methodology to achieve a wide set of desired technical goals.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.11 s.254
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• pp.1358-1368
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• 2006

This paper proposes a Pareto artificial life algorithm for solving multi-objective optimization problems. The artificial life algorithm for optimization problem with a single objective function is improved to handle Pareto optimization problem through incorporating the new method to estimate the fitness value for a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm was applied to the optimum design of a journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application were presented to give the possible solutions to a decision maker or a designer. Furthermore, the relation between linearly combined single-objective optimization problem and Pareto optimization problem has been studied.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.869-874
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• 2007

In searching for solutions to multiobjective optimization problem, we find that there is no single optimal solution but rather a set of solutions known as 'Pareto optimal set'. To find approximation of ideal pareto optimal set, search capability of diverse individuals at population space can determine the performance of evolutionary algorithms. This paper propose the method to maintain population diversify and to find non-dominated alternatives in Game model based Co-Evolutionary Algorithm.

• Journal of Power Electronics
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• v.12 no.1
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• pp.104-112
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• 2012

In this paper a new method for the design of a simple PI controller is presented and it has been applied in the control of a Boost based PFC rectifier. The Strength Pareto evolutionary algorithm, which is based on the Pareto Optimality concept, used in Game theory literature is implemented as a multi-objective optimization approach to gain a good transient response and a high quality input current. In the proposed method, the input current harmonics and the dynamic response have been assumed as objective functions, while the PI controller's gains of the PFC rectifier (Kpi, Tpi) are design variables. The proposed algorithm generates a set of optimal gains called a Pareto Set corresponding to a Pareto Front, which is a set of optimal results for the objective functions. All of the Pareto Front points are optimum, but according to the design priority objective function, each one can be selected. Simulation and experimental results are presented to prove the superiority of the proposed design methodology over other methods.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.869-874
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• 2005

This paper proposes the Pareto artificial life algorithm for a multi-objective function optimization problem. The artificial life algorithm for a single objective function optimization problem is improved through incorporating the new method to estimate the fitness value fur a solution and the Pareto list to memorize and to improve the Pareto optimal set. The proposed algorithm is applied to the optimum design of a Journal bearing which has two objective functions. The Pareto front and the optimal solution set for the application are reported to present the possible solutions to a decision maker or a designer.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.3
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• pp.284-289
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• 2005

A structural design system is developed for the optimum design of double hull tankers based on the multi-objective function method. As a multi-objective function method, Pareto optimal based random search method is adopted to find the minimum structural weight and fabrication cost. The fabrication cost model is developed by considering the welding technique, welding poses and assembly stages to manage the fabrication man-hour and process. In this study, a new structural design is investigated due to the rapidly increased material cost. Several optimum structural designs on the basis of high material cost are carried out based on the Pareto optimal set obtained by the random search method. The design results are compared with existing ship, which is designed under low material cost.

• Journal of Energy Engineering
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• v.13 no.2
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• pp.128-132
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• 2004

This paper presents a new methodology to get pareto-optimal solution for generation planning. First, we apply dynamic programming, and we can get an optimal economic dispatch considering total quantity of contamination for the specified term. Second, we developed a method which can get pareto-optimal solution. This solution is consisted of a set of optimal generation planning. As a result, decision maker can get pareto-optimal solutions, and can choose a solution. We applied this method to the test system, and showed the usefulness.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.6
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• pp.987-992
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• 2002

In an attempt to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. In this paper, pareto-based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. This algorithm is based on Continuous Evolutionary Algorithms to solve single objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche-formation method fur fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto-optimal tradeoff surface. Finally, the validity of this method has been demonstrated through a numerical example.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.4
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• pp.119-125
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• 2011

The part family (PF) formation in a cellular manufacturing has been a key issue for the successful implementation of Group Technology (GT). Basically, a part has two different attributes; i.e., design and manufacturing. The respective similarity in both attributes is often conflicting each other. However, the two attributes should be taken into account appropriately in order for the PF to maximize the benefits of the GT implementation. This paper proposes a clustering algorithm which considers the two attributes simultaneously based on pareto optimal theory. The similarity in each attribute can be represented as two individual objective functions. Then, the resulting two objective functions are properly combined into a pareto fitness function which assigns a single fitness value to each solution based on the two objective functions. A GA is used to find the pareto optimal set of solutions based on the fitness function. A set of hypothetical parts are grouped using the proposed system. The results show that the proposed system is very promising in clustering with multiple objectives.