• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.495-504
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• 2013

Evolutionary algorithms have been applied to multi-objective optimization problems by approximation methods using computational intelligence. Those methods have been improved gradually in order to generate more exactly many approximate Pareto optimal solutions. The paper introduces a new method using support vector machine to find an approximate Pareto frontier in multi-objective optimization problems. Moreover, this paper applies an evolutionary algorithm to the proposed method in order to generate more exactly approximate Pareto frontiers. Then a decision making with two or three objective functions can be easily performed on the basis of visualized Pareto frontiers by the proposed method. Finally, a few examples will be demonstrated for the effectiveness of the proposed method.

• Management Science and Financial Engineering
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• v.13 no.2
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• pp.29-46
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• 2007

This paper makes an attempt to compare the two important methods for finding solutions of multi-item inventory problem with more than one conflicting objectives. Panda et al.[9] discusses a distance-based method to find the best possible compromise solution with variation of priority under the given weight structure. In this paper, the problem in [9] is revisited through the Pareto-optimal front of genetic algorithm with the help of a situation of retail stocking of FMCG business. The advantages of using the solutions from the perspective of the decision maker obtained through multi-objective optimization are highlighted in terms of population search, weighted goals and priority structure, cost, set of compromise solutions along with prevention of stock-out situation.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.533-549
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• 1996

This paper is concerned with a sequencing problem in mixed model assembly lines, which is important to efficient utilization of the lines. In the problem, we deal with the two objectives of minimizing the risk of stoppage and leveling part usage, and consider sequence-dependent setup time. In this paper, we present a genetic algorithm(GA) suitable for the multi-objective optimization problem. The aim of multi-objective optimization problems is to find all possible non-dominated solutions. The proposed algorithm is compared with existing multi-objective GAs such as vector evaluated GA, Pareto GA, and niched Pareto GA. The results show that our algorithm outperforms the compared algorithms in finding good solutions and diverse non-dominated solutions.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.149-157
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• 2004

This paper presents a new method for hi-objective optimization. Ordinary weighted sum method is easy to implement, but it has two significant drawbacks: (1) the solution distribution by the weighted sum method is not uniform, and (2) the method cannot determine any solutions that reside in non-convex regions of a Pareto front. The proposed adaptive weighted sum method does not solve a multiobjective optimization in a predetermined way, but it focuses on the regions that need more refinement by imposing additional inequality constraints. It is demonstrated that the adaptive weighted sum method produces uniformly distributed solutions and finds solutions on non-convex regions. Two numerical examples and a simple structural problem are presented to verify the performance of the proposed method.

• The Journal of the Korea Contents Association
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• v.15 no.7
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• pp.21-29
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• 2015

This paper proposes a novel two-tier optimization approach for decision making in many-objective problems. Because the Pareto-optimal solution ratio increases exponentially with an increasing number of objectives, simply finding the Pareto-optimal solutions is not sufficient for decision making in many-objective problems. In other words, it is necessary to discriminate the more preferable solutions from the other solutions. In the proposed approach, user preference-oriented as well as diverse Pareto-optimal solutions can be obtained as candidate solutions by introducing an additional tier of optimization. The second tier of optimization employs the corresponding secondary objectives, global evaluation and crowding distance, which were proposed in previous works, to represent the users preference to a solution and the crowdedness around a solution, respectively. To demonstrate the effectiveness of the proposed approach, decision making for some benchmark functions is conducted, and the outcomes with and without the proposed approach are compared. The experimental results demonstrate that the decisions are successfully made with consideration of the users preference through the proposed approach.

• 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.

• 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.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.218-223
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• 2008

A new Pareto-optimal design algorithm, requiring least computational work, is proposed for a single phase AC solenoid actuator with multi-design-objectives: maximizing holding force and minimizing eddy current loss simultaneously. In the algorithm, the design space is successively reduced by a suitable factor, as iteration repeats, with the center of pseudo-optimal point. At each iteration, the objective functions are approximated to a simple second-order response surface with the CCD sampling points generated within the reduced design space, and Pareto-optimal solutions are obtained by applying($1+{\lambda}$) evolution strategy with the fitness values of Pareto strength.

• Structural Engineering and Mechanics
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• v.70 no.3
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• pp.367-380
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• 2019

The paper concerns topology and geometry optimization of statically determinate beams with arbitrary number of supports. The optimization problem is treated as a bi-criteria one, with the objectives of minimizing the absolute maximum bending moment and the maximum deflection for a uniform gravity load. The problem is formulated and solved using the Pareto optimality concept and the lexicographic ordering of the objectives. The non-dominated sorting genetic algorithm NSGA-II and the local search method are used for the optimization in the Pareto sense, whereas the genetic algorithm and the exhaustive search method for the lexicographic optimization. Trade-offs between objectives are examined and sets of Pareto-optimal solutions are provided for different topologies. Lexicographically optimal beams are found assuming that the maximum moment is a more important criterion. Exact formulas for locations and values of the maximum deflection are given for all lexicographically optimal beams of any topology and any number of supports. Topologies with lexicographically optimal geometries are classified into equivalence classes, and specific features of these classes are discussed. A qualitative principle of the division of topologies equivalent in terms of the maximum moment into topologies better and worse in terms of the maximum deflection is found.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.3
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• pp.25-43
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• 2010

We consider a multi-objective balancing and sequencing problem in mixed model assembly lines, which is important for an efficient use of the assembly lines. In this paper, we present a neighborhood symbiotic evolutionary algorithm to simultaneously solve the two problems of balancing and model sequencing under multiple objectives. We aim to find a set of well-distributed solutions close to the true Pareto optimal solutions for decision makers. The proposed algorithm has a two-leveled structure. At Level 1, two populations are operated : One consists of individuals each of which represents a partial solution to the balancing problem and the other consists of individuals for the sequencing problem. Level 2, which is an upper level, works one population whose individuals represent the combined entire solutions to the two problems. The process of Level 1 imitates a neighborhood symbiotic evolution and that of Level 2 simulates an endosymbiotic evolution together with an elitist strategy to promote the capability of solution search. The performance of the proposed algorithm is compared with those of the existing algorithms in convergence, diversity and computation time of nondominated solutions. The experimental results show that the proposed algorithm is superior to the compared algorithms in all the three performance measures.