• 대한기계학회논문집A
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• 제26권6호
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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.

• 한국농공학회논문집
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• 제46권2호
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• pp.77-89
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• 2004

This study was carried out to find out the reasonable optimum design method for the design of prestressed concrete structures. The optimum design problems were formulated and computer programs to solve these problems were developed. To test the reliablity, efficiency, possibility of application and reasonablity of optimum design problems and computer programs, both continuous optimization method and mixed-discrete optimization method were applied to the design of prestressed concrete composite girder and application results were discussed. It is proved that mixed-discrete optimization method is more reliable, efficient and reasonable than continuous optimization method for the optimum design of prestressed concrete structures.

• Advances in Computational Design
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• 제1권2호
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• pp.175-194
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• 2016

Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins for navigation and hunting in various environments. This ability of dolphins is mimicked in this paper to develop a new optimization method. Dolphin Echolocation Optimization (DEO) is an optimization method based on dolphin's approach for hunting food and exploration of environment. DEO has already been developed for discrete optimization search space and here it is extended to continuous search space. DEO has simple rules and is adjustable for predetermined computational cost. DEO provides the optimum results and leads to alternative optimality curves suitable for the problem. This algorithm has a few parameters and it is applicable to a wide range of problems like other metaheuristic algorithms. In the present work, the efficiency of this approach is demonstrated using standard benchmark problems.

• 한국기계가공학회지
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• 제19권5호
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• pp.27-37
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• 2020

Structural optimization problems with discrete design variables require more function calculations (or finite element analyses) than those in the continuous design space. In this study, a method to find an optimal solution in the discrete design of the truss structure is presented, reducing the number of function calculations. Because a continuous optimal solution is the Karush-Kuhn-Tucker point that satisfies the optimality condition, it is assumed that the discrete optimal solution is around the continuous optimum. Then, response values such as weight, displacement, and stress are predicted using approximate models-referred to as hybrid metamodels-within specified design ranges. The discrete design method using the hybrid metamodels is used as a post-process of the continuous optimization process. Standard truss design problems of 10-bar, 25-bar, 15-bar, and 52-bar are solved to show the usefulness of this method. The results are compared with those of existing methods.

• 한국지능시스템학회논문지
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• 제21권2호
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• pp.206-211
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• 2011

본 논문에서는 연속적인 파라미터 공간에 대한 최적화에 대해 진화적 알고리즘의 특징적인 형상화를 제시한다. 이 방법은유전알고리즘이 연속적인 탐색공간에서의 파라미터 식별에 대해 가장 강점을 지녔다는 점에 착안한 것이다. 유전알고리즘과 제안한 알고리즘과의 주요한 차이점은 개별적 또는 연속적인 묘사의 차이가 있다는 것이다. 잘 알려진 실험함수의 최적화문제를 도입하여 연속 탐색공간 문제에 대해 제안하는 알고리즘에 대해 계산시간 및 사용메모리 등의 성능이 우수하다는 효율성을 보였다.

• Steel and Composite Structures
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• 제42권6호
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• pp.855-864
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• 2022

Due to limited resources, and increasing speed of development, the optimal use of available resources has become the most important challenge of human societies. In the last few decades, many researchers have focused their research on solving various optimization problems, providing new optimization methods, and improving the performance of existing optimization methods. Echolocation Search Algorithm (ESA) is an evolutionary optimization algorithm that is based on mimicking the mechanism of the animals such as bats, dolphins, oilbirds, etc in food finding to solve optimization problems. In this paper, the ability of ESA for solving truss size optimization problems with continuous variables is investigated. To examine the efficiency of ESA, three benchmark examples are considered. The numerical results exhibit the effectiveness of ESA for solving truss optimization problems.

• 산업경영시스템학회지
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• 제33권4호
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• pp.58-68
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• 2010

Particle Swarm Optimization (PSO) which has been well known to solve continuous problems can be applied to discrete combinatorial problems. Several DPSO (Discrete Particle Swarm Optimization) algorithms have been proposed to solve discrete problems such as traveling salesman, vehicle routing, and flow shop scheduling problems. They are different in representation of position and velocity vectors, operation mechanisms for updating vectors. In this paper, the performance of 5 DPSOs is analyzed by applying to traditional Traveling Salesman Problems. The experiment shows that DPSOs are comparable or superior to a genetic algorithm (GA). Also, hybrid PSO combined with local optimization (i.e., 2-OPT) provides much improved solutions. Since DPSO requires more computation time compared with GA, however, the performance of hybrid DPSO is not better than hybrid GA.

• 경영과학
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• 제13권1호
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• pp.97-109
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• 1996

Genetic Algorithms have been successfully applied to various problems (for example, engineering design problems with a mix of continuous, integer and discrete design variables) that could not have been readily solved with traditional computational techniques. But, several problems for which conventional Genetic Algorithms are ill defined are premature convergence of solution and application of exterior penalty function. Therefore, we developed an Improved Genetic Algorithms (IGAs) to solve above two problems. As a case study, IGAs is applied to several nonlinear optimization problems and it is proved that this algorithm is very useful and efficient in comparison with traditional methods and conventional Genetic Algorithm.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1995년도 봄 학술발표회 논문집
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• pp.187-194
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• 1995

Traditional genetic algorithms(GA) have mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its targe computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of read code GA are developed to use continuous design variables directly. The results of real code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As results of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the rent code GA developed here can be used for the general optimization problem.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.559-560
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• 2021

차등 진화는 연속 최적화 문제를 해결하기 위한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 차등 진화를 적용하면 성능이 급격히 저하되고 런타임이 기하급수적으로 증가한다. 따라서 Spark(SparkDECC로 알려짐)를 기반으로 하는 새로운 협력 공진화 차동 진화가 제안된다. 분할 정복 전략은 SparkDECC에서 사용된다.