• Structural Engineering and Mechanics
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• 제42권3호
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• pp.335-352
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• 2012

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

• 산업경영시스템학회지
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• 제20권44호
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• pp.93-101
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• 1997

This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are application of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an improved GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

• 한국건축시공학회지
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• 제12권6호
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• pp.664-673
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• 2012

Determining the layout of temporary facilities that support construction activities at a site is an important planning activity, as layout can significantly affect cost, quality of work, safety, and other aspects of the project. The construction site layout problem involves difficult combinatorial optimization. Recently, various artificial intelligence(AI)-based algorithms have been applied to solving many complex optimization problems, including neural networks(NN), genetic algorithms(GA), and swarm intelligence(SI) which relates to the collective behavior of social systems such as honey bees and birds. This study proposes a site facility layout optimization algorithm based on self-organizing maps(SOM). Computational experiments are carried out to justify the efficiency of the proposed method and compare it with particle swarm optimization(PSO). The results show that the proposed algorithm can be efficiently employed to solve the problem of site layout.

• 산업경영시스템학회지
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• 제36권3호
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• pp.63-70
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• 2013

Many real world optimization problems are discrete and multi-valued. Meta heuristics including Genetic Algorithm and Particle Swarm Optimization have been effectively used to solve these multi-valued optimization problems. However, extensive comparative study on the performance of these algorithms is still required. In this study, performance of these algorithms is evaluated with multi-modal and multi-dimensional test functions. From the experimental results, it is shown that Discrete Particle Swarm Optimization (DPSO) provides better and more reliable solutions among the considered algorithms. Also, additional experiments shows that solution quality of DPSO is not lowered significantly when bit size representing a solution increases. It means that bit representation of multi-valued discrete numbers provides reliable solutions instead of becoming barrier to performance of DPSO.

• Structural Engineering and Mechanics
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• 제70권6호
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• pp.649-659
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• 2019

Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using Moth-Flame Optimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.

• Coupled systems mechanics
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• 제13권1호
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• pp.1-19
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• 2024

The effectiveness of coupling with a bolted rim is assessed in this research using a newly designed optimization algorithm. The current study, which is provided here, evaluates 10 contemporary metaheuristic approaches for enhancing the coupling with bolted rim design problem. The algorithms used are particle swarm optimization (PSO), crow search algorithm (CSA), enhanced honeybee mating optimization (EHBMO), Harmony search algorithm (HSA), Krill heard algorithm (KHA), Pattern search algorithm (PSA), Charged system search algorithm (CSSA), Salp swarm algorithm (SSA), Big bang big crunch optimization (B-BBBCO), Gradient based Algorithm (GBA). The contribution of the paper isto optimize the coupling with bolted rim problem by comparing these 10 algorithms and to find which algorithm gives the best optimized result. These algorithm's performance is evaluated statistically and subjectively.

• 정보처리학회논문지B
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• 제11B권2호
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• pp.213-220
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• 2004

진화 알고리즘은 여러 개의 상충하는 목적을 갖는 다목적 최적화 문제를 해결하기에 적합한 방법이다. 특히, 파레토 지배관계에 기초하여 개체의 적합도를 평가하는 파레토 기반 진화알고리즘들은 그 성능에 있어서 우수한 평가를 받고 있다. 최근의 파레토 기반 진화알고리즘들은 전체 파레토 프론트에 균일하게 분포하는 해집합의 생성을 위해 개체들의 밀도를 개체의 적합도를 평가하기 위한 하나의 요소로 사용하고 있다. 그러나 밀도의 역할은 전체 진화과정에서 중요한 요소가 되기보다는 파레토 프론트에 어느 정도 수렴된 후, 개체의 균일 분포를 만들기 위해 사용된다. 본 논문에서 우리는 파레토 지배 순위와 밀도에 대한 임의가중치를 적용한 다목적 최적화 진화알고리즘을 제안한다. 제안한 알고리즘은 진화 개체의 적합도를 평가하기 위해 파레토 순위와 밀도에 대한 임의의 가중치를 적용하므로 전체 진화과정에서 파레토 순위와 밀도가 비슷한 영향을 미치도록 하였다. 또한, 제안한 방법을 6개의 다목적 최적화 문제에 적용한 결과 비교적 우수한 결과를 보였다.

• 한국CDE학회논문집
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• 제1권3호
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• pp.234-241
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• 1996

Recently, Genetic Algorithms(GAs), which consist of genetic operators named selection crossover and mutation, are widely adapted into a search procedure for structural optimization. Contrast to traditional optimal design techniques which use design sensitivity analysis results, GAs are very simple in their algorithms and there is no need of continuity of functions(or functionals) any more in GAs. So, they can be easily applicable to wide territory of design optimization problems. Also, virtue to multi-point search procedure, they have higher probability of convergence to global optimum compared with traditional techniques which take one-point search method. The introduction of basic theory on GAs, and the application examples in combination optimization of ten-member truss structure are presented in this paper.

• 산업기술연구
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• 제20권B호
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• pp.115-123
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• 2000

There are many optimal design packages, but they are big ones and they have only a few kinds of optimal algorithm coded with Fortran and it is sometimes necessary for user to write down some codes into their packages. So it is hard for user to learn how to use and customize them. More over, there are no commercial home-made software for optimum design. So, in this paper, several famous optimum algorithms are coded and modulized with C++ which is known as a suitable computer language in order to build up more algorithms into one computer software. All algorithms developed with C++ here were tested for comparison with optimization tool box of MATLAB and are superior to MATLAB.

• 수산해양교육연구
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• 제29권2호
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• pp.544-551
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• 2017

Metaheuristic algorithms are efficient techniques for a class of mathematical optimization problems without having to deeply adapt to the inherent nature of each problem. They are very useful for structural design optimization in which the cost of gradient computation can be very expensive. Among them, the characteristics of simulated annealing and genetic algorithms are briefly discussed. In Metropolis genetic algorithm, favorable features of Metropolis criterion in simulated annealing are incorporated in the reproduction operations of simple genetic algorithm. Numerical examples of structural design optimization are presented. The example structures are truss, breakwater and steel box girder bridge. From the theoretical evaluation and numerical experience, performance and applicability of metaheuristic algorithms for structural design optimization are discussed.