• Smart Structures and Systems
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• v.20 no.5
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• pp.607-618
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• 2017

In this research, a newly developed nature-inspired optimization method, the Lion Pride Optimization algorithm (LPOA), is utilized for optimal design of composite steel box girder bridges. A composite box girder bridge is one of the common types of bridges used for medium spans due to their economic, aesthetic, and structural benefits. The aim of the present optimization procedure is to provide a feasible set of design variables in order to minimize the weight of the steel trapezoidal box girders. The solution space is delimited by different types of design constraints specified by the American Association of State Highway and Transportation Officials. Additionally, the optimal solution obtained by LPOA is compared to the results of other well-established meta-heuristic algorithms, namely Gray Wolf Optimization (GWO), Ant Lion Optimizer (ALO) and the results of former researches. By this comparison the capability of the LPOA in optimal design of composite steel box girder bridges is demonstrated.

• Structural Engineering and Mechanics
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• v.42 no.6
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• pp.783-797
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• 2012

A new hybrid meta-heuristic optimization algorithm is presented for design of structures. The algorithm is based on the concepts of the charged system search (CSS) and the particle swarm optimization (PSO) algorithms. The CSS is inspired by the Coulomb and Gauss's laws of electrostatics in physics, the governing laws of motion from the Newtonian mechanics, and the PSO is based on the swarm intelligence and utilizes the information of the best fitness historically achieved by the particles (local best) and by the best among all the particles (global best). In the new hybrid algorithm, each agent is affected by local and global best positions stored in the charged memory considering the governing laws of electrical physics. Three different types of structures are optimized as the numerical examples with the new algorithm. Comparison of the results of the hybrid algorithm with those of other meta-heuristic algorithms proves the robustness of the new algorithm.

• Structural Engineering and Mechanics
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• v.58 no.3
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• pp.533-553
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• 2016

In this paper, the recently developed meta-heuristic algorithm called tug of war optimization is applied to optimal design of castellated beams. Two common types of laterally supported castellated beams are considered as design problems: beams with hexagonal openings and beams with circular openings. Here, castellated beams have been studied for two cases: beams without filled holes and beams with end-filled holes. Also, tug of war optimization algorithm is utilized for obtaining the solution of these design problems. For this purpose, the minimum cost is taken as the objective function, and some benchmark problems are solved from literature.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.73-82
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• 2011

There are a lot of people trying to solve the Traveling Salesman Problem (TSP) by using the Meta Heuristic Algorithms. TSP is an NP-Hard problem, and is used in testing search algorithms and optimization algorithms. Also TSP is one of the models of social problems. Many methods are proposed like Hybrid methods and Custom-built methods in Meta Heuristic. In this paper, we propose the S-MINE Algorithm to use the MINE Algorithm introduced in 2009 on the TSP.

• Structural Engineering and Mechanics
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• v.53 no.5
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• pp.847-865
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• 2015

This paper presents the application of a recently developed meta-heuristic algorithm, called Colliding Bodies Optimization (CBO), for size and topology optimization of steel trusses. This method is based on the one-dimensional collisions between two bodies, where each agent solution is considered as a body. The performance of the proposed algorithm is investigated through four benchmark trusses for minimum weight with static and dynamic constraints. A comparison of the numerical results of the CBO with those of other available algorithms indicates that the proposed technique is capable of locating promising solutions using lesser or identical computational effort, with no need for internal parameter tuning.

• Steel and Composite Structures
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• v.24 no.1
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• pp.129-140
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• 2017

Structural optimization is one of the popular and active research areas in the field of structural engineering. In the present study, the newly developed moth-flame optimization (MFO) algorithm and its enhanced version termed as enhanced moth-flame optimization (EMFO) are employed to implement the optimization process of planar and 3D steel frame structures with discrete design variables. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. A number of benchmark steel frame optimization problems are solved by the MFO and EMFO algorithms and the results are compared with those of other meta-heuristics. The obtained numerical results indicate that the proposed EMFO algorithm possesses better computational performance compared with other existing meta-heuristics.

• Journal of Korean Institute of Industrial Engineers
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• v.33 no.4
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• pp.447-456
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• 2007

The ant colony optimization (ACO) is a probabilistic Meta-heuristic algorithm which has been developed in recent years. Originally ACO was used for solving the well-known Traveling Salesperson Problem. More recently, ACO has been used to solve many difficult problems. In this paper, we develop an ant colony optimization method to solve the maximum independent set problem, which is known to be NP-hard. In this paper, we suggest a new method for local information of ACO. Parameters of the ACO algorithm are tuned by evolutionary operations which have been used in forecasting and time series analysis. To show the performance of the ACO algorithm, the set of instances from discrete mathematics and computer science (DIMACS)benchmark graphs are tested, and computational results are compared with a previously developed ACO algorithm and other heuristic algorithms.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.37-48
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• 2022

In this study, a hybrid meta-heuristic approach is proposed for solving engineering design optimization problems. Various approaches in many literatures have been proposed to solve engineering optimization problems with various types of decision variables and complex constraints. Unfortunately, however, their efficiencies for locating optimal solution do not be highly improved. Therefore, we propose a hybrid meta-heuristic approach for improving their weaknesses. the proposed GA-VNS-HC approach is combining genetic algorithm (GA) for global search with variable neighborhood search (VNS) and hill climbing (HC) for local search. In case study, various types of engineering design optimization problems are used for proving the efficiency of the proposed GA-VNS-HC approach

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.464-464
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• 2012

수문분야에 있어서 빈도해석의 목적은 특정 재현기간에 대한 발생 가능한 수문량의 규모를 파악하는데 있으며, 빈도해석의 정확도는 적합한 확률분포모형의 선택과 매개변수 추정방법에 의존하게 된다. 일반적으로 각 확률분포모형의 특성을 대표하는 매개변수를 추정하기 위해서는 모멘트 방법, 확률가중 모멘트 방법, 최대우도법 등을 이용하게 된다. 모멘트 방법에 의한 매개변수 추정은 해를 구하기 위한 과정이 단순한 반면, 비대칭형의 왜곡된 분포를 갖는 자료들에 대해서는 부정확한 결과를 나타내게 된다. 확률가중 모멘트 방법은 표본의 크기가 작거나 왜곡된 자료일 경우에도 비교적 안정적인 결과를 제공하는 반면, 확률 가중치가 정수로만 제한되는 단점을 갖고 있다. 그리고 대수 우도함수를 이용하여 매개변수를 추정하게 되는 최우도법은 가장 효율적인 매개변수 추정치를 얻을 수 있는 것으로 알려져 있으나, 비선형 연립방정식으로 표현되는 해를 구하기 위해서는 Newton-Raphson 방법을 사용하는 등 절차가 복잡하며, 때로는 수렴이 되지 않아 해룰 구하지 못하는 경우가 발생되게 된다. 이에 반해, 최근의 Genetic Algorithm, Ant Colony Optimization 및 Simulated Annealing과 같은 Meta-Heuristic Algorithm들은 복잡합 공학적 최적화 문제 있어서 효율적인 대안으로 주목받고 있으며, Hassanzadeh et al.(2011)에 의해 수문학적 빈도해석을 위한 매개변수 추정에 있어서도 그 적용성이 검증된바 있다. 본 연구의 목적은 연 최대강수 자료의 빈도해석에 적용되는 확률분포모형들의 매개변수 추정을 위해 Meta-Heuristic Algorithm을 적용하고자 함에 있다. 따라서 본 연구에서는 매개변수 추정을 위한 방법으로 Genetic Algorithm 및 Harmony Search를 적용하였고, 그 결과를 최우도법에 의한 결과와 비교하였다. GEV 분포를 이용하여 Simulation Test를 수행한 결과 Genetic Algorithm을 이용하여 추정된 매개변수들은 최우도법에 의한 결과들과 비교적 유사한 분포를 나타내었으나 과도한 계산시간이 요구되는 것으로 나타났다. 하지만 Harmony Search를 이용하여 추정된 매개변수들은 최우도법에 의한 결과들과 유사한 분포를 나타내었을 뿐만 아니라 계산시간 또한 매우 짧은 것으로 나타났다. 또한 국내 74개소의 강우관측소 자료와 Gamma, Log-normal, GEV 및 Gumbel 분포를 이용한 실증연구에 있어서도 Harmony Search를 이용한 매개변수 추정은 효율적인 매개 변수 추정치를 제공하는 것으로 나타났다.

• Structural Engineering and Mechanics
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• v.45 no.2
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• pp.183-199
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• 2013

Current methods of analysis of trusses depend on matrix formulations based on equilibrium equations which are in fact derived from energy principles, and compatibility conditions. Recently it has been shown that the minimum energy principle, by itself, in its pure and unmodified form, can well be exploited to analyze structures when coupled with an optimization algorithm, specifically with a meta-heuristic algorithm. The resulting technique that can be called Total Potential Optimization using Meta-heuristic Algorithms (TPO/MA) has already been applied to analyses of linear and nonlinear plane trusses successfully as coupled with simulated annealing and local search algorithms. In this study the technique is applied to both 2-dimensional and 3-dimensional trusses emphasizing robustness, reliability and accuracy. The trials have shown that the technique is robust in two senses: all runs result in answers, and all answers are acceptable as to the reliability and accuracy within the prescribed limits. It has also been shown that Harmony Search presents itself as an appropriate algorithm for the purpose.