• International Journal of Aeronautical and Space Sciences
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• 제14권4호
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• pp.341-349
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• 2013

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

• Architectural research
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• 제11권1호
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• pp.15-23
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• 2009

An application of the constrained adaptive topology optimization (CATO) algorithm is described for three-dimensional topology optimization of engineering structures. The enhanced assumed strain lower order solid finite element (FE) is used to evaluate the values of objective and constraint functions required in optimization process. The strain energy (SE) terms such as elastic and modal SEs are employed as the objective function to be minimized and the initial volume of structures is introduced as the constraint function. The SIMP model is adopted to facilitate the material redistribution and also to produce clearer and more distinct structural topologies. The linearly weighted objective function is introduced to consider both static and dynamic characteristics of structures. Several numerical tests are tackled and it is used to investigate the performance of the proposed three-dimensional topology optimization process. From numerical results, it is found to be that the CATO algorithm is easy to implement and extremely applicable to produce the reasonable optimum topologies for three dimensional optimization problems.

• Steel and Composite Structures
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• 제24권1호
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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.

• 한국통신학회논문지
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• 제32권11A호
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• pp.1079-1085
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• 2007

NSGA (Non-dominated Sorting Algorithm) 는 다목적 최적화 분야에서 널리 사용되고 있는 비지배 정렬 기반의 유전자 알고리즘으로 최적화를 요구하는 분야에서 널리 사용되고 있다. 하지만 연산의 복잡도, 사전 우수해 선별 조건의 미흡함과 공유 변수값 결정의 어려움등이 문제로 제기 되었고, 이러한 단점을 보완한 NSGA-II(Non-dominated Sorting Algorithm-B) 알고리즘이 제안되었다. 그러나 기존의 NSGA-II알고리즘은 다목적 최적화 알고리즘과 동일하게 목적치를 최대화 또는 최소화시키는 방향으로 최적화가 진행되어 선택적인 최적화 수행이 어렵다. 이러한 문제점을 보완하기 위하여 본 논문에서는 NSGA-II알고리즘이 가지는 장점을 바탕으로 설계자의 요구조건에 종속적으로 최적화 과정을 수행할 수 있는 GBNSGA-II (Goal-pareto Based NSGA-II)를 제안하고 기존의 NSGA-II알고리즘과 비교를 통해 성능의 우수성을 검증하였다.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 추계학술대회논문집 - 한국공작기계학회
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• pp.443-448
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• 2000

In this paper, multiphase optimization of machine structure is presented. The goal of first step is to obtain (i) light weight, (ii) rigidity statically. In this step, multiple optimization problem with two objective functions is treated using Pareto Genetic Algorithm. Where two objective functions are weight of the structure, and static compliance. The method is applied to a new machine structure design.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.107.6-107
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• 2001

Genetic alogorithm(GA) is a well-known global optimization algorithm. However, as the searching bounds grow wider., performance of local optimization deteriorates. In this paper, we propose a hybrid algorithm which integrates the gradient algorithm and GA so as to reinforce the performance of local optimization. We apply this algorithm to the system identification of second order RLC circuit. Identification results show that the proposed algorithm gets the better and robust performance to find the exact values of RLC elements.

• Journal of Information Processing Systems
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• 제13권4호
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• pp.1000-1013
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• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.4864-4882
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• 2016

In this work, we consider the optimization problem of minimizing energy consumption for real-time multicast over wireless multi-hop networks. Previously, a distributed primal-dual subgradient algorithm was used for finding a solution to the optimization problem. However, the traditional subgradient algorithms have drawbacks in terms of i) sensitivity to iteration parameters; ii) need for saving previous iteration results for computing the optimization results at the current iteration. To overcome these drawbacks, using a joint network coding and scheduling optimization framework, we propose a novel distributed primal-dual Random Deflected Subgradient (RDS) algorithm for solving the optimization problem. Furthermore, we derive the corresponding recursive formulas for the proposed RDS algorithm, which are useful for practical applications. In comparison with the traditional subgradient algorithms, the illustrated performance results show that the proposed RDS algorithm can achieve an improved optimal solution. Moreover, the proposed algorithm is stable and robust against the choice of parameter values used in the algorithm.

• 한국자동차공학회논문집
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• 제6권5호
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• pp.45-54
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• 1998

For proper release of side airbags, the onset of crash should be detected first. After crash detection, the algorithm has to make a decision whether the side airbag deployment is necessary. If the deployment is necessary, proper timing has to be provided for the maximum protection of driver or passenger. The side airbag release algorithm should be robust against the statistical deviations which are inherent to experimental crash test data. Deterministic optimization algorithms cannot be used for the side aribag release algorithm since the objective function cannot be expressed in a closed form. From this background, genetic algorithm has been used for the optimization. The optimization requires moderate amount of computation and gives satisfactory results.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1169-1182
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• 2020

The ant colony optimization (ACO) algorithm is a classical metaheuristic optimization algorithm. However, the conventional ACO was liable to trap in the local minimum and has an inherent slow rate of convergence. In this work, we propose a novel combinatorial ACO algorithm (CG-ACO) to alleviate these limitations. The genetic algorithm and the cloud model were embedded into the ACO to find better initial solutions and the optimal parameters. In the experiment section, we compared CG-ACO with the state-of-the-art methods and discussed the parameter stability of CG-ACO. The experiment results showed that the CG-ACO achieved better performance than ACO_R, simple genetic algorithm (SGA), CQPSO and CAFSA and was more likely to reach the global optimal solution.