• 제목/요약/키워드: Metaheuristic Algorithm

검색결과 148건 처리시간 0.029초

무기-표적 할당 문제에 대한 메타휴리스틱의 성능 비교 (Comparative Study on Performance of Metaheuristics for Weapon-Target Assignment Problem)

  • 최용호;이영훈;김지은
    • 한국군사과학기술학회지
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    • 제20권3호
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    • pp.441-453
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    • 2017
  • In this paper, a new type of weapon-target assignment(WTA) problem has been suggested that reflects realistic constraints for sharing target with other weapons and shooting double rapid fire. To utilize in rapidly changing actual battle field, the computation time is of great importance. Several metaheuristic methods such as Simulated Annealing, Tabu Search, Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization have been applied to the real-time WTA in order to find a near optimal solution. A case study with a large number of targets in consideration of the practical cases has been analyzed by the objective value of each algorithm.

Metaheuristic Optimization Techniques for an Electromagnetic Multilayer Radome Design

  • Nguyen, Trung Kien;Lee, In-Gon;Kwon, Obum;Kim, Yoon-Jae;Hong, Ic-Pyo
    • Journal of electromagnetic engineering and science
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    • 제19권1호
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    • pp.31-36
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    • 2019
  • In this study, an effective method for designing an electromagnetic multilayer radome is introduced. This method is achieved by using ant colony optimization for a continuous domain in the transmission coefficient maximization with stability for a wide angle of incidence in both perpendicular and parallel polarizations in specific X- and Ku-bands. To obtain the optimized parameter for a C-sandwich radome, particle swarm optimization algorithm is operated to give a clear comparison on the effectiveness of ant colony optimization for a continuous domain. The qualification of an optimized multilayer radome is also compared with an effective solid radome type in transmitted power stability and presented in this research.

Subset selection in multiple linear regression: An improved Tabu search

  • Bae, Jaegug;Kim, Jung-Tae;Kim, Jae-Hwan
    • Journal of Advanced Marine Engineering and Technology
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    • 제40권2호
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    • pp.138-145
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    • 2016
  • This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

Design of Smart City Considering Carbon Emissions under The Background of Industry 5.0

  • Fengjiao Zhou;Rui Ma;Mohamad Shaharudin bin Samsurijan;Xiaoqin Xie
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제18권4호
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    • pp.903-921
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    • 2024
  • Industry 5.0 puts forward higher requirements for smart cities, including low-carbon, sustainable, and people-oriented, which pose challenges to the design of smart cities. In response to the above challenges, this study introduces the cyber-physical-social system (CPSS) and parallel system theory into the design of smart cities, and constructs a smart city framework based on parallel system theory. On this basis, in order to enhance the security of smart cities, a sustainable patrol subsystem for smart cities has been established. The intelligent patrol system uses a drone platform, and the trajectory planning of the drone is a key problem that needs to be solved. Therefore, a mathematical model was established that considers various objectives, including minimizing carbon emissions, minimizing noise impact, and maximizing coverage area, while also taking into account the flight performance constraints of drones. In addition, an improved metaheuristic algorithm based on ant colony optimization (ACO) algorithm was designed for trajectory planning of patrol drones. Finally, a digital environmental map was established based on real urban scenes and simulation experiments were conducted. The results show that compared with the other three metaheuristic algorithms, the algorithm designed in this study has the best performance.

적합성 함수를 이용한 2차원 저장소 적재 문제의 휴리스틱 알고리즘 (A Heuristic Algorithm for the Two-Dimensional Bin Packing Problem Using a Fitness Function)

  • 연용호;이선영;이종연
    • 정보처리학회논문지B
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    • 제16B권5호
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    • pp.403-410
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    • 2009
  • 2차원 저장소 적재는 NP-hard 문제로서 그 문제의 정확한 해를 구하는 것이 어려운 것으로 알려져 있으며, 이의 더 좋은 해를 얻기 위해 유전자(genetic) 알고리즘, 시뮬레이티드 어닐링(simulated annealing), 타부서치(tabu search)등과 같은 근사적 접근법이 제안되어 왔다. 하지만 분지한계(branch-and-bound)나 타부서치 기법들을 이용한 기존의 대표적인 근사 알고리즘들은 휴리스틱 알고리즘의 해에 기반을 둠으로 효율성이 낮고 반복수행에 의한 계산시간이 길다. 따라서 본 논문에서는 이러한 근사 알고리즘의 복잡성을 간소화하고, 알고리즘의 효율성을 높이기 위해 적재가능성을 판단하는 적합성 함수(fitness function)를 정의하고 이를 이용하여 어떤 특정 개체의 적재영역을 판단하는데 영향을 주는 적재영역의 수를 계산한다. 또한, 이들을 이용한 새로운 휴리스틱 알고리즘을 제안하였다. 끝으로 기존의 휴리스틱 또는 메타휴리스틱 기법과의 비교실험을 통해 기존의 휴리스틱 알고리즘인 FFF와 FBS에 비해 97%의 결과가 같거나 우수하였으며, 타부서치 알고리즘에 비해 86%의 결과가 같거나 우수한 것으로 나타났다.

A novel harmony search based optimization of reinforced concrete biaxially loaded columns

  • Nigdeli, Sinan Melih;Bekdas, Gebrail;Kim, Sanghun;Geem, Zong Woo
    • Structural Engineering and Mechanics
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    • 제54권6호
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    • pp.1097-1109
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    • 2015
  • A novel optimization approach for reinforced concrete (RC) biaxially loaded columns is proposed. Since there are several design constraints and influences, a new computation methodology using iterative analyses for several stages is proposed. In the proposed methodology random iterations are combined with music inspired metaheuristic algorithm called harmony search by modifying the classical rules of the employed algorithm for the problem. Differently from previous approaches, a detailed and practical optimum reinforcement design is done in addition to optimization of dimensions. The main objective of the optimization is the total material cost and the optimization is important for RC members since steel and concrete are very different materials in cost and properties. The methodology was applied for 12 cases of flexural moment combinations. Also, the optimum results are found by using 3 different axial forces for all cases. According to the results, the proposed method is effective to find a detailed optimum result with different number of bars and various sizes which can be only found by 2000 trial of an engineer. Thus, the cost economy is provided by using optimum bars with different sizes.

Subspace search mechanism and cuckoo search algorithm for size optimization of space trusses

  • Kaveh, A.;Bakhshpoori, T.
    • Steel and Composite Structures
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    • 제18권2호
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    • pp.289-303
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    • 2015
  • This study presents a strategy so-called Subspace Search Mechanism (SSM) for reducing the computational time for convergence of population based metaheusristic algorithms. The selected metaheuristic for this study is the Cuckoo Search algorithm (CS) dealing with size optimization of trusses. The complexity of structural optimization problems can be partially due to the presence of high-dimensional design variables. SSM approach aims to reduce dimension of the problem. Design variables are categorized to predefined groups (subspaces). SSM focuses on the multiple use of the metaheuristic at hand for each subspace. Optimizer updates the design variables for each subspace independently. Updating rules require candidate designs evaluation. Each candidate design is the assemblage of responsible set of design variables that define the subspace of interest. SSM is incorporated to the Cuckoo Search algorithm for size optimizing of three small, moderate and large space trusses. Optimization results indicate that SSM enables the CS to work with less number of population (42%), as a result reducing the time of convergence, in exchange for some accuracy (1.5%). It is shown that the loss of accuracy can be lessened with increasing the order of complexity. This suggests its applicability to other algorithms and other complex finite element-based engineering design problems.

Metaheuristic 알고리즘을 적용한 위상회전 기법에 의한 PAPR 감소에 관한 연구 (A Study on the PAPR Reduction Using Phase Rotation Method Applying Metaheuristic Algorithm)

  • 유선용;박비호;김완태;조성준
    • 대한전자공학회논문지TC
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    • 제46권5호
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    • pp.26-35
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    • 2009
  • OFDM (Orthogonal Frequency Division Multiplexing) 시스템은 주파수 선택적 페이딩 (frequency selective fading)과 협대역 간섭 (narrowband interference)에 강한 전송 방식으로 대용량 데이터 통신에 적합하다. 하지만 독립적으로 변조된 다수의 부반송파들이 동위상으로 중첩되면서 신호의 진폭이 증가하여 PAPR (Peak-to-Average Power Ratio)이 증가하는 문제가 발생한다. PAPR 문제를 해결하기 위해 제안된 위상회전 기법은 OFDM 신호에 위상 가중치를 곱하여 신호의 비선형 왜곡 없이 PAPR을 감소시킬 수 있지만, 위상 가중치를 탐색하는 과정에서 계산의 복잡도가 부블록 수에 따라 지수적으로 증가하는 단점이 있다. 따라서 위상회전 기법의 위상 탐색 과정에 계산의 복잡도를 감소시키면서 효율적으로 위상 가중치를 구할 수 있는 기술의 연구가 필요하다. 본 논문에서는 최적해를 구하기 위하여 사용되는 Metaheuristic 알고리즘을 위상탐색 과정에 적용하기 위한 모델링 과정을 제시하고 PTS 기법에 최적화함으로써 PAPR을 감소시키는 구조를 제안한다. 이 구조는 PTS 기법의 위상 탐색 과정에서 계산 복잡도가 지수적으로 증가하는 문제를 해결하고 PAPR 감소 성능도 보장할 수 있다. 제안하는 알고리즘을 통신 시스템에 적용하였을 때 PAPR 감소 효율을 시뮬레이션을 통해 분석했다.

Metaheuristic-reinforced neural network for predicting the compressive strength of concrete

  • Hu, Pan;Moradi, Zohre;Ali, H. Elhosiny;Foong, Loke Kok
    • Smart Structures and Systems
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    • 제30권2호
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    • pp.195-207
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    • 2022
  • Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

무선 브로드캐스트 애드혹 네트워크에서 네트워크 수명을 최대화하기 위한 타부서치 알고리즘 (Tabu search Algorithm for Maximizing Network Lifetime in Wireless Broadcast Ad-hoc Networks)

  • 장길웅
    • 한국정보통신학회논문지
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    • 제26권8호
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    • pp.1196-1204
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    • 2022
  • 본 논문은 브로드캐스트 전송방식을 사용하는 무선 애드혹 네트워크에서 네트워크 수명을 최대화하는 최적화 알고리즘을 제안한다. 본 논문에서 제안하는 최적화 알고리즘은 메모리 구조를 이용하여 로컬 검색 방법을 향상시키는 메타휴리스틱 방식인 타부서치 알고리즘을 적용한다. 제안된 타부서치 알고리즘은 네트워크 수명 최대화 문제에 대하여 효율적인 인코딩 방식과 인접해 검색 방법을 제안한다. 제안된 방식을 적용하여 효율적인 브로드캐스트 라우팅을 설계함으로써 전체 네트워크의 수명을 최대화한다. 제안된 타부서치 알고리즘은 네트워크에서 발생하는 브로드캐스트 전송에서 모든 노드의 소모 에너지와 최초 소실 노드 시점, 알고리즘 실행 시간 관점에서 평가되었다. 다양한 조건의 성능평가 결과에서 제안된 타부서치 알고리즘이 이전에 제안된 메타휴리스틱 알고리즘과 비교했을 때 더 우수함을 확인할 수 있었다.