• Title/Summary/Keyword: meta-heuristic search

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Optimal solution search method by using modified local updating rule in ACS-subpath algorithm (부경로를 이용한 ACS 탐색에서 수정된 지역갱신규칙을 이용한 최적해 탐색 기법)

  • Hong, SeokMi;Lee, Seung-Gwan
    • Journal of Digital Convergence
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    • v.11 no.11
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    • pp.443-448
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    • 2013
  • Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the total frequency of visits of the currently selected node in the previous iteration. I used the ACS algoritm using subpath for search. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.

An Application of Harmony Search Algorithm for Operational Cost Minimization of MicroGrid System (마이크로 그리드 운영비용 최소화를 위한 Harmony Search 알고리즘 응용)

  • Rhee, Sang-Bong;Kim, Kyu-Ho;Kim, Chul-Hwan
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.7
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    • pp.1287-1293
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    • 2009
  • This paper presents an application of Harmony Search (HM) meta-heuristic optimization algorithm for optimal operation of microgrid system. The microgrid system considered in this paper consists of a wind turbine, a diesel generator, and a fuel cell. An one day load profile which divided 20 minute data and wind resource for wind turbine generator were used for the study. In optimization, the HS algorithm is used for solving the problem of microgrid system operation which a various generation resources are available to meet the customer load demand with minimum operating cost. The application of HS algorithm to optimal operation of microgrid proves its effectiveness to determine optimally the generating resources without any differences of load mismatch and having its nature of fast convergency time as compared to other optimization method.

Applying a Tabu Search Approach for Solving the Two-Dimensional Bin Packing Problem (타부서치를 이용한 2차원 직사각 적재문제에 관한 연구)

  • Lee Sang-Heon;Lee Jeong-Min
    • Korean Management Science Review
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    • v.22 no.1
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    • pp.167-178
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    • 2005
  • The 2DBPP(Two-Dimensional Bin Packing Problem) is a problem of packing each item into a bin so that no two items overlap and the number of required bins is minimized under the set of rectangular items which may not be rotated and an unlimited number of identical .rectangular bins. The 2DBPP is strongly NP-hard and finds many practical applications in industry. In this paper we discuss a tabu search approach which includes tabu list, intensifying and diversification Strategies. The HNFDH(Hybrid Next Fit Decreasing Height) algorithm is used as an internal algorithm. We find that use of the proper parameter and function such as maximum number of tabu list and space utilization function yields a good solution in a reduced time. We present a tabu search algorithm and its performance through extensive computational experiments.

Optimum Design of Truss on Sizing and Shape with Natural Frequency Constraints and Harmony Search Algorithm (하모니 서치 알고리즘과 고유진동수 제약조건에 의한 트러스의 단면과 형상 최적설계)

  • Kim, Bong-Ik;Kown, Jung-Hyun
    • Journal of Ocean Engineering and Technology
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    • v.27 no.5
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    • pp.36-42
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    • 2013
  • We present the optimum design for the cross-sectional(sizing) and shape optimization of truss structures with natural frequency constraints. The optimum design method used in this paper employs continuous design variables and the Harmony Search Algorithm(HSA). HSA is a meta-heuristic search method for global optimization problems. In this paper, HSA uses the method of random number selection in an update process, along with penalty parameters, to construct the initial harmony memory in order to improve the fitness in the initial and update processes. In examples, 10-bar and 72-bar trusses are optimized for sizing, and 37-bar bridge type truss and 52-bar(like dome) for sizing and shape. Four typical truss optimization examples are employed to demonstrate the availability of HSA for finding the minimum weight optimum truss with multiple natural frequency constraints.

Weight optimization of coupling with bolted rim using metaheuristics algorithms

  • Mubina Nancy;S. Elizabeth Amudhini Stephen
    • Coupled systems mechanics
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    • v.13 no.1
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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.

A new meta-heuristic optimization algorithm using star graph

  • Gharebaghi, Saeed Asil;Kaveh, Ali;Ardalan Asl, Mohammad
    • Smart Structures and Systems
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    • v.20 no.1
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    • pp.99-114
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    • 2017
  • In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method.

Unit Commitment Using Tabu Search (Tabu Search를 이용한 발전기 기동정지계획)

  • Chun, H.J.;Kim, H.S.;Mun, K.J.;Hwang, G.H.;Lee, H.S.;Park, J.H.
    • Proceedings of the KIEE Conference
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    • 1999.07c
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    • pp.1098-1100
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    • 1999
  • This paper proposes a method of solving a unit commitment problem using tabu search (TS). The TS is efficient optimization method using meta-heuristic. To improve the diversification properties of TS, path relinking method is introduced. To show the usefulness of the proposed method, we performed an experiment for the system of 10 units. Numerical results show improvements in the generation cost and the computation time compared to previously obtained results.

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Applications of Harmony Search in parameter estimation of probability distribution models for non-homogeneous hydro-meteorological extreme events

  • Lee, Tae-Sam;Yoon, Suk-Min;Gang, Myung-Kook;Shin, Ju-Young;Jung, Chang-Sam
    • Proceedings of the Korea Water Resources Association Conference
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    • 2012.05a
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    • pp.258-258
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    • 2012
  • In frequency analyses of hydrological data, it is necessary for the interested variables to be homogenous and independent. However, recent evidences have shown that the occurrence of extreme hydro-meteorological events is influenced by large-scale climate variability, and the assumption of homogeneity does not generally hold anymore. Therefore, in order to associate the non-homogenous characteristics of hydro-meteorological variables, we propose the parameter estimation method of probability models using meta-heuristic algorithms, specifically harmony search. All the weather stations in South Korea were employed to demonstrate the performance of the proposed approaches. The results showed that the proposed parameter estimation method using harmony search is a comparativealternative for the probability distribution of the non-homogenous hydro-meteorological variables data.

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A Study on Methodology of the Snow Removal Operation of Air Wing Using Hybrid ACS Algorithm (하이브리드 ACS 알고리즘을 이용한 군 비행단 제설작전 방법연구)

  • Choi, Jung-Rock;Kim, Gak-Gyu;Lee, Sang-Heon
    • Korean Management Science Review
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    • v.30 no.2
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    • pp.31-42
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    • 2013
  • The vehicle routing problem (VRP) can be described as a problem to find the optimum traveling routes from one or several depot (s) to number of geographically scattered customers. This study executes a revised Heterogeneous Vehicle Routing Problem (HVRP) to minimize the cost that needs to conduct efficiently the snow removal operations of Air Wing under available resources and limited operations time. For this HVRP, we model the algorithm of an hybrid Ant Colony System (ACS). In the initial step for finding a solution, the modeled algorithm applies various alterations of a parameter that presents an amount of pheromone coming out from ants. This improvement of the initial solution illustrates to affect to derive better result ultimately. The purpose of this study proves that the algorithm using Hybrid heuristic incorporated in tabu and ACS develops the early studies to search best solution.

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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    • v.18 no.2
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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.