• Title, Summary, Keyword: Hybrid evolutionary algorithm

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A Hybrid of Evolutionary Search and Local Heuristic Search for Combinatorial Optimization Problems

  • Park, Lae-Jeong;Park, Cheol-Hoon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.1 no.1
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    • pp.6-12
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    • 2001
  • Evolutionary algorithms(EAs) have been successfully applied to many combinatorial optimization problems of various engineering fields. Recently, some comparative studies of EAs with other stochastic search algorithms have, however, shown that they are similar to, or even are not comparable to other heuristic search. In this paper, a new hybrid evolutionary algorithm utilizing a new local heuristic search, for combinatorial optimization problems, is presented. The new intelligent local heuristic search is described, and the behavior of the hybrid search algorithm is investigated on two well-known problems: traveling salesman problems (TSPs), and quadratic assignment problems(QAPs). The results indicate that the proposed hybrid is able to produce solutions of high quality compared with some of evolutionary and simulated annealing.

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Optimal Environmental and Economic Operation using Evolutionary Computation and Neural Networks (진화연산과 신경망이론을 이용한 전력계통의 최적환경 및 경제운용)

  • Rhee, Sang-Bong;Kim, Kyu-Ho;You, Seok-Ku
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.48 no.12
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    • pp.1498-1506
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    • 1999
  • In this paper, a hybridization of Evolutionary Strategy (ES) and a Two-Phase Neural Network(TPNN) is applied to the optimal environmental and economic operation. As the evolutionary computation, ES is to search for the global optimum based on natural selection and genetics but it shows a defect of reducing the convergence rate in the latter part of search, and often does not search the exact solution. Also, neural network theory as a local search technique can be used to search a more exact solution. But it also has the defect that a solution frequently sticks to the local region. So, new algorithm is presented as hybrid methods by combining merits of two methods. The hybrid algorithm has been tested on Emission Constrained Economic Dispatch (ECED) problem and Weighted Emission Economic Dispatch (WEED) problem for optimal environmental and economic operation. The result indicated that the hybrid approach can outperform the other computational efficiency and accuracy.

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Classifier System and Co-evolutionary Hybrid Approach to Restoration Service of Electric Power Distribution Networks

  • Filipiak, Sylwester
    • Journal of Electrical Engineering and Technology
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    • v.7 no.3
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    • pp.288-296
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    • 2012
  • The method proposed by the author is intended for assistance in decision-making (concerning changes of connections) by operators of complex distribution systems during states of malfunction (particularly in the events of malfunctions, for which the consequences encompass extended parts of the network), through designation of connection action scenarios (creating substitute configurations). It is the use by the classifying system working with the co-evolution algorithm that enables the effective creation of substitute scenarios for the Medium Voltage electric power distribution network. The author also completed works concerning the possibility of using cooperation of the evolutionary algorithm and the co-evolutionary algorithm with local search algorithms. The method drawn up may be used in current systems managing the work of distribution networks to assist network operators in taking decisions concerning connection actions in supervised electric power systems.

A Hybrid Method for Improvement of Evolutionary Computation (진화 연산의 성능 개선을 위한 하이브리드 방법)

  • Chung, Jin-Ki;Oh, Se-Young
    • Journal of Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.317-322
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    • 2002
  • The major operations of Evolutionary Computation include crossover, mutation, competition and selection. Although selection does not create new individuals like crossover or mutation, a poor selection mechanism may lead to problems such as taking a long time to reach an optimal solution or even not finding it at all. In view of this, this paper proposes a hybrid Evolutionary Programming (EP) algorithm that exhibits a strong capability to move toward the global optimum even when stuck at a local minimum using a synergistic combination of the following three basic ideas. First, a "local selection" technique is used in conjunction with the normal tournament selection to help escape from a local minimum. Second, the mutation step has been improved with respect to the Fast Evolutionary Programming technique previously developed in our research group. Finally, the crossover and mutation operations of the Genetic Algorithm have been added as a parallel independent branch of the search operation of an EP to enhance search diversity.

An evolutionary hybrid optimization of MARS model in predicting settlement of shallow foundations on sandy soils

  • Luat, Nguyen-Vu;Nguyen, Van-Quang;Lee, Seunghye;Woo, Sungwoo;Lee, Kihak
    • Geomechanics and Engineering
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    • v.21 no.6
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    • pp.583-598
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    • 2020
  • This study is attempted to propose a new hybrid artificial intelligence model called integrative genetic algorithm with multivariate adaptive regression splines (GA-MARS) for settlement prediction of shallow foundations on sandy soils. In this hybrid model, the evolution algorithm - Genetic Algorithm (GA) was used to search and optimize the hyperparameters of multivariate adaptive regression splines (MARS). For this purpose, a total of 180 experimental data were collected and analyzed from available researches with five-input variables including the bread of foundation (B), length to width (L/B), embedment ratio (Df/B), foundation net applied pressure (qnet), and average SPT blow count (NSPT). In further analysis, a new explicit formulation was derived from MARS and its accuracy was compared with four available formulae. The attained results indicated that the proposed GA-MARS model exhibited a more robust and better performance than the available methods.

A Nodes Set Based Hybrid Evolutionary Strategy on the Rectilinear Steiner Tree Problem (점집합을 개체로 이용한 직각거리 스타이너 나무 문제의 하이브리드 진화 전략에 관한 연구)

  • Yang Byoung-Hak
    • Korean Management Science Review
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    • v.23 no.1
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    • pp.75-85
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    • 2006
  • The rectilinear Steiner tree problem (RSTP) is to find a minimum-length rectilinear interconnection of a set of terminals in the plane. It is well known that the solution to this problem will be the minimal spanning tree(MST) on some set Steiner points. The RSTP is known to be NP-complete. The RSTP has received a lot of attention in the literature and heuristic and optimal algorithms have been proposed. A key performance measure of the algorithm for the RSTP is the reduction rate that is achieved by the difference between the objective value of the RSTP and that of the MST without Steiner points. A hybrid evolutionary strategy on RSTP based upon nodes set is presented. The computational results show that the hybrid evolutionary strategy is better than the previously proposed other heuristic. The average reduction rate of solutions from the evolutionary strategy is about 11.14%, which is almost similar to that of optimal solutions.

Multi-objective Optimization of a Laidback Fan Shaped Film-Cooling Hole Using Evolutionary Algorithm

  • Lee, Ki-Don;Husain, Afzal;Kim, Kwang-Yong
    • International Journal of Fluid Machinery and Systems
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    • v.3 no.2
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    • pp.150-159
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    • 2010
  • Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

An Estimation of Fitness Evaluation in Evolutionary Algorithm for the Rectilinear Steiner Tree Problem (직각거리 스타이너 나무 문제의 하이브리드 진화 해법에서 효율적인 적합도 추정에 관한 연구)

  • Yang, Byoung-Hak
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.589-598
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    • 2006
  • The rectilinear Steiner tree problem is to find a minimum-length rectilinear interconnection of a set of terminals in the plane. It is well known that the solution to this problem will be the minimal spanning tree (MST) on some set Steiner points. A hybrid evolutionary algorithm is introduced based upon the Prim algorithm. The Prim algorithm for the fitness evaluation requires heavy calculation time. The fitness value of parents is inherited to their child and the fitness value of child is estimated by the inherited structure of tree. We introduce four alternative evolutionary algorithms, Experiment result shows that the calculation time is reduced to 25% without loosing the solution quality by using the fitness estimation.

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Security Constrained Optimal Power Flow by Hybrid Algorithms (하이브리드 알고리즘을 응용하여 안전도제약을 만족시키는 최적전력조류)

  • Kim, Gyu-Ho;Lee, Sang-Bong;Lee, Jae-Gyu;Yu, Seok-Gu
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.49 no.6
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    • pp.305-311
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    • 2000
  • This paper presents a hybrid algorithm for solving optimal power flow(OPF) in order to enhance a systems capability to cope with outages, which is based on combined application of evolutionary computation and local search method. The efficient algorithm combining main advantages of two methods is as follows : Firstly, evolutionary computation is used to perform global exploitation among a population. This gives a good initial point of conventional method. Then, local methods are used to perform local exploitation. The hybrid approach often outperforms either method operating alone and reduces the total computation time. The objective function of the security constrained OPF is the minimization of generation fuel costs and real power losses. The resulting optimal operating point has to be feasible after outages such as any single line outage(respect of voltage magnitude, reactive power generation and power flow limits). In OPF considering security, the outages are selected by contingency ranking method(contingency screening model). The OPF considering security, the outages are selected by contingency ranking method(contingency screening model). The method proposed is applied to IEEE 30 buses system to show its effectiveness.

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Distributed Database Design using Evolutionary Algorithms

  • Tosun, Umut
    • Journal of Communications and Networks
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    • v.16 no.4
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    • pp.430-435
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    • 2014
  • The performance of a distributed database system depends particularly on the site-allocation of the fragments. Queries access different fragments among the sites, and an originating site exists for each query. A data allocation algorithm should distribute the fragments to minimize the transfer and settlement costs of executing the query plans. The primary cost for a data allocation algorithm is the cost of the data transmission across the network. The data allocation problem in a distributed database is NP-complete, and scalable evolutionary algorithms were developed to minimize the execution costs of the query plans. In this paper, quadratic assignment problem heuristics were designed and implemented for the data allocation problem. The proposed algorithms find near-optimal solutions for the data allocation problem. In addition to the fast ant colony, robust tabu search, and genetic algorithm solutions to this problem, we propose a fast and scalable hybrid genetic multi-start tabu search algorithm that outperforms the other well-known heuristics in terms of execution time and solution quality.