• Title/Summary/Keyword: combinatorial search

검색결과 111건 처리시간 0.025초

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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    • 제1권1호
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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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Search for Phosphors for Use in Displays and Lightings using Heuristics-based Combinatorial Materials Science

  • Sharma, Asish Kumar;Sohn, Kee-Sun
    • 한국정보디스플레이학회:학술대회논문집
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    • 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
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    • pp.207-210
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    • 2009
  • According to the recent demand for materials for use in various displays and solid state lightings, new phosphors with improved performance have been pursued consistently. Multi objective genetic algorithm assisted combinatorial material search (MOGACMS) strategies have been applied to various multi-compositional inorganic systems to search for new phosphors and to optimize the properties of phosphors.

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DEVELOPMENT OF A TABU SEARCH HEURISTIC FOR SOLVING MULTI-OBJECTIVE COMBINATORIAL PROBLEMS WITH APPLICATIONS TO CONSTRUCTING DISCRETE OPTIMAL DESIGNS

  • JOO SUNG JUNG;BONG JIN YUM
    • Management Science and Financial Engineering
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    • 제3권1호
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    • pp.75-88
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    • 1997
  • Tabu search (TS) has been successfully applied for solving many complex combinatorial optimization problems in the areas of operations research and production control. However, TS is for single-objective problems in its present form. In this article, a TS-based heuristic is developed to determine Pareto-efficient solutions to a multi-objective combinatorial optimization problem. The developed algorithm is then applied to the discrete optimal design problem in statistics to demonstrate its usefulness.

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Neighbor Generation Strategies of Local Search for Permutation-based Combinatorial Optimization

  • Hwang, Junha
    • 한국컴퓨터정보학회논문지
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    • 제26권10호
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    • pp.27-35
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    • 2021
  • 지역 탐색은 다양한 조합 최적화 문제들을 해결하기 위해 활용되어 왔다. 지역 탐색에 있어서 가장 중요한 요소 중 하나가 이웃해를 생성하는 방법이다. 본 논문에서는 순열 기반 조합 최적화를 위한 지역 탐색의 이웃해 생성 전략들을 제안하고, 순회 외판원 문제를 대상으로 각 전략들의 성능을 비교한다. 본 논문에서는 총 10가지 이웃해 생성 전략을 제안한다. 기본적으로 기존에 많이 사용했던 Swap 등 4가지 전략 이외에 Rotation 등 4가지 기법을 새롭게 제안한다. 이외에 기본 이웃해 생성 전략들을 결합하여 만든 Combined1과 Combined2가 있다. 실험은 기본적인 지역 탐색을 적용하되 이웃해 생성 전략만 변경하여 수행하였다. 실험 결과, 이웃해 생성 전략에 따라 성능 차이가 큰 것을 확인하였으며 아울러 Combined2의 성능이 가장 좋음을 확인하였다. 뿐만 아니라 Combined2는 기존의 지역 탐색 기법들보다 더 좋은 성능을 발휘함을 확인하였다.

Profit-based Thermal Unit Maintenance Scheduling under Price Volatility by Reactive Tabu Search

  • Sugimoto Junjiro;Yokoyama Ryuichi
    • KIEE International Transactions on Power Engineering
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    • 제5A권4호
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    • pp.331-338
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    • 2005
  • In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

Minimum-weight design of non-linear steel frames using combinatorial optimization algorithms

  • Hayalioglu, M.S.;Degertekin, S.O.
    • Steel and Composite Structures
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    • 제7권3호
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    • pp.201-217
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    • 2007
  • Two combinatorial optimization algorithms, tabu search and simulated annealing, are presented for the minimum-weight design of geometrically non-linear steel plane frames. The design algorithms obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Stress constraints of AISC Load and Resistance Factor Design (LRFD) specification, maximum and interstorey drift constraints and size constraints for columns were imposed on frames. The stress constraints of AISC Allowable Stress Design (ASD) were also mounted in the two algorithms. The comparisons between AISC-LRFD and AISC-ASD specifications were also made while tabu search and simulated annealing were used separately. The algorithms were applied to the optimum design of three frame structures. The designs obtained using tabu search were compared to those where simulated annealing was considered. The comparisons showed that the tabu search algorithm yielded better designs with AISC-LRFD code specification.

Combinatorial particle swarm optimization for solving blocking flowshop scheduling problem

  • Eddaly, Mansour;Jarboui, Bassem;Siarry, Patrick
    • Journal of Computational Design and Engineering
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    • 제3권4호
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    • pp.295-311
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    • 2016
  • This paper addresses to the flowshop scheduling problem with blocking constraints. The objective is to minimize the makespan criterion. We propose a hybrid combinatorial particle swarm optimization algorithm (HCPSO) as a resolution technique for solving this problem. At the initialization, different priority rules are exploited. Experimental study and statistical analysis were performed to select the most adapted one for this problem. Then, the swarm behavior is tested for solving a combinatorial optimization problem such as a sequencing problem under constraints. Finally, an iterated local search algorithm based on probabilistic perturbation is sequentially introduced to the particle swarm optimization algorithm for improving the quality of solution. The computational results show that our approach is able to improve several best known solutions of the literature. In fact, 76 solutions among 120 were improved. Moreover, HCPSO outperforms the compared methods in terms of quality of solutions in short time requirements. Also, the performance of the proposed approach is evaluated according to a real-world industrial problem.

SA-selection-based Genetic Algorithm for the Design of Fuzzy Controller

  • Han Chang-Wook;Park Jung-Il
    • International Journal of Control, Automation, and Systems
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    • 제3권2호
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    • pp.236-243
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    • 2005
  • This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

퍼지로직제어에 의해 강화된 혼합유전 알고리듬 (Hybrid Genetic Algorithm Reinforced by Fuzzy Logic Controller)

  • 윤영수
    • 대한산업공학회지
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    • 제28권1호
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    • pp.76-86
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    • 2002
  • In this paper, we suggest a hybrid genetic algorithm reinforced by a fuzzy logic controller (flc-HGA) to overcome weaknesses of conventional genetic algorithms: the problem of parameter fine-tuning, the lack of local search ability, and the convergence speed in searching process. In the proposed flc-HGA, a fuzzy logic controller is used to adaptively regulate the fine-tuning structure of genetic algorithm (GA) parameters and a local search technique is applied to find a better solution in GA loop. In numerical examples, we apply the proposed algorithm to a simple test problem and two complex combinatorial optimization problems. Experiment results show that the proposed algorithm outperforms conventional GAs and heuristics.

경험적 탐색에 의한 배전계통 보호기기 정정협조 (Heuristic Search in Overcurrent Protection Coordination of Power Distribution Systems)

  • 이승재;박영문;이정원
    • 대한전기학회논문지
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    • 제40권2호
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    • pp.135-143
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    • 1991
  • The setting problem of protective devices in power distribution systems is a combinatorial search problem involving a lot of trial & errors to search for a proper solution which has a great impact for the system reliability. This paper discusses the nature of the problem and proposes a scheme to prune the decision tree achieving the efficient search for a setting solution by applying heuristic rules which utilize pattern informations of backup and primary devices. Its effectiveness is demonstrated through many tests on real systems.

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