• Title/Summary/Keyword: Probabilistic Search

Search Result 98, Processing Time 0.029 seconds

A Study on a New Function Optimization Method Using Probabilistic Tabu Search Strategy (확률적 타부 탐색 전략을 이용한 새로운 함수 최적화 방법에 관한 연구)

  • Kim, Hyung-Su;Hwang, Gi-Hyun;Park, June-Ho
    • The Transactions of the Korean Institute of Electrical Engineers D
    • /
    • v.50 no.11
    • /
    • pp.532-540
    • /
    • 2001
  • In this paper, we propose a probabilistic tabu search strategy for function optimization. It is composed of two procedures, one is Basic search procedure that plays a role in local search, and the other is Restarting procedure that enables to diversify search region. In basic search procedure, we use Belief space and Near region to create neighbors. Belief space is made of high-rank neighbors to effectively restrict searching space, so it can improve searching time and local or global searching capability. When a solution is converged in a local area, Restarting procedure works to search other regions. In this time, we use Probabilistic Tabu Strategy(PTS) to adjust parameters such as a reducing rate, initial searching region etc., which makes enhance the performance of searching ability in various problems. In order to show the usefulness of the proposed method, the PTS is applied to the minimization problems such as De Jong functions, Ackley function, and Griewank functions etc., the results are compared with those of GA or EP.

  • PDF

Probabilistic Model for Performance Analysis of a Heuristic with Multi-byte Suffix Matching

  • Choi, Yoon-Ho
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.7 no.4
    • /
    • pp.711-725
    • /
    • 2013
  • A heuristic with multi-byte suffix matching plays an important role in real pattern matching algorithms. By skipping many characters at a time in the process of comparing a given pattern with the text, the pattern matching algorithm based on a heuristic with multi-byte suffix matching shows a faster average search time than algorithms based on deterministic finite automata. Based on various experimental results and simulations, the previous works show that the pattern matching algorithms with multi-byte suffix matching performs well. However, there have been limited studies on the mathematical model for analyzing the performance in a standard manner. In this paper, we propose a new probabilistic model, which evaluates the performance of a heuristic with multi-byte suffix matching in an average-case search. When the theoretical analysis results and experimental results were compared, the proposed probabilistic model was found to be sufficient for evaluating the performance of a heuristic with suffix matching in the real pattern matching algorithms.

Hydro-Thermal Optimal Scheduling Using Probabilistic Tabu Search (확률 타부 탐색법을 이용한 수화력 계통의 경제운용에 관한 연구)

  • Kim, Hyeong-Su;Mun, Gyeong-Jun;Park, Jun-Ho
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.51 no.3
    • /
    • pp.153-161
    • /
    • 2002
  • In this paper, we propose a Probabilistic Tabu Search(PTS) method for hydro-thermal scheduling. Hydro scheduling has many constraints and very difficult to solve the optical schedule because it has many local minima. To solve the problem effectively, the proposed method uses two procedures, one is Tabu search procedure that plays a role in local search, and the other is Restarting procedure that enables to diversify its search region. To adjust Parameters such as a reducing rate and initial searching region, search strategy is selected according to its probability after restarting procedure. Dynamic decoding method was also used to restrict a search region and to handle water balance constraints. In order to show the usefulness of the proposed method, the PTS is applied on two cases which have independent or dependent hydro plants and compared to those of other method. The simulation results show it is very efficient and useful algorithm to solve the hydro-thermal scheduling problem.

Extended Query Search Performance Evaluations for Vector Model and Probabilistic Model of Information System (정보검색시스템의 확률 및 벡터모델에 대한 질의 확장 검색 성능 평가)

  • 전유정;변동률;박순철
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.9 no.1
    • /
    • pp.36-42
    • /
    • 2004
  • In this paper, we compare the vector model performance with the probabilistic model of information system. We use LSI(Latent Semantic Indexing) model for vector model, while Condor information search system that is ready to sell on business is used as a probabilistic model. Each model produces the search results from the original queries and the queries extended by a dictionary definition. We compare those results between two models and find out the vector model is much better than the probabilistic model for the most queries.

  • PDF

A Comparative Study on Reliability Index and Target Performance Measure Based Probabilistic Structural Design Optimizations (신뢰도지수와 목표성능치에 기반한 확률론적 구조설계 최적화기법에 대한 비교연구)

  • 양영순;이재옥
    • Proceedings of the Computational Structural Engineering Institute Conference
    • /
    • 2000.10a
    • /
    • pp.32-39
    • /
    • 2000
  • Probabilistic structural design optimization, which is characterized by the so-called probabilistic. constraints which introduce permissible probability of violation, is preferred to deterministic design optimization since unpredictable inherent uncertainties and randomness in structural and environmental properties are to be taken quantitatively into account by probabilistic design optimization. In this paper, the well-known reliability index based MPFP(Most Probable Failure Point) search approach and the newly introduced target performance measure based MPTP(Minimum Performance Target Point) search approach are summarized and compared. The present comparison focuses on the number of iterations required for the estimation of probabilistic constraints and a technique for improvement which removes exhaustive iterations is presented as well. A 10 bar truss problem is examined for this.

  • PDF

Hydro-Thermal Optimal Scheduling Using Probabilistic Tabu Search (확률 타부 탐색법을 이용한 수화력 계통의 경제운용)

  • Kim, Hyung-Su;Park, June-Ho
    • Proceedings of the KIEE Conference
    • /
    • 2002.11b
    • /
    • pp.76-79
    • /
    • 2002
  • In this paper, we propose a Probabilistic Tabu Search(PTS) method for hydro-thermal scheduling. Hydro scheduling has many constraints and very difficult to solve the optimal schedule because it has many local minima. To solve the problem effectively, the proposed method uses two procedures, one is Tabu search procedure that plays a role in local search, and the other is Restarting procedure that enables to diversify its search region. To adjust parameters such as a reducing rate and initial searching region, search strategy is selected according to its probability after Restarting procedure. In order to show the usefulness of the proposed method, the PTS is applied on two cases which have dependent hydro plants and compared to those of other method. The simulation results show it is very efficient and useful algorithm to solve the hydro-thermal scheduling problem.

  • PDF

A Minimum Expected Length Insertion Algorithm and Grouping Local Search for the Heterogeneous Probabilistic Traveling Salesman Problem (이종 확률적 외판원 문제를 위한 최소 평균거리 삽입 및 집단적 지역 탐색 알고리듬)

  • Kim, Seung-Mo;Choi, Ki-Seok
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.33 no.3
    • /
    • pp.114-122
    • /
    • 2010
  • The Probabilistic Traveling Salesman Problem (PTSP) is an important topic in the study of traveling salesman problem and stochastic routing problem. The goal of PTSP is to find a priori tour visiting all customers with a minimum expected length, which simply skips customers not requiring a visit in the tour. There are many existing researches for the homogeneous version of the problem, where all customers have an identical visiting probability. Otherwise, the researches for the heterogeneous version of the problem are insufficient and most of them have focused on search base algorithms. In this paper, we propose a simple construction algorithm to solve the heterogeneous PTSP. The Minimum Expected Length Insertion (MELI) algorithm is a construction algorithm and consists of processes to decide a sequence of visiting customers by inserting the one, with the minimum expected length between two customers already in the sequence. Compared with optimal solutions, the MELI algorithm generates better solutions when the average probability is low and the customers have different visiting probabilities. We also suggest a local search method which improves the initial solution generated by the MELI algorithm.

Severe Accident Management Using PSA Event Tree Technology

  • Choi, Young;Jeong, Kwang Sub;Park, SooYong
    • International Journal of Safety
    • /
    • v.2 no.1
    • /
    • pp.50-56
    • /
    • 2003
  • There are a lot of uncertainties in the severe accident phenomena and scenarios in nuclear power plants (NPPs) and one of the major issues for severe accident management is the reduction of these uncertainties. The severe accident management aid system using Probabilistic Safety Assessments (PSA) technology is developed for the management staff in order to reduce the uncertainties. The developed system includes the graphical display for plant and equipment status, previous research results by a knowledge-base technique, and the expected plant behavior using PSA. The plant model used in this paper is oriented to identify plant response and vulnerabilities via analyzing the quantified results, and to set up a framework for an accident management program based on these analysis results. Therefore the developed system may playa central role of information source for decision-making for severe accident management, and will be used as a training tool for severe accident management.

A Probabilistic Filtering Technique for Improving the Efficiency of Local Search (국지적 탐색의 효율향상을 위한 확률적 여과 기법)

  • Kang, Byoung-Ho;Ryu, Kwang-Ryel
    • Journal of KIISE:Software and Applications
    • /
    • v.34 no.3
    • /
    • pp.246-254
    • /
    • 2007
  • Local search algorithms start from a certain candidate solution and probe its neighborhood to find ones with improved quality. This paper proposes a method of probabilistically filtering out bad-looking neighbors based on a simple low-cost preliminary evaluation heuristics. The probabilistic filtering enables us to save time wasted on fully evaluating those solutions that will eventually be trashed, and thus improves the search efficiency by allowing us to spend more time on examining better looking solutions. Experiments with two large-scaled real-world problems, which are a traffic signal control problem in traffic network and a load balancing problem in production scheduling, have shown that the proposed method finds better quality solutions, given the same amount of CPU time.