Experiments with new stochastic global optimization search techniques

Özdamar, Linet; Demirhan, Melek

doi:10.1016/s0305-0548(99)00054-4

Cited by 41 publications

(18 citation statements)

References 40 publications

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“…In the 1980s, the SA algorithm had a major impact on the field of heuristic search for its simplicity and efficiency in solving combinatorial optimization problems. Moreover, the SA algorithm has been extended to deal with continuous optimization problems [7][8][9].…”

Section: Simulated Annealing-based Optimization Procedurementioning

confidence: 99%

Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method

Kibria

Castillo-Villar

Millwater

2015

Mathematical Problems in Engineering

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The reliability modeling of a module in a turbine engine requires knowledge of its failure rate, which can be estimated by identifying statistical distributions describing the percentage of failure per component within the turbine module. The correct definition of the failure statistical behavior per component is highly dependent on the engineer skills and may present significant discrepancies with respect to the historical data. There is no formal methodology to approach this problem and a large number of labor hours are spent trying to reduce the discrepancy by manually adjusting the distribution's parameters. This paper addresses this problem and provides a simulation-based optimization method for the minimization of the discrepancy between the simulated and the historical percentage of failures for turbine engine components. The proposed methodology optimizes the parameter values of the component's failure statistical distributions within the component's likelihood confidence bounds. A complete testing of the proposed method is performed on a turbine engine case study. The method can be considered as a decision-making tool for maintenance, repair, and overhaul companies and will potentially reduce the cost of labor associated to finding the appropriate value of the distribution parameters for each component/failure mode in the model and increase the accuracy in the prediction of the mean time to failures (MTTF).

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Section: Simulated Annealing-based Optimization Procedurementioning

confidence: 99%

Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method

Kibria

Castillo-Villar

Millwater

2015

Mathematical Problems in Engineering

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“…In the 80's, SA had a major impact on the field of heuristic search for its simplicity and efficiency for solving combinatorial optimization problems. Then, it has been extended to deal with continuous optimization problems (Dekkers and Aarts, 1991;Ozdamar and Demirhan, 2000;Locatelli, 2000). SA is a stochastic algorithm which enables under some conditions the degradation of a solution.…”

Section: Simulated Annealingmentioning

confidence: 99%

ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

et al. 2013

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Abstract:This document presents a general-purpose software framework dedicated to the design, the analysis and the implementation of local search algorithms: ParadisEO-MO. A substantial number of single-solution based local search metaheuristics has been proposed so far, and an attempt of unifying existing approaches is here presented. Based on a fine-grained decomposition, a conceptual model is proposed and is validated by regarding a number of state-of-the-art methodologies as simple variants of the same structure. This model is then incorporated into the ParadisEO-MO software framework. This framework has proven its efficiency and high flexibility by enabling the resolution of many academic and real-world optimization problems from science and industry.

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“…However, simulated annealing algorithms for continuous variables have been proposed by fewer authors than for discrete variables [15]. Some more recent gradient-free optimization methods for continuous variables involving simulated annealing are presented, for example, in [15][16][17][18][19][20][21][22]41]. Some of these methods are hybrids, where simulated annealing is combined with a local search method.…”

Section: Introductionmentioning

confidence: 99%

“…On the second level, another parallel synchronous hybrid is used, where the first-level hybrid is combined with simulated annealing. An excellent study of 13 different variants of gradient-free simulated annealing is presented in [19], where, among others, simulated annealing is hybridized with a local search and clustering and partitioning techniques. Some of these methods are parallel synchronous and some are parallel asynchronous hybrids.…”

Section: Introductionmentioning

confidence: 99%

Efficient hybrid methods for global continuous optimization based on simulated annealing

Miettinen¹,

Mäkelä

Maaranen

2006

Computers & Operations Research

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Experiments with new stochastic global optimization search techniques

Cited by 41 publications

References 40 publications

Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method

Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method

ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms

Efficient hybrid methods for global continuous optimization based on simulated annealing

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