With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solutio...
详细信息
With the continuous increase of workshop production scale, traditional heuristic algorithms in solving the scheduling problem have the defects of unsatisfactory computing time and insufficient stability of the solution result. However, data mining has a good performance in solving large-scale scheduling problems. A data mining method was proposed for industrial big data to solve the problem of large-scale parallel machines scheduling. This methodology can obtain an effective initial solution for single -operation parallel machine scheduling problem by exploring the effective information in the historical scheduling data. based on historical customer orders, the offline learning was used to continuously generate simulated data for learning, which makes up for the shortcomings of insufficient data. A TLBO framework (teaching -learning -based optimization) hybrid K -means algorithm was redesigned to enhance the accuracy of offline learning and the efficiency of data searching. In the online operation part, according to the optimal solutions for high -similarity manufacturing orders are the approximate solutions, the new customer order will be quickly matched with the most similar manufacturing order through similarity calculation, and then and then local search is performed. Finally, the globally optimal solution is obtained after screening. Experimental results show that the hybrid teaching -learning methodology can solve the large-scale parallel machines scheduling problem with a better learning performance and computational efficiency.
The Hasofer-Lind-Rackwitz-Fiessler (HLRF) algorithm of the first order reliability method may fail in the presence of nonlinear problems. As a simple and efficient meta-heuristic strategy, the teaching-learning-based ...
详细信息
The Hasofer-Lind-Rackwitz-Fiessler (HLRF) algorithm of the first order reliability method may fail in the presence of nonlinear problems. As a simple and efficient meta-heuristic strategy, the teaching-learning-basedoptimization (TLBO) algorithm accompanied with one of its variants, TLBO with triangular varying population sizes, is employed in reliability analysis to overcome the numerical difficulties that occurs in the HLRF algorithm or its modifications of the first order reliability method mainly falling into a state of instability such as periodic solutions and chaos. In the meantime, choice on the penalty coefficient in the equivalent unconstrained optimization problem of reliability analysis is discussed. A more appropriate scheme to adaptively select a sequence of the penalty coefficients in iterations is presented in terms of Karush-Kuhn-Tucker (KKT) conditions to ensure the equivalence of the original constrained optimization problem of the first order reliability method. Numerical experiments show that, compared with the manner of exponential growth might producing great errors in complex and nonlinear problems, the adaptive choice of penalty coefficients in light of KKT conditions results in a good efficiency with a satisfactory accuracy especially when TLBO with triangular varying population sizes is utilized to solve the equivalent optimization problem of reliability analysis.
Blackout has become one of the major serious threats to power system stability, security and reliability. To prevent blackout many correct control actions must be taken. One of these actions is the load shedding. This...
详细信息
ISBN:
(纸本)9781538608791
Blackout has become one of the major serious threats to power system stability, security and reliability. To prevent blackout many correct control actions must be taken. One of these actions is the load shedding. This paper proposes new optimization technique known as teaching-learning-basedoptimization algorithm (TLBO) for solving steady state optimal load shedding problem. Different multi-objectives are considered to prevent the voltage instability which lead to partial or total blackout. load shedding Minimization, voltage stability maximization, and loadability maximization are taken as multi-objective. The developed algorithm is validated and tested on standard IEEE 30-bus test system considering contingency state. The TLBO results are compared with the other reported methods such as;gradient technique based on Kuhn-Tucker theorem (GTBKTT) and Improved harmony search algorithm (IHSA). The obtained results demonstrate the effectiveness of TLBO algorithm solution compared to other algorithms.
Strong localized downbursts generated in thunderstorms can produce surface winds very dangerous for civil structures and infrastructures. Modelling and simulating such severe wind systems is therefore extremely import...
详细信息
Strong localized downbursts generated in thunderstorms can produce surface winds very dangerous for civil structures and infrastructures. Modelling and simulating such severe wind systems is therefore extremely important for structural safety and design wind speed evaluation. This paper deals with the downburst wind field simulation by means of an optimization algorithm that uses a downburst analytical model, previously developed by the authors, and two metaheuristic algorithms, namely the Differential Evolution (DE) and the teachinglearning-basedoptimization (TLBO), for the downburst kinematic and geometrical parameters evaluation. The optimization problem minimizes the relative error between recorded and simulated wind speed and direction time histories. A comparison is made between the performance of two algorithms for ten thunderstorm events measured in north-western Italy between October 2011 and October 2015. Both algorithms provide solutions which are coherent with the downburst parameters values present in literature. TLBO outperforms DE since it has a faster convergence rate to the optimal solution.
暂无评论