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End date: 2009Subject: search.filters.subject.simulated annealing

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    A Tabu Search Algorithm for the Training of Neural Networks
    (2009) Dengiz, B.; Alabas-Uslu, C.; Dengiz, O.
    The most widely used training algorithm of neural networks (NNs) is back propagation ( BP), a gradient-based technique that requires significant computational effort. Metaheuristic search techniques such as genetic algorithms, tabu search (TS) and simulated annealing have been recently used to cope with major shortcomings of BP such as the tendency to converge to a local optimal and a slow convergence rate. In this paper, an efficient TS algorithm employing different strategies to provide a balance between intensification and diversification is proposed for the training of NNs. The proposed algorithm is compared with other metaheuristic techniques found in literature using published test problems, and found to outperform them in the majority of the test cases.
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    A Hybrid Simulated Annealing For A Multi-Objective Stochastic Assembly Line Balancing Problem
    (2008) Cakir, Burcin; Dengiz, Berna; Altiparmak, Fulya; Xia, GP; Deng, XQ; 0000-0003-1730-4214; AAF-7020-2021
    Asssembly line balancing is the problem of assigning tasks to the workstations, while optimizing one or more objectives without violating restrictions imposed on the line. In practice, task times may be random due to the worker fatigue, low skill levels, job dissatisfaction, poorly maintained equipment, defects in raw material, etc. When stochastic task times are taken consideration in assembly lines, balancing procedure is more complex due to the probability of incompleteness of stations times in a given cycle time. In this study, a multi-objective simulated annealing algorithm (m_SAA) is proposed for single-model, stochastic assembly line balancing problem with the aim of minimizing of smoothness index and total design cost. To obtain Pareto-optimal solutions, m_SAA implements tabu list and a multinomial probability mass function approach. The effectiveness of the proposed m_SAA is comparatively investigated using another SA using weight-sum approach on the test problems. Computational results show that m_SAA with multinomial probability mass function approach is more effective than SA with weight-sum approach in terms of quality of Pareto-optimal solutions.