Please use this identifier to cite or link to this item: https://doi.org/10.1109/TCYB.2013.2264670
Title: Memetic algorithm for real-time combinatorial stochastic simulation optimization problems with performance analysis
Authors: Horng, S.-C.
Lin, S.-Y.
Lee, L.H. 
Chen, C.-H.
Keywords: Artificial neural network
Assemble to order (ATO)
Combinatorial optimisation
Evolution algorithm
Memetic algorithm (MA)
Optimal computing budget allocation (OCBA)
Stochastic simulation
Surrogate model
Issue Date: Oct-2013
Source: Horng, S.-C., Lin, S.-Y., Lee, L.H., Chen, C.-H. (2013-10). Memetic algorithm for real-time combinatorial stochastic simulation optimization problems with performance analysis. IEEE Transactions on Cybernetics 43 (5) : 1495-1509. ScholarBank@NUS Repository. https://doi.org/10.1109/TCYB.2013.2264670
Abstract: A three-phase memetic algorithm (MA) is proposed to find a suboptimal solution for real-time combinatorial stochastic simulation optimization (CSSO) problems with large discrete solution space. In phase 1, a genetic algorithm assisted by an offline global surrogate model is applied to find N good diversified solutions. In phase 2, a probabilistic local search method integrated with an online surrogate model is used to search for the approximate corresponding local optimum of each of the N solutions resulted from phase 1. In phase 3, the optimal computing budget allocation technique is employed to simulate and identify the best solution among the N local optima from phase 2. The proposed MA is applied to an assemble-to-order problem, which is a real-world CSSO problem. Extensive simulations were performed to demonstrate its superior performance, and results showed that the obtained solution is within 1% of the true optimum with a probability of 99%. We also provide a rigorous analysis to evaluate the performance of the proposed MA. © 2013 IEEE.
Source Title: IEEE Transactions on Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/63181
ISSN: 21682267
DOI: 10.1109/TCYB.2013.2264670
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