Efficient hybrid algorithms to solve mixed discrete-continuous optimization problems: A comparative study
Abstract
Purpose
In real-world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear optimization problems, the use of finite element methods is very time-consuming. The purpose of this study is to investigate the efficiency of the proposed hybrid algorithms for the mixed discrete-continuous optimization and compare it with the performance of genetic algorithms (GAs).
Design/methodology/approach
In this paper, the enhanced multipoint approximation method (MAM) is used to reduce the original nonlinear optimization problem to a sequence of approximations. Then, the sequential quadratic programing technique is applied to find the continuous solution. Following that, the implementation of discrete capability into the MAM is developed to solve the mixed discrete-continuous optimization problems.
Findings
The efficiency and rate of convergence of the developed hybrid algorithms outperforming GA are examined by six detailed case studies in the ten-bar planar truss problem, and the superiority of the Hooke–Jeeves assisted MAM algorithm over the other two hybrid algorithms and GAs is concluded.
Originality/value
The authors propose three efficient hybrid algorithms, the rounding-off, the coordinate search and the Hooke–Jeeves search-assisted MAMs, to solve nonlinear mixed discrete-continuous optimization problems. Implementations include the development of new procedures for sampling discrete points, the modification of the trust region adaptation strategy and strategies for solving mix optimization problems. To improve the efficiency and effectiveness of metamodel construction, regressors f defined in this paper can have the form in common with the empirical formulation of the problems in many engineering subjects.
Keywords
Acknowledgements
The author is thankful to Professor Vassili Toropov from Queen Mary University of London for providing the necessary discussion for the preparation of the paper and the support from Natural Science Project of Education Office of Shaanxi Province, China (No.15JK1484).
Citation
Liu, D., Liu, C., Zhang, C., Xu, C., Du, Z. and Wan, Z. (2018), "Efficient hybrid algorithms to solve mixed discrete-continuous optimization problems: A comparative study", Engineering Computations, Vol. 35 No. 2, pp. 979-1002. https://doi.org/10.1108/EC-03-2017-0103
Publisher
:Emerald Publishing Limited
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