Efficient hybrid algorithms to solve mixed discrete-continuous optimization problems

Dianzi Liu (Faculty of Science, University of East Anglia, Norwich, UK and College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China)
Chengyang Liu (Faculty of Science, University of East Anglia, Norwich, UK)
Chuanwei Zhang (College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China)
Chao Xu (College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China)
Ziliang Du (School of Aeronautic Science and Engineering, Beihang University, Beijing, China)
Zhiqiang Wan (School of Aeronautic Science and Engineering, Beihang University, Beijing, China)

Engineering Computations

ISSN: 0264-4401

Publication date: 16 April 2018

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

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", Engineering Computations, Vol. 35 No. 2, pp. 979-1002. https://doi.org/10.1108/EC-03-2017-0103

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Publisher

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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