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Memetic Computing
Published in: Memetic Computing 2/2018

09-09-2016 | Regular Research Paper

Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation

Authors: Yanhong Feng, Juan Yang, Congcong Wu, Mei Lu, Xiang-Jun Zhao

Published in: Memetic Computing | Issue 2/2018

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Abstract

Recently, inspired by the migration behavior of monarch butterflies in nature, a metaheuristic optimization algorithm, called monarch butterfly optimization (MBO), was proposed. In the present study, a novel chaotic MBO algorithm (CMBO) is proposed, in which chaos theory is introduced in order to enhance its global optimization ability. Here, 12 one-dimensional classical chaotic maps are used to tune two main migration processes of monarch butterflies. Meanwhile, applying Gaussian mutation operator to some worst individuals can effectively prevent premature convergence of the optimization process. The performance of CMBO is verified and analyzed by three groups of large-scale 0–1 knapsack problems instances. The results show that the introduction of appropriate chaotic map and Gaussian perturbation can significantly improve the solution quality together with the overall performance of the proposed CMBO algorithm. The proposed CMBO can outperform the standard MBO and other eight state-of-the-art canonical algorithms.
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Metadata
Title
Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian mutation
Authors
Yanhong Feng
Juan Yang
Congcong Wu
Mei Lu
Xiang-Jun Zhao
Publication date
09-09-2016
Publisher
Springer Berlin Heidelberg
Published in
Memetic Computing / Issue 2/2018
Print ISSN: 1865-9284
Electronic ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-016-0211-4

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