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

Solving 0–1 knapsack problems by chaotic monarch butterfly optimization algorithm with Gaussian... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Memetic Computing Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Mathematical and Computational Engineering; Artificial Intelligence (incl. Robotics); Complex Systems; Control, Robotics, Mechatronics; Bioinformatics; Applications of Mathematics
ISSN
1865-9284
eISSN
1865-9292
D.O.I.
10.1007/s12293-016-0211-4
Publisher site
See Article on Publisher Site

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.

Journal

Memetic ComputingSpringer Journals

Published: Sep 9, 2016

References

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