Quantum-Inspired Differential Evolution with Grey Wolf Optimizer for 0-1 Knapsack Problem release_acrge4upjzg7podp7ot2rgxs5u

by Yule Wang, Wanliang Wang

Published in Mathematics by MDPI AG.

2021   Issue 11, p1233

Abstract

The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO) to enhance the diversity and convergence performance and improve the performance in high-dimensional cases for 0-1 knapsack problems. The proposed algorithm adopts quantum computing principles such as quantum superposition states and quantum gates. It also uses adaptive mutation operations of differential evolution, crossover operations of differential evolution, and quantum observation to generate new solutions as trial individuals. Selection operations are used to determine the better solutions between the stored individuals and the trial individuals created by mutation and crossover operations. In the event that the trial individuals are worse than the current individuals, the adaptive grey wolf optimizer and quantum rotation gate are used to preserve the diversity of the population as well as speed up the search for the global optimal solution. The experimental results for 0-1 knapsack problems confirm the advantages of QDGWO with the effectiveness and global search capability for knapsack problems, especially for high-dimensional situations.
In application/xml+jats format

Archived Files and Locations

application/pdf  2.5 MB
file_ggwcqbwlije63p6tekhz77tvna
res.mdpi.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-05-28
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2227-7390
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 395d87b9-22a3-42cd-8753-76cc40a6fb65
API URL: JSON