A binary multi-verse optimizer for 0-1 multidimensional knapsack problems with application in interactive multimedia systems

View/ Open
Embargoed until: 2022-04-16
Author(s)
Abdel-Basset, Mohamed
El-Shahat, Doaa
Faris, Hossam
Mirjalili, Seyedali
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
This work proposes a new Modified Multi-Verse Optimization (MMVO) algorithm for solving the 0-1 knapsack (0-1 KP) and multidimensional knapsack problems (MKP). MMVO incorporates a two-step repair strategy for handling constraints. In addition, a barrier function is employed for assigning negative values to the infeasible solutions so that their fitness cannot outperform the fitness of the feasible ones. MMVO avoids local optima by re-initializing the population every predetermined number of iterations while keeping the best solution obtained so far. For discretizing the solutions, MMVO employs a V-shaped transfer function ...
View more >This work proposes a new Modified Multi-Verse Optimization (MMVO) algorithm for solving the 0-1 knapsack (0-1 KP) and multidimensional knapsack problems (MKP). MMVO incorporates a two-step repair strategy for handling constraints. In addition, a barrier function is employed for assigning negative values to the infeasible solutions so that their fitness cannot outperform the fitness of the feasible ones. MMVO avoids local optima by re-initializing the population every predetermined number of iterations while keeping the best solution obtained so far. For discretizing the solutions, MMVO employs a V-shaped transfer function (tanh). The research applies the proposed method to several knapsack case studies and demonstrates its application in resource allocation of Adaptive Multimedia Systems (AMS). The results show the benefits of the MMVO algorithm in solving binary test and real-world problems.
View less >
View more >This work proposes a new Modified Multi-Verse Optimization (MMVO) algorithm for solving the 0-1 knapsack (0-1 KP) and multidimensional knapsack problems (MKP). MMVO incorporates a two-step repair strategy for handling constraints. In addition, a barrier function is employed for assigning negative values to the infeasible solutions so that their fitness cannot outperform the fitness of the feasible ones. MMVO avoids local optima by re-initializing the population every predetermined number of iterations while keeping the best solution obtained so far. For discretizing the solutions, MMVO employs a V-shaped transfer function (tanh). The research applies the proposed method to several knapsack case studies and demonstrates its application in resource allocation of Adaptive Multimedia Systems (AMS). The results show the benefits of the MMVO algorithm in solving binary test and real-world problems.
View less >
Journal Title
Computers & Industrial Engineering
Volume
132
Copyright Statement
© 2019 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
Subject
Mathematical Sciences
Information and Computing Sciences
Engineering
Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Engineering, Industrial
Computer Science