Novel binary biogeography-based optimization algorithm for the knapsack problem

Authors:
BiBingyan Zhao;Changshou Deng;Yanling Yang;Hu Peng
Affiliations:
School of Business, Jiujiang University, Jiujiang, China;School of Information Science and Technology, Jiujiang University, Jiujiang, China;School of Information Science and Technology, Jiujiang University, Jiujiang, China;School of Information Science and Technology, Jiujiang University, Jiujiang, China
Venue:
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Year:
2012

Citing 7
Cited 0

Load balancing methods and parallel dynamic programming algorithm using dominance technique applied to the 0--1 knapsack problem

Journal of Parallel and Distributed Computing
MOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem

Computers and Operations Research
A new ant colony optimization algorithm for the multidimensional Knapsack problem

Computers and Operations Research
Population distributions in biogeography-based optimization algorithms with elitism

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A hybrid simulated annealing metaheuristic algorithm for the two-dimensional knapsack packing problem

Computers and Operations Research
Biogeography-Based Optimization

IEEE Transactions on Evolutionary Computation
Markov Models for Biogeography-Based Optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mathematical models of biogeography inspired the development of the biogeography-based optimization algorithm. In this article we propose a binary version of biogeography-based optimization (BBO) for the Knapsack Problem. Two new mutation operators are proposed to extend the biogeography-based optimization algorithm to binary optimization problems. We also demonstrate the performance of the resulting new binary Biogeography-based optimization algorithm in solving four Knapsack problems and compare it with that of the standard Genetic Algorithm. The simulation results show that our new method is effective and efficient for the Knapsack problem.