Quantum and biogeography based optimization for a class of combinatorial optimization

Authors:
Lixiang Tan;Li Guo
Affiliations:
University of Science and Technology of China, Hefei, China;University of Science and Technology of China, Hefei, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 5
Cited 2

Genetic Algorithm Based-On the Quantum Probability Representation

IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithms with a new termination criterion, Hε gate, and two-phase scheme

IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization

IEEE Transactions on Evolutionary Computation
Quantum-Inspired Immune Clonal Algorithm for Global Optimization

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

Biogeography migration algorithm for traveling salesman problem

ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Research of Biogeography-Based Multi-Objective Evolutionary Algorithm

Journal of Information Technology Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, an algorithm named Quantum and Biogeography based Optimization(QBO) is proposed to investigate the possibility of optimization by evolving multiple Quantum Probability Models(QPMs) via evolutionary strategies inspired by the mathematics of biogeography. In QBO, each QPM modeling an area in decision space represents a habitat, the whole population of QPMs evolve as an ecosystem with multiple habitats interacting. The migration and immigration mechanisms originally presented in Biogeography Based Optimization (BBO) [1] is introduced into QBO to implement the efficient information sharing among QPMs, which enhance the evolution of probability models towards the better status that can generate more better solutions. Experimental results on classical 0/1 knapsack problems of various scale show that the mechanisms in BBO are feasible to evolve multiple QPMs, and QBO is efficient for hard optimization problem.