Paper Info
Title
Quantum Multi-objective Evolutionary Algorithm with Particle Swarm Optimization Method
Abstract
This paper proposes a novel algorithm for Multiobjective Optimization Problems based on Quantum Particle Swarm. To improve performance of original particle swarm optimization algorithm and avoid trapping to local excellent situations, this method constructs the new quantum solutions expression for multi-objective optimization particle swarm. It adopts the non-dominated sorting method for solutions population and use a new population diversity preserving strategy which is based on the Pareto max-min distance. The multi dimensional 0-1 knapsack optimization problems are carried out and the results show that the proposed method can efficiently find Pareto optimal solutions that are closer to Pareto font and better on distribution. Especially, this proposed method is outstanding on more complex high-dimensional optimization problems.
Year
DOI
Venue
2008
10.1109/ICNC.2008.785
ICNC
Keywords
Field
DocType
quantum multi-objective evolutionary algorithm,pareto optimal solution,solutions population,original particle swarm optimization,multi-objective optimization particle swarm,particle swarm optimization method,pareto max-min distance,new quantum solutions expression,knapsack optimization problem,pareto font,complex high-dimensional optimization problem,pareto analysis,particle swarm optimization,convergence,evolutionary computation,multiobjective optimization,optimization,multi objective optimization,particle swarm,sorting,optimization problem,algorithm design and analysis
Particle swarm optimization,Derivative-free optimization,Mathematical optimization,Evolutionary algorithm,Vector optimization,Computer science,Meta-optimization,Multi-swarm optimization,Artificial intelligence,Imperialist competitive algorithm,Machine learning,Metaheuristic
Conference
Citations 
PageRank 
References 
3
0.45
3
Authors
4
Name
Order
Citations
PageRank
Zhiyong Li1285.91
Kun Xu231.12
Songbing Liu350.90
Kenli Li41389124.28