Quantum-inspired immune clonal multiobjective optimization algorithm

Authors:
Yangyang Li;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing, Xidian University, Xi'an, China;Institute of Intelligent Information Processing, Xidian University, Xi'an, China
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

Citing 8
Cited 1

Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

Proceedings of the 1st International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization

Evolutionary Computation
Quantum-inspired immune clonal algorithm

ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach

IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle

IEEE Transactions on Evolutionary Computation
A novel genetic algorithm based on immunity

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantum-inspired evolutionary algorithms: a survey and empirical study

Journal of Heuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the concept and principles of quantum computing, a quantum-inspired immune clonal multiobjective optimization algorithm (QICMOA) is proposed to solve extended 0/1 knapsack problems. In QICMOA, we select less-crowded Pareto-optimal individuals to perform cloning, recombination update. Meanwhile, the Pareto-optimal individual is proliferated and divided into a set of subpopulation groups. Individual in a subpopulation group is represented by multi-state gene quantum bits. For the novel representation, qubit individuals in subpopulation are updated by applying a new chaos update strategy. The proposed recombination realizes the information communication among individuals so as to improve the search efficiency. We compare QICMOA with SPEA, NSGA, VEGA and NPGA in solving nine 0/1 knapsack problems. The statistical results show that QICMOA has a good performance in converging to true Pareto-optimal fronts with a good distribution.