Quantum-inspired Multiobjective Evolutionary Algorithm for Multiobjective 0/1 Knapsack Problems

This paper proposes a multiobjective evolutionary algorithm (MOEA) inspired by quantum computing, which is named quantum-inspired multiobjective evolutionary algorithm (QMEA). In the previous papers, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algo...

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Veröffentlicht in:2006 IEEE International Conference on Evolutionary Computation S. 2601 - 2606
Hauptverfasser: Yehoon Kim, Jong-Hwan Kim, Kuk-Hyun Han
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2006
Schlagworte:
Evolutionary computation
Genetic algorithms
Interference
Merging
Quantum computing
Quantum mechanics
Research and development
Sorting
Stochastic processes
Uncertainty
ISBN:9780780394872, 0780394879
ISSN:1089-778X
Online-Zugang:Volltext
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Zusammenfassung:This paper proposes a multiobjective evolutionary algorithm (MOEA) inspired by quantum computing, which is named quantum-inspired multiobjective evolutionary algorithm (QMEA). In the previous papers, quantum-inspired evolutionary algorithm (QEA) was proved to be better than conventional genetic algorithms for single-objective optimization problems. To improve the quality of the nondominated set as well as the diversity of population in multiobjective problems, QMEA is proposed by employing the concept and principles of quantum computing such as uncertainty, superposition, and interference. Experimental results pertaining to the multiobjective 0/1 knapsack problem show that QMEA finds solutions close to the Pareto-optimal front while maintaining a better spread of nondominated set.
ISBN:9780780394872
0780394879
ISSN:1089-778X
DOI:10.1109/CEC.2006.1688633