Quantum-inspired Multiobjective Evolutionary Algorithm for Multiobjective 0/1 Knapsack Problems
| Autor: | Ye-Hoon Kim, Jong-Hwan Kim, Kuk-Hyun Han |
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| Rok vydání: | 2006 |
| Předmět: |
Mathematical optimization
education.field_of_study Optimization problem Theoretical computer science Computer Science::Neural and Evolutionary Computation Population Continuous knapsack problem MathematicsofComputing_NUMERICALANALYSIS Mathematics::Optimization and Control Evolutionary algorithm Evolutionary computation Knapsack problem education Evolutionary programming Quantum computer Mathematics |
| Zdroj: | IEEE Congress on Evolutionary Computation |
| DOI: | 10.1109/cec.2006.1688633 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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