Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms |
| |
Authors: | Zhiyong Li Günter Rudolph Kenli Li |
| |
Affiliation: | aSchool of Computer and Communication, Hunan University, Changsha, 410082, China;bLehrstuhl für Algorithm Engineering, Universität Dortmund, 44221 Dortmund, Germany |
| |
Abstract: | In recent research, we proposed a general framework of quantum-inspired multi-objective evolutionary algorithms (QMOEA) and gave one of its sufficient convergence conditions to the Pareto optimal set. In this paper, two Q-gate operators, H gate and R&N gate, are experimentally validated as two Q-gate paradigms meeting the convergence condition. The former is a modified rotation gate, and the latter is a combination of rotation gate and NOT gate with the specified probability. To investigate their effectiveness and applicability, several experiments on the multi-objective 0/1 knapsack problems are carried out. Compared to two typical evolutionary algorithms and the QMOEA only with rotation gate, the QMOEA with H gate and R&N gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate ε value regions for two Q-gates are verified. |
| |
Keywords: | Multi-objective evolutionary algorithms Multi-objective 0/1 knapsack problems Quantum computing Convergence performance |
本文献已被 ScienceDirect 等数据库收录! |
|