Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms

Authors:
Zhiyong Li;Günter Rudolph;Kenli Li
Affiliations:
School of Computer and Communication, Hunan University, Changsha, 410082, China and Lehrstuhl für Algorithm Engineering, Universität Dortmund, 44221 Dortmund, Germany;Lehrstuhl für Algorithm Engineering, Universität Dortmund, 44221 Dortmund, Germany;School of Computer and Communication, Hunan University, Changsha, 410082, China
Venue:
Computers & Mathematics with Applications
Year:
2009

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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"@e gate and R&N"@e 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"@e gate and R&N"@e gate have more powerful convergence ability in high complex instances. Moreover, the QMOEA with R&N"@e gate has the best convergence in almost all of the experimental problems. Furthermore, the appropriate @e value regions for two Q-gates are verified.