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When quantum-inspired genetic algorithm (QGA) is used to solve continuous function optimization problems, there are several shortcomings, such as non-determinability of lookup table of updating quantum gates, requiring prior knowledge of the best solution and premature phenomenon. So novel quantum genetic algorithm (NQGA) is proposed in this paper to solve continuous function optimization problems. The core of NQGA is that a new evolutionary strategy including qubit phase comparison approach to update quantum gates, adaptive search grid and catastrophe-mutation method is introduced. NQGA has good capability of balancing exploration and exploitation and has some excellent characteristics of both good global search capability and good local search capability, rapid convergence. And the convergence of NQGA is also analyzed in this paper. The results from the tests of several typically complex functions and experimental results of digital filter design demonstrate that NQGA is superior to several conventional genetic algorithms (CGAs) greatly in optimization quality and efficiency.
Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE (Volume:2 )
Date of Conference: 12-15 Oct. 2003