An improved quantum clone genetic algorithm

Intelligent optimization algorithm based on quantum behavior is the hot spot of intelligent computation. Quantum clone genetic algorithm has many shortcomings such as: low efficiency, poor population diversity, slow convergence speed, easy to trap in local minimums, blindness in global optimal searching direction and so on. An improved quantum clone genetic algorithm (IQCGA) is proposed in this paper. The algorithm have many merits: (1) Adopting the nicking probability division initialization strategy, population diversity is enhanced; (2) Introducing the quantum whole interference crossover, the information is spread in the whole population, so it helps to avoid trapping in local minimums, and accelerates convergence speed; (3) Making use of adaptation strategy in quantum rotate gate updating, searching speed for optimal solution is accelerated; (4) In order to avoid premature and evolution stagnation, the superior individual whole crossover quantum catastrophe strategy is adopted, which helps the population to search the objective solution in different directions. IQCGA is composted of initialization stage and iteration evolution stage. Furthermore, theory analysis is given to prove the convergence. Finally, Simulation results prove that the algorithms can maintain the population diversity, reduce calculation complexity, accelerate searching speed for optimal solution, and avoid trapping in local minimums. So it has the better searching optimal ability and convergence speed.