【摘要】 针对现行各种改进型遗传算法容易早熟收敛,并且难跳出局部最优的问题,提出一种基于虚拟种群技术的改进型遗传算法。该改进型遗传算法不改变遗传算法中选择、交差、变异等核心算子的参数值,从而有效避免了种群进化过程中因控制遗传算子参数的策略设置不当而引起的算法收敛速度慢的问题。通过虚拟种群与实际种群间的信息交换,隐式地增大了实际种群的多样性。仿真结果表明,在种群规模相同的情况下,虚拟种群遗传算法能以最少的代数跳出局部最优,并在最小的代数收敛于全局最优。更多还原

【Abstract】 An improved Genetic Algorithm(GA) based on the virtual population technology was proposed to avoid the frequent occurrences of premature convergence,and this improved algorithm can efficiently jump out of the local optimum.The parameter value of the key genetic operators,such as the selecting,the crossing and the mutating,in the improved GA were unchanged with the increased generation.This can avoid the problem of the slow convergence,which was often caused by the improper strategy of changing the parameter value of the genetic operators.The diversity of the real population was increased implicitly by the informational exchange between the real population and the virtual one.Finally,the simulation result indicated that,with the same size population,the improved Virtual Population Genetic Algorithm(VPGA) can jump out of the local optimum within the least generations,and also,can convergent to the global optimum at the minimum generation.更多还原