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通过改进遗传算法 ,提出一种求解全局优化问题的变异基随机搜索方法 .该法以变异算子作为唯一的遗传算子 ,利用生物变异原理进行局部搜索 ,同时为使算法具有一定的全局搜索性能引入随机初始化技术 .它具有较强的局部搜索能力 ,可在有限时间内取得较好解 .仿真实验证明 ,本算法在求解全局优化问题上的有效性 ,并表明其局部收敛能力与求解结果均优于传统遗传算法 .
By improving the genetic algorithm, this paper proposes a mutation random search method to solve the global optimization problem, which takes the mutation operator as the only genetic operator and uses the principle of biological mutation to search locally. In order to make the algorithm have a certain global search performance The introduction of random initialization technology.It has strong local search ability, can get better solution in a limited time.Simulation experiments show that the algorithm in solving the global optimization problem is valid, and shows that the local convergence ability and the results of the solution are Better than traditional genetic algorithm.