【摘要】 文中提出了一种基于遗传算法和微粒群算法的混合算法,该算法兼有遗传算法和微粒群算法的优点。混合算法以微粒群算法为主体,同时应用遗传算子操作来优化参数搜索,并引进了摒弃因子来调整微粒的随机性,最终得到最优值。本算法中交叉和变异算子采用了概率自适应策略,微粒群算法使用了动态惯性因子来控制微粒的速度更新。通过对标准试验函数的测试,与标准遗传算法及微粒群算法的结果比较,证明了该混合算法的有效性,并应用于图像增强处理,获得了较为满意的结果。更多还原

【Abstract】 Propose a novel hybrid algorithm,which based the genetic algorithm and particle swarm optimization.This algorithm combines the strengths of particle swarm optimization with genetic algorithms.It takes the particle swarm optimization as the main operator,at the same time,applies of genetic operators to optimize the search parameters and eventually gets the optimal value.In this algorithm,the crossover and mutation operator use the strategy of adaptive probability,particle swarm optimization uses dynamic inertia factor to control the speed of particles update.Compare this hybrid algorithm to both the standard genetic algorithm and particle swarm optimization in the standard test functions,the result shows this hybrid algorithm to be highly effectiveness, often outperforming the both genetic algorithm and particle swarm optimization. So apply it to the image enhancement,and get the more satisfied results.更多还原