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为了高效求解动态连续优化问题,提出一种分层粒子群优化算法.该算法将动态函数定义域分成个子空间,每个空间用一个粒子群作为第一层进行独立搜索,个子空间的最优粒子再组成一个全局粒子群进行全局搜索,以达到全局牵引的作用,同时提出探测环境和响应环境的策略.利用经典的动态函数对算法进行测试,结果表明所提出算法能够迅速适应环境变化和跟踪最优解的变化,效果令人满意.
In order to efficiently solve the dynamic continuous optimization problem, a hierarchical particle swarm optimization algorithm is proposed, which divides the domain of dynamic function into several subspaces, each of which uses a particle swarm as the first layer to search independently. The optimal particle And then form a global particle swarm for global search, in order to achieve global traction, at the same time put forward the strategy of detecting environment and responding to the environment.Using classical dynamic function to test the algorithm, the results show that the proposed algorithm can quickly adapt to environmental changes and tracking The optimal solution changes, the effect is satisfactory.