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针对动态覆盖问题可以转化为多目标优化问题,提出一种解决多目标优化的连续空间蚁群算法(Continuous Space Ant Colony System,CSACS).该算法通过随机划分过程,对连续解空间划分为多个子空间,分别在不同子空间利用蚁群进行区域内以及区域间搜索Pareto最优解,为了保证最优解的多样性,引入小生境策略进行Pareto最优解适应度更新.实验表明,在不同网络规模和迭代次数下,区域覆盖度和网络寿命相对于传统经典算法有较好改进.字数以250字以上为宜.请不要在摘要中引用参考文献和英文缩略语.
Aiming at the problem that dynamic coverage can be transformed into multi-objective optimization problem, a continuous space Ant Colony System (CSACS) is proposed to solve the multi-objective optimization problem. The algorithm divides the continuous solution space into multiple sub-systems Space, the ant colony is used in different subspaces to search Pareto optimal solution in the region and region respectively. In order to ensure the diversity of the optimal solution, niche strategy is introduced to update the fitness of Pareto optimal solution. The experiment shows that in different networks Scale and number of iterations, the regional coverage and network lifetime are better than the traditional classical algorithms, and the number of words should be more than 250. Please do not quote the references and English abbreviations in the abstract.