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蝙蝠算法是一种模拟蝙蝠回声定位行为的新型群智能优化算法,对多维函数,个体在全局最佳蝙蝠的引导下修改所有的维,这种候选解生成方式可能导致种群多样性下降过快和算法局部求精能力不足.针对这些不足,提出一种改进的蝙蝠算法,使用随机蝙蝠来引导个体飞行和局部搜索,以提高种群多样性,使用修改部分维的策略来加强算法的局部求精能力.在典型测试函数上对新算法进行了仿真,结果表明改进的蝙蝠算法能够有效提高算法的收敛速度并改善解的质量,与其它改进蝙蝠算法和改进群智能算法的比较表明,改进算法在求解多维函数优化问题上是具有竞争力的.
The bat algorithm is a new swarm intelligence optimization algorithm that simulates bat echolocation behavior. It modifies all dimensions of multidimensional functions and individuals under the guidance of the global best bat. This method of generating candidate solutions may lead to the rapid decline of population diversity and In order to solve these problems, an improved bat algorithm is proposed, which uses random bats to guide individual flight and local search to improve the population diversity. The strategy of modifying partial dimensions is used to enhance the local refinement ability of the algorithm The simulation results show that the improved bat algorithm can effectively improve the convergence speed and improve the quality of the solution.Compared with other improved bat algorithm and improved swarm intelligence algorithm, Multidimensional function optimization is competitive.