【摘要】 针对蚁狮优化算法(ALO)在求解工程优化问题时易陷入局部最优及收敛速度慢等缺陷，提出一种基于Levy飞行和差分进化的改进蚁狮优化算法(LDALO)。改进算法对ALO中的蚂蚁进行差分进化操作，从而改善种群多样性，避免算法陷入局部最优并提高算法全局搜索能力。精英引导的Levy飞行被用于蚂蚁位置更新，以加快算法收敛速度。改进算法还在蚁狮捕食蚂蚁后对蚁狮进行差分变异，以提高算法的寻优精度。仿真实验基于10个基准函数进行，其结果显示LDALO较其他对比算法收敛速度更快，寻优精度更高。在无线传感器网络覆盖优化、压力容器设计、拉压弹簧设计等工程优化问题中的应用，验证了LDALO的适用性和有效性。更多还原

【Abstract】 An improved ant lion optimizer using Levy flight and differential evolution(LDALO)is proposed to overcome the disadvantages of ALO such as easily falling into local optima and slow convergence. In LDALO,differential evolution is used to mutate the positions of the ants, for the purpose of diversifying ant population, avoiding the algorithm falling into local optima and enhancing the algorithm’s global search ability. To accelerate the convergence speed of LDALO,the ants’ positions are updated by using Levy flight guided by the elite. The position of an ant lion is also mutated by differential evolution after the ant lion’s prey on an ant, to improve IDALO’s optimization accuracy. Ten benchmark functions are used in the simulation, and the results show that LDALO has faster convergence speed and higher optimization accuracy than other comparison algorithms. The applicability and effectiveness of LDALO are verified through applyingin the optimization problems such as coverage optimization in wireless sensor networks, pressure vessel design, and tension/compression spring design.更多还原