An improved osprey optimization algorithm fusing Latin hypercube and Lévy flight

An improved Osprey Optimization Algorithm (IOOA) fusing Latin hypercube and Lévy flight is proposed to solve the problem that the original OOA in which the initialized population diversity is low and the iterative search process is unable to jump out of local optima. First, a Latin hypercube initialization strategy is used to generate diverse population individuals. Second, a Lévy flight strategy is used to generate random steps and directions, which enables the population to jump out of the local optimum by random steps in multiple directions during the iteration process. The experimental results are numerically analyzed through simulations on four types of benchmark functions, The IOOA algorithm shows significant improvement in convergence speed and accuracy.