Abstract:

The L_OOA(An Improved Osprey Optimization Algorithm) is proposed to address the issues of the original OOA (Osprey Optimization Algorithm), which is prone to local optima and slow optimization speed. Firstly, to maintain population diversity, the Tent chaotic mapping strategy is adopted to initialize the individual positions of the population. Secondly, by introducing the Levy strategy to update the position of the Osprey, the Osprey Optimization Algorithm can improve its ability to jump out of local optima. The spiral curve strategy is introduced into the Osprey optimization algorithm to improve its computational accuracy. Finally, comparative

experiments are conducted with other intelligent algorithms on the CEC2021 ( Computational Experimental Competition 2021)testfunction set. Experiments prove that L_OOA has better accuracy and faster speed.

Key words: osprey optimization algorithm, spiral curve strategy, Levy strategy, Tent chaotic mapping