Improved Osprey Optimization Algorithm with Multi-Strategy Fusion - Wenli Lei; Jinping Han; Xinghao Wu (Biomimetics)
The osprey optimization algorithm (OOA) is an effective metaheuristic algorithm. Although the OOA has the characteristics of strong optimality-seeking ability and fast convergence speed, it also has the disadvantages of imbalance between global exploration and local exploitation ability, easily falling into local optima in the later stage, and reduced population diversity and convergence speed. Therefore, this paper proposes an improved osprey optimization algorithm (IOOA) with multi-strategy fusion. First, Fuch chaotic mapping is used to initialize the ospreys’ population and increase the population diversity. Then, an adaptive weighting factor is introduced in the exploration phase of the algorithm to help the algorithm improve the convergence accuracy. The Cauchy variation strategy is integrated in the algorithm’s exploitation stage to enhance the diversity of the ospreys’ population and avoid falling into local optima. Finally, a Warner mechanism for the sparrow search algorithm is introduced to coordinate the algorithm’s local optimization and global search capabilities. The IOOA with various optimization algorithms is tested in a simulation for 10 benchmark test functions and 15 CEC2017 test functions, and non-parametric tests are performed on the IOOA. Experimental results show that the IOOA achieves improved accuracy and stability. The application of the IOOA to the three-bar truss engineering design problem further verifies its superiority in dealing with practical optimization problems.