An improved osprey optimization algorithmand its application
To address the poor accuracy and stability for Osprey Optimization Algorithm ( OOA) , this paper proposes some improvement strategies. First, SPM chaotic mapping has been integrated into the stage of population initialization to improve the population diversity. Second, Weibull's long and short distance random disturbances are integrated respectively in the exploration and mining stages to update the position of osprey, effectively improving the convergence accuracy of OOA. Finally, a mutation strategy of"optimum-random mean"is proposed to enhance the ability of algorithm to jump out of the local optimal during the iterative process. The proposed algorithm is called Improved Osprey Optimization Algorithm ( IOOA) . To verify the optimization ability of IOOA, it is compared with other emerging intelligent algorithms for the optimization of 12 benchmark functions. Our results show the success rate of optimization, convergence speed and stability of IOOA are significantly higher than those of other algorithms. In addition, the application of IOOA on the hyperparameter optimization of Hybrid Kernel Relevance Vector Machine is able to accurately predict the multi-objective performance of diesel engines.
osprey optimization algorithmSPM chaotic mappingweibull random disturbanceoptimum-random mean mutation
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