A reinforced multi-strategy parrot optimization algorithm and its application in practical engineering optimization

To address the issues of low solution accuracy, premature convergence, and susceptibility to local optima in the Parrot Optimization (PO) algorithm, a Reinforced Multi-strategy Parrot Optimization (RMPO) algorithm has been designed. First, cubic mapping is introduced in PO to initialize the solution positions, which helps generate a more uniformly distributed initial population, avoiding the initial solution clustering caused by random initialization, thereby enhancing the algorithm’s global exploration capability. Second, drawing inspiration from the vigilance behavior of sparrow populations, the search strategy is adjusted based on individual fitness to reduce the probability of the algorithm getting stuck in local optima. A nonlinear convergence factor is introduced to balance the algorithm’s exploitation ability. To prevent the convergence factor from converging too quickly in later iterations, an adaptive t -distribution operator and differential mutation are employed to enhance global exploration and local exploitation capabilities. Finally, to systematically evaluate the performance of RMPO, tests were conducted on the CEC2019 and CEC2022 test suites, and comparisons were made with eight other intelligent optimization algorithms. RMPO achieved the optimal solution on 7/10 functions of CEC2019 and 8/12 functions of CEC2022, with the first overall statistical ranking (average ranking 1.50) on CEC2022 and the top ranking on CEC2019 for high-dimensional complex problems. To verify the application capability of RMPO in engineering problems, the algorithm was further applied to solve design problems for pressure vessels, welded beams, and stepped cone pulleys, and it achieved the highest optimization accuracy and stability in all three engineering problems.