To address the limitations of the traditional parrot optimization algorithm(POA),such as susceptibility to local optima and slow convergence, a novel POA based on hybrid reverse learning and crisscross strategy(POABHR...To address the limitations of the traditional parrot optimization algorithm(POA),such as susceptibility to local optima and slow convergence, a novel POA based on hybrid reverse learning and crisscross strategy(POABHRLCS) was proposed in this paper. The key innovations of POABHRLCS include the following. First,Kent chaotic mapping is used for population initialization,enhancing the diversity of the initial population. Second,a hybrid reverse learning strategy combining lens imaging reverse learning and stochastic reverse learning is introduced to improve the algorithm's ability to escape local optima. Third,adaptive factors,including dynamic inertia weights and switching factors,are introduced to balance global exploration and local exploitation. Finally,a crisscross strategy employing horizontal and vertical crossover operations is used to maintain population diversity and prevent premature convergence. Extensive experiments on 23 benchmark functions demonstrate that POABHRLCS achieves faster convergence and higher solution accuracy compared to state-of-the-art metaheuristic algorithms.Furthermore,the algorithm outperforms other comparative algorithms in solving engineering constraint problems,such as the multi-disc clutch brake design and the three-bar truss volume optimization. These results confirm the practicality and effectiveness of POABHRLCS in both theoretical and real-world applications.展开更多
基金supported by the National Key Research and Development Program of China(2022ZD0119000)
文摘To address the limitations of the traditional parrot optimization algorithm(POA),such as susceptibility to local optima and slow convergence, a novel POA based on hybrid reverse learning and crisscross strategy(POABHRLCS) was proposed in this paper. The key innovations of POABHRLCS include the following. First,Kent chaotic mapping is used for population initialization,enhancing the diversity of the initial population. Second,a hybrid reverse learning strategy combining lens imaging reverse learning and stochastic reverse learning is introduced to improve the algorithm's ability to escape local optima. Third,adaptive factors,including dynamic inertia weights and switching factors,are introduced to balance global exploration and local exploitation. Finally,a crisscross strategy employing horizontal and vertical crossover operations is used to maintain population diversity and prevent premature convergence. Extensive experiments on 23 benchmark functions demonstrate that POABHRLCS achieves faster convergence and higher solution accuracy compared to state-of-the-art metaheuristic algorithms.Furthermore,the algorithm outperforms other comparative algorithms in solving engineering constraint problems,such as the multi-disc clutch brake design and the three-bar truss volume optimization. These results confirm the practicality and effectiveness of POABHRLCS in both theoretical and real-world applications.
