多策略协同的足球队训练优化算法及其工程应用OA

To tackle the issues of imbalance between global exploration and local exploitation capabil-ities,as well as the susceptibility to getting trapped in local optima in the football team training algo-rithm(FTTA),a multi-strategy football team training algorithm(MSFTTA)is proposed.Firstly,an adaptive fitness-distance balance(AFDB)strategy is introduced,which guides the training process through the scores of candidate solutions to achieve a balance between exploration and exploitation.Sec-ondly,a non-exclusive integration search of superior and inferior solutions is conducted during the indi-vidual additional training phase to promote independent exploration by individuals and enhance the con-vergence accuracy of the algorithm in its later stages.Finally,inspired by the marine predators algo-rithm,the fish aggregating devices(FADs)disturbance strategy is incorporated to simulate unexpected situations during matches,adding randomness and improving the algorithm's ability to escape local ex-trema.Experimental simulations using CEC2017 test function demonstrate that the proposed algorithm exhibits significant improvements in both optimization performance and search accuracy.Additionally,optimization analyses conducted on two engineering case studies further validate the practicality and ef-fectiveness of the improved algorithm.