面向绿色纺织柔性作业车间调度的混沌协同进化算法OA

To address the green scheduling requirements in flexible production workshops of the textile industry,a Chaotic Synergistic Evolutionary Algorithm(CSEA)integrating discrete particle swarm optimization with sim-ulated annealing mechanisms was developed to optimize production efficiency and equipment energy consump-tion.Firstyl,a multi-objective scheduling model incorporating textile equipment energy consumption was estab-lished using a classical genetic algorithm framework,with innovative implementation of a chaos theory-based dynamic crossover probability adjustment mechanism.Logistic mapping equations were employed to enhance diversity search capability during scheduling processes.Then,discrete particle swarm optimization was embed-ded in population evolution to optimize textile equipment load distribution,complemented by simulated anneal-ing for refined neighborhood search of processes,achieving dual optimization of global exploration and local ex-ploitation.Finally,by using an adaptive early stopping strategy to terminate invalid iterations dynamically,the time cost is significantly reduced.After testing on the Kacem dataset,compared with traditional genetic algo-rithms and standard particle swarm optimization algorithms,the proposed hybrid algorithm has improved con-vergence speed by 37.6%,effectively tackling the textile equipment scheduling and energy consumption control challenges in multi-variety,small-batch production scenarios.