Consumption of traditional fossil energy has promoted rapid economic development and caused effects such as climate warming and environmental degradation. In order to solve the problem of environmental economic dispatch (EED), this paper proposes a DE-CQPSO (Differential Evolution-Crossover Quantum Particle Swarm Optimization) algorithm based on the fast convergence of differential evolution algorithms and the particle diversity of crossover operators of genetic algorithms. In order to obtain better optimization results, a parameter adaptive control method is used to update the crossover probability. And the problem of multi-objective optimization is solved by introducing a penalty factor. The experimental results show that: the evaluation index and convergence speed of the DE-CQPSO algorithm are better than QPSO (Quantum Particle Swarm Optimization) and other algorithms, whether it is single-objective optimization of fuel cost and emissions or multi-objective optimization considering both optimization objectives. A good compromise value is verified, which verifies the effectiveness and robustness of the DE-CQPSO algorithm in solving environmental economic dispatch problems. The study provides a new research direction for solving environmental economic dispatch problems. At the same time, it provides a reference for the reasonable output of the unit to a certain extent.

传统化石能源的消费促进了经济的快速发展，并造成了气候变暖和环境退化等影响。为了解决环境经济调度问题，基于差分进化算法的快速收敛和遗传交叉算子的粒子多样性，提出了一种DE-CQPSO算法（差分进化-交叉量子粒子群优化）。算法。为了获得更好的优化结果，使用参数自适应控制方法来更新交叉概率。通过引入惩罚因子解决了多目标优化问题。实验结果表明：无论是燃料成本和排放的单目标优化，还是兼顾两个优化目标的多目标优化，DE-CQPSO算法的评估指标和收敛速度均优于QPSO（量子粒子群优化）算法。验证了一个很好的折衷值，证明了DE-CQPSO算法在解决环境经济调度问题上的有效性和鲁棒性。该研究为解决环境经济调度问题提供了新的研究方向。同时，在一定程度上为机组的合理输出提供了参考。验证了一个很好的折衷值，证明了DE-CQPSO算法在解决环境经济调度问题上的有效性和鲁棒性。该研究为解决环境经济调度问题提供了新的研究方向。同时，在一定程度上为机组的合理输出提供了参考。验证了一个很好的折衷值，证明了DE-CQPSO算法在解决环境经济调度问题上的有效性和鲁棒性。该研究为解决环境经济调度问题提供了新的研究方向。同时，在一定程度上为机组的合理输出提供了参考。