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An improved quantum particle swarm optimization algorithm for environmental economic dispatch

机译：一种改进的环境经济派遣量子粒子群优化算法

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摘要

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. (C) 2020 Elsevier Ltd. All rights reserved.
机译：传统化石能源的消费促进了经济快速发展，造成了气候变暖和环境退化等效果。为了解决环境经济调度（EED）的问题，本文提出了一种基于差分演进算法快速收敛的DE-CQPSO（差分演化交叉量子粒子群算法，以及遗传学的交叉算子的粒子多样性算法。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。通过引入惩罚因素来解决多目标优化问题。实验结果表明：DE-CQPSO算法的评价指标和收敛速度优于QPSO（量子粒子群优化）和其他算法，无论是燃料成本和排放还是考虑到多目标优化的单目标优化。两个优化目标。验证了良好的妥协值，验证了DE-CQPSO算法在解决环境经济派遣问题方面的有效性和鲁棒性。该研究为解决环境经济派遣问题提供了一种新的研究方向。同时，它提供了一定程度的单位合理输出的参考。 （c）2020 elestvier有限公司保留所有权利。

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