An improved quantum particle swarm optimization algorithm for environmental economic dispatch

• Published in: Expert systems with applications Vol. 152; p. 113370
• Main Authors: Xin-gang, Zhao, Ji, Liang, Jin, Meng, Ying, Zhou
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.08.2020; Elsevier BV
• Subjects: Adaptive control; Algorithms; Carbon emission reduction; Convergence; Crossover operator; Differential evolution operator; Economic development; Economics; Environmental economic dispatch; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Multiple objective analysis; Optimization; Particle swarm optimization; Quantum particle swarm optimization; Quantum particle swarm optimization; Environmental economic dispatch; Crossover operator; Adaptive control; Differential evolution operator; Carbon emission reduction
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113370

•We consider both fuel costs and emissions, and find the best compromise value.•We introduce differential evolution operator into quantum particle swarm optimization (QPSO).•We introduce crossover operator into quantum particle swarm optimization (QPSO).•Adaptive control is adopted for crossover probability. 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.