Affiliations: [a] Internet of Things, Wuxi University, Wuxi, Jiangsu, China | [b] Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China | [c] Artificial Intelligence and Data Science, Coventry University, Coventry, UK
Abstract: Economic dispatch problems (EDPs) can be reduced to non-convex constrained optimization problems, and most of the population-based algorithms are prone to have problems of premature and falling into local optimum when solving EDPs. Therefore, this paper proposes a hybrid quantum-behaved particle swarm optimization (HQPSO) algorithm to alleviate the above problems. In the HQPSO, the Solis and Wets local search method is used to enhance the local search ability of the QPSO so that the algorithm can find solutions that is close to optimal when the constraints are met, and two evolution operators are proposed and incorporated for the purpose of making a better balance between local search and global search abilities at the later search stage. The performance comparison is made among the HQPSO and the other ten population-based random search methods under two different experimental configurations and four different power systems in terms of solution quality, robustness, and convergence property. The experimental results show that the HQPSO improves the convergence properties of the QPSO and finally obtains the best total generation cost without violating any constraints. In addition, the HQPSO outperforms all the other algorithms on 7 cases of all 8 experimental cases in terms of global best position and mean position, which verifies the effectiveness of the algorithm.
Keywords: Constrained nonlinear optimization, hybrid quantum-behaved particle swarm optimization, economic dispatch problem, solis and wets local search
