College of Computer Science, Liaocheng University, Liaocheng 252059, China.
School of information science and engineering, Shandong Normal University, 250014, China.
Math Biosci Eng. 2019 Mar 29;16(4):2775-2794. doi: 10.3934/mbe.2019138.
In this study, a hybrid invasive weed optimization (HIWO) algorithm that hybridizes the invasive weed optimization (IWO) algorithm and genetic algorithm (GA) has been proposed to solve economic dispatch (ED) problems in power systems. In the proposed algorithm, the IWO algorithm is used as the main optimizer to explore the solution space, whereas the crossover and mutation operations of the GA are developed to significantly improve the optimization ability of IWO. In addition, an effective repair method is embedded in the proposed algorithm to repair infeasible solutions by handing various practical constraints of ED problems. To verify the optimization performance of the proposed algorithm and the effectiveness of the repair method, six ED problems in the different-scale power systems were tested and compared with other algorithms proposed in the literature. The experimental results indicated that the proposed HIWO algorithm can obtain the more economical dispatch solutions, and the proposed repair method can effectively repair each infeasible dispatch solution to a feasible solution. The convergence capability, applicability and effectiveness of HIWO were also demonstrated through the comprehensive comparison results.
在这项研究中，提出了一种混合入侵杂草优化（HIWO）算法，该算法将入侵杂草优化（IWO）算法和遗传算法（GA）进行了混合，以解决电力系统中的经济调度（ED）问题。在所提出的算法中，IWO 算法被用作主要的优化器来探索解空间，而 GA 的交叉和变异操作则被开发出来，以显著提高 IWO 的优化能力。此外，在所提出的算法中嵌入了一种有效的修复方法，通过处理 ED 问题的各种实际约束来修复不可行的解决方案。为了验证所提出算法的优化性能和修复方法的有效性，在不同规模的电力系统中测试了六个 ED 问题，并与文献中提出的其他算法进行了比较。实验结果表明，所提出的 HIWO 算法可以获得更经济的调度解决方案，并且所提出的修复方法可以有效地将每个不可行的调度解决方案修复为可行的解决方案。通过综合比较结果，还证明了 HIWO 的收敛能力、适用性和有效性。
