The purpose of this research work is to solve the multi-objective optimal power flow (MO-OPF) problem using non-interactive approach. In this approach, the decision maker (DM) is not involved; however, the prior preference information is available to the DM. A satisficing function is offered to take care of the conflict between non-commensurable objectives, and the multi-objective problem is reformulated as a scalar optimization problem. This approach reduces the computation work involved for generating the Pareto front and for selecting the best satisficing solution. To attain the satisficing solutions, a hybrid optimization technique is applied, which integrates invasive weed optimization (IWO) with Powell’s pattern search (PPS) method. The IWO algorithm, utilized as the stochastic search technique, takes inspiration from the ability of weeds to adopt the environmental changes. Being a conjugate-based local search technique, the PPS method exhibits admirable exploitation search capability that further improves the solution provided by the IWO technique. The effectiveness of the proposed solution approach is confirmed by applying it to the three standard test systems, and the comparison is carried out with the well-established algorithms. Further, t test confirms the robustness of the proposed solution approach.

中文翻译：

---

求解多目标最优潮流问题的非交互式方法

本研究工作的目的是使用非交互式方法解决多目标最优潮流（MO-OPF）问题。在这种方法中，决策者 (DM) 不参与；然而，先验偏好信息对 DM 是可用的。提供了一个满足函数来处理不可公度目标之间的冲突，并将多目标问题重新表述为一个标量优化问题。这种方法减少了生成帕累托前沿和选择最佳满意解决方案所涉及的计算工作。为了获得令人满意的解决方案，应用了一种混合优化技术，该技术将侵入性杂草优化 (IWO) 与鲍威尔模式搜索 (PPS) 方法相结合。IWO 算法，用作随机搜索技术，从杂草适应环境变化的能力中汲取灵感。作为一种基于共轭的局部搜索技术，PPS 方法展示了令人钦佩的开发搜索能力，进一步改进了 IWO 技术提供的解决方案。所提出的解决方法的有效性通过将其应用于三个标准测试系统而得到证实，并与完善的算法进行了比较。此外，t 检验证实了所提出的解决方案方法的稳健性。并与完善的算法进行比较。此外，t 检验证实了所提出的解决方案方法的稳健性。并与完善的算法进行比较。此外，t 检验证实了所提出的解决方案方法的稳健性。