A novel bio-inspired algorithm, namely, Dingo Optimization Algorithm (DOA), is proposed for solving optimization problems. The DOA mimics the social behavior of the Australian dingo dog. The algorithm is inspired by the hunting strategies of dingoes which are attacking by persecution, grouping tactics, and scavenging behavior. In order to increment the overall efficiency and performance of this method, three search strategies associated with four rules were formulated in the DOA. These strategies and rules provide a fine balance between intensification (exploitation) and diversification (exploration) over the search space. The proposed method is verified using several benchmark problems commonly used in the optimization field, classical design engineering problems, and optimal tuning of a Proportional-Integral-Derivative (PID) controller are also presented. Furthermore, the DOA’s performance is tested against five popular evolutionary algorithms. The results have shown that the DOA is highly competitive with other metaheuristics, beating them at the majority of the test functions.

中文翻译：

---

受野狗狩猎策略启发的工程设计优化仿生方法

提出了一种新的仿生算法，即 Dingo 优化算法 (DOA)，用于解决优化问题。DOA 模仿澳大利亚野狗的社会行为。该算法的灵感来自野狗的狩猎策略，这些策略通过迫害、分组策略和清除行为进行攻击。为了提高该方法的整体效率和性能，在 DOA 中制定了与四个规则相关的三个搜索策略。这些策略和规则在搜索空间的集约化（开发）和多样化（探索）之间提供了良好的平衡。使用优化领域中常用的几个基准问题、经典设计工程问题、还介绍了比例积分微分 (PID) 控制器的优化调整。此外，DOA 的性能针对五种流行的进化算法进行了测试。结果表明，DOA 与其他元启发式算法相比具有很强的竞争力，在大多数测试功能上都击败了它们。