Abstract

In this paper, a new optimization algorithm called the Hitchcock bird-inspired algorithm (HBIA) is proposed. It is inspired by the aggressive bird behavior portrayed by Alfred Hitchcock in the 1963 thriller “The Birds.” It is noteworthy to emphasize that the bird’s behavior as shown in the movie is itself inspired by a considered natural birds behavior when faced with extreme conditions. HBIA is a stochastic swarm intelligence algorithm that captures the essence of the fictional behavior of the phenomenon of birds throughout the Hitchcock’s film and model an optimization mechanism. The algorithm is based on the attack pattern of birds in the film, which has the stages of lurking, attack and reorganization, defined by the initialization, movement strategies in the search space and strategy of local minimum escape, respectively. The technique has as differential the use of adaptive parameters, a discretized random initialization and the use of the beta distribution. In contrast to the existing ones, the proposed technique provides an efficient optimization in high-dimensionality cost functions, using adaptive parameters, a discretized random initialization and the use of the beta distribution. Its performance is analyzed and compared to classic techniques, such as PSO, ABC and CS, as well as to the existing adaptive techniques, such as sine cosine algorithm, whale optimization algorithm, teaching–learning-based optimization and vortex search. HBIA’s performance is investigated by several experiments implemented through eight cost functions. The results show that the HBIA can find more satisfactory solutions in large search spaces and high dimensionality of the evaluated cost functions when compared to the existing optimization methods.

中文翻译：

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一种新颖的元启发法，受希区柯克鸟的行为启发，有效地优化了高维大型搜索空间

摘要

本文提出了一种新的优化算法，称为希区柯克鸟启发算法（HBIA）。它的灵感来自阿尔弗雷德·希区柯克（Alfred Hitchcock）在1963年的惊悚片《鸟类》中表现出的具有侵略性的鸟类行为。值得注意的是，电影中的鸟类行为本身是受到极端条件下自然鸟类行为的启发。HBIA是一种随机群智能算法，可捕捉整个希区柯克电影中鸟类现象的虚构行为的本质，​​并为优化机制建模。该算法基于电影中鸟类的攻击模式，具有潜伏，攻击和重组的阶段，分别由初始化，搜索空间中的移动策略和局部最小逃生策略定义。该技术具有自适应参数的使用，离散化的随机初始化和beta分布的使用等作为差分。与现有技术相比，所提出的技术使用自适应参数，离散化随机初始化和β分布的使用，为高维成本函数提供了有效的优化。分析其性能，并将其与经典技术（如PSO，ABC和CS）以及现有的自适应技术（如正弦余弦算法，鲸鱼优化算法，基于学习的优化和涡流搜索）进行比较。HBIA的性能通过通过八个成本函数实施的几次实验进行了调查。