In this paper, a swarm intelligence optimization algorithm is proposed as the Shrike Optimization Algorithm (SHOA). Many creatures living in a group and surviving for the next generation randomly search for food; they follow the best one in the swarm, called swarm intelligence. Swarm-based algorithms are designed to mimic creatures' behaviours, but in multimodal problem competition, they cannot find optimal solutions in some difficult cases. The main inspiration for the proposed algorithm is taken from the swarming behaviours of shrike birds in nature. The shrike birds are migrating from their territory to survive. However, the SHOA mimics the surviving behaviour of shrike birds for living, adaptation, and breeding. Two parts of optimization exploration and exploitation are designed by modelling shrike breeding and searching for foods to feed nestlings until they get ready to fly and live independently. This paper is a mathematical model for the SHOA to perform optimization. The SHOA benchmarked 19 well-known mathematical test functions, 10 from CEC-2019, and 12 from CEC-2022 most recent test functions, a total of 41 competitive mathematical test functions benchmarked and four real-world engineering problems with different conditions, both constrained and unconstrained. The statistical results obtained from the Wilcoxon sum ranking and Fridman test show that SHOA has a significant statistical superiority in handling the test benchmarks compared to competitor algorithms in multi-modal problems. The results for engineering optimization problems show the SHOA outperforms other nature-inspired algorithms in many cases.

翻译：本文提出了一种群体智能优化算法，称为伯劳鸟优化算法（SHOA）。许多群居生物为繁衍后代而随机觅食；它们追随群体中的最优个体，此现象称为群体智能。基于群体的算法旨在模拟生物行为，但在多模态问题竞争中，它们在某些复杂情况下无法找到最优解。本算法的主要灵感来源于自然界中伯劳鸟的群体行为。伯劳鸟为生存而迁徙领地。SHOA模拟了伯劳鸟为生存、适应和繁殖所表现的行为特征。算法通过建立伯劳鸟繁殖模型及为哺育雏鸟直至其具备独立飞行生存能力而觅食的行为模型，设计了优化探索与开发两个阶段。本文建立了SHOA执行优化的数学模型。SHOA对19个经典数学测试函数、CEC-2019的10个函数及CEC-2022最新的12个测试函数（共计41个竞争性数学测试函数）进行了基准测试，并在不同约束与非约束条件下验证了四个实际工程优化问题。Wilcoxon秩和检验与Fridman检验的统计结果表明，在处理多模态问题测试基准时，SHOA相较于竞争算法具有显著的统计优势。工程优化问题的结果显示，SHOA在多数情况下优于其他仿生自然算法。