In this paper, a novel bio-inspired optimization algorithm is proposed, called Bombardier Beetle Optimizer (BBO). This type of species is very intelligent, which has an ability to defense and escape from predators. The principles of the former one is inspired by the defense mechanism of Bombardier Beetle against the predators, which the Bombardier Beetle triggers a toxic chemical spray when it feels threatened. This reaction occurs in a specialized reaction chamber inside its abdomen and includes a well regulated enzymatic mechanism, which comprises hot water vapor, oxygen, and irritating substances like p-benzoquinones. In addition, the proposed BBO simulates also the escape mechanism of Bombardier Beetle from predator, which it has the ability to calculate its distance from predator and it can fly away. The BBO is tested with optimizing Congress on Evolutionary Computation (CEC 2017) test bed suites. Moreover, it is compared against well-known metaheuristic optimization algorithms includes Chernobyl Disaster Optimizer (CDO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Bermuda Triangle Optimizer (BTO), Sperm Swarm Optimization (SSO) and Gravitational Search Algorithm (GSA). The outcomes of this paper prove the BBO's efficiency in which outperforms the other algorithms in terms of convergence rate and quality of results.

翻译：本文提出了一种新颖的仿生优化算法，称为投弹甲虫优化器。该物种具有高度智能，具备防御和逃离捕食者的能力。算法的设计原理首先受到投弹甲虫防御机制的启发：当感知威胁时，投弹甲虫会触发有毒化学喷雾。该反应发生在其腹部的特化反应腔内，包含一个调控良好的酶促机制，产生热水蒸气、氧气及对苯醌等刺激性物质。此外，所提出的BBO算法还模拟了投弹甲虫逃离捕食者的机制，即能够计算与捕食者的距离并飞行逃离。BBO在进化计算大会测试集上进行了优化性能验证，并与多种知名元启发式优化算法进行了对比，包括切尔诺贝利灾难优化器、灰狼优化器、粒子群优化、百慕大三角优化器、精子群优化及引力搜索算法。实验结果证明了BBO算法的有效性，其在收敛速度和结果质量方面均优于对比算法。