Colliding bodies optimization (CBO) is a recently proposed algorithm, and there are no algorithm-specific parameters that should be previously determined in updating equations of bodies. CBO has been used to solve various optimization problems because of its simple structure. However, CBO suffers from low convergence speed and premature convergence. To enhance CBO’s performance, a new variant named learning strategy based colliding bodies optimization (LSCBO), which is based on the learning strategy of the Teaching–learning-based optimization algorithm (TLBO), is proposed in this paper. In this method, a hybrid strategy combining the colliding process of CBO and the learning process of TLBO is proposed to generate new positions of the bodies. Compared with some other CBO variants, the guidance of the best individual is introduced to improve the convergence speed of CBO, and a random mutation method based on the historic information is designed to help bodies escape from local optima. Moreover, a new method for determining the mass of bodies is designed to avoid computation overflow. To evaluate the effectiveness of LSCBO, 47 benchmark functions and three real-world structural design problems are tested in the simulation experiments, and the results are compared with those of other well-known meta-heuristic algorithms. The statistical simulation results indicate that the performance of CBO is obviously improved by the developed method.

中文翻译：

---

一种增强的碰撞体优化及其应用

碰撞体优化 (CBO) 是最近提出的算法，在更新物体方程时，没有应事先确定的特定于算法的参数。CBO由于其结构简单，已被用于解决各种优化问题。然而，CBO 存在收敛速度慢和早熟收敛的问题。为了提高CBO的性能，本文提出了一种新的变体，称为基于学习策略的碰撞体优化（LSCBO），它基于基于教学的优化算法（TLBO）的学习策略。在该方法中，提出了一种结合 CBO 碰撞过程和 TLBO 学习过程的混合策略来生成新的物体位置。与其他一些 CBO 变体相比，引入最佳个体的引导来提高CBO的收敛速度，并设计了一种基于历史信息的随机变异方法来帮助身体摆脱局部最优。此外，还设计了一种确定物体质量的新方法，以避免计算溢出。为了评估 LSCBO 的有效性，在仿真实验中测试了 47 个基准函数和三个真实世界的结构设计问题，并将结果与​​其他著名的元启发式算法的结果进行了比较。统计仿真结果表明，所开发的方法显着提高了CBO的性能。一种确定物体质量的新方法旨在避免计算溢出。为了评估 LSCBO 的有效性，在仿真实验中测试了 47 个基准函数和三个真实世界的结构设计问题，并将结果与​​其他著名的元启发式算法的结果进行了比较。统计仿真结果表明，所开发的方法显着提高了CBO的性能。一种确定物体质量的新方法旨在避免计算溢出。为了评估 LSCBO 的有效性，在仿真实验中测试了 47 个基准函数和三个真实世界的结构设计问题，并将结果与​​其他著名的元启发式算法的结果进行了比较。统计仿真结果表明，所开发的方法显着提高了CBO的性能。