Slime mold algorithm (SMA) is a recently developed meta-heuristic algorithm that mimics the ability of a single-cell organism (slime mold) for finding the shortest paths between food centers to search or explore a better solution. It is noticed that entrapment in local minima is the most common problem of these meta-heuristic algorithms. Thus, to further enhance the exploitation phase of SMA, this paper introduces a novel chaotic algorithm in which sinusoidal chaotic function has been combined with the basic SMA. The resultant chaotic slime mold algorithm (CSMA) is applied to 23 extensively used standard test functions and 10 multidisciplinary design problems. To check the validity of the proposed algorithm, results of CSMA has been compared with other recently developed and well-known classical optimizers such as PSO, DE, SSA, MVO, GWO, DE, MFO, SCA, CS, TSA, PSO-DE, GA, HS, Ray and Sain, MBA, ACO, and MMA. Statistical results suggest that chaotic strategy facilitates SMA to provide better performance in terms of solution accuracy. The simulation result shows that the developed chaotic algorithm outperforms on almost all benchmark functions and multidisciplinary engineering design problems with superior convergence.

粘菌算法 (SMA) 是最近开发的元启发式算法，它模仿单细胞生物体（粘菌）寻找食物中心之间的最短路径以搜索或探索更好解决方案的能力。值得注意的是，陷入局部最小值是这些元启发式算法中最常见的问题。因此，为了进一步加强 SMA 的开发阶段，本文介绍了一种新的混沌算法，其中正弦混沌函数已与基本 SMA 相结合。将由此产生的混沌黏菌算法 (CSMA) 应用于 23 个广泛使用的标准测试函数和 10 个多学科设计问题。为了检查所提出算法的有效性，将 CSMA 的结果与其他最近开发的著名经典优化器（如 PSO、DE、SSA、MVO、GWO、DE、MFO、SCA、CS、TSA、PSO-DE、GA、HS、Ray and Sain、MBA、ACO 和 MMA。统计结果表明，混沌策略有助于 SMA 在求解精度方面提供更好的性能。仿真结果表明，所开发的混沌算法在几乎所有基准函数和多学科工程设计问题上都表现出色，收敛性极佳。