This paper presents a novel bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO) for solving the challenging optimization problems. The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. This paper mathematically models these behaviors and benchmarks on a set of 38 test problems to ensure its applicability on different regions of search space. The RSO algorithm is compared with eight well-known optimization algorithms to validate its performance. It is then employed on six real-life constrained engineering design problems. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of proposed algorithm. The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms. Note that the source codes of the proposed technique are available at: http://www.dhimangaurav.com.

本文提出了一种新颖的生物启发式优化算法，称为大鼠群优化器（RSO），用于解决具有挑战性的优化问题。该优化器的主要灵感是大自然中大鼠的追赶和攻击行为。本文在一组38个测试问题上对这些行为和基准进行了数学建模，以确保其适用于搜索空间的不同区域。将RSO算法与八种著名的优化算法进行比较，以验证其性能。然后将其应用于六个现实生活中受限的工程设计问题。还研究了收敛性和计算分析，以测试所提出算法的探索，开发和局部最优避免。实验结果表明，与其他著名的优化算法相比，所提出的RSO算法在解决现实世界中的优化问题上非常有效。请注意，可以从以下网址获得所建议技术的源代码：http://www.dhimangaurav.com。