A novel algorithm for global optimization: Rat Swarm Optimizer

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.

Automated summary

This paper introduces a bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO), which is inspired by the chasing and attacking behaviors of rats in nature. With benchmarking on 38 test problems, the experimental results show that the RSO algorithm is highly effective in solving real-world optimization problems.