MRSO: Balancing Exploration and Exploitation through Modified Rat Swarm Optimization for Global Optimization

TL;DR

The paper introduces MRSO, an enhanced optimization algorithm inspired by rat behavior, which effectively balances exploration and exploitation, outperforming several existing algorithms in benchmark tests and engineering problems.

Contribution

MRSO is a novel modification of RSO that improves convergence and exploration, demonstrating superior performance in benchmark functions and engineering design tasks.

Findings

MRSO outperforms RSO in most benchmark functions.

MRSO achieves higher accuracy and avoids local optima.

MRSO surpasses eight recent algorithms in various tests.

Abstract

The rapid advancement of intelligent technology has led to the development of optimization algorithms that leverage natural behaviors to address complex issues. Among these, the Rat Swarm Optimizer (RSO), inspired by rats' social and behavioral characteristics, has demonstrated potential in various domains, although its convergence precision and exploration capabilities are limited. To address these shortcomings, this study introduces the Modified Rat Swarm Optimizer (MRSO), designed to enhance the balance between exploration and exploitation. MRSO incorporates unique modifications to improve search efficiency and durability, making it suitable for challenging engineering problems such as welded beam, pressure vessel, and gear train design. Extensive testing with classical benchmark functions shows that MRSO significantly improves performance, avoiding local optima and achieving higher accuracy in six out of nine multimodal functions and in all seven fixed-dimension multimodal functions. In the CEC 2019 benchmarks, MRSO outperforms the standard RSO in six out of ten functions, demonstrating superior global search capabilities. When applied to engineering design problems, MRSO consistently delivers better average results than RSO, proving its effectiveness. Additionally, we compared our approach with eight recent and well-known algorithms using both classical and CEC-2019 bench-marks. MRSO outperforms each of these algorithms, achieving superior results in six out of 23 classical benchmark functions and in four out of ten CEC-2019 benchmark functions. These results further demonstrate MRSO's significant contributions as a reliable and efficient tool for optimization tasks in engineering applications.