Enhancing Meta-Heuristic Algorithms by Dynamically Changing Exploration and Exploitation
Bhavpreet Singh, Jaspreet Singh Batth, Paurav Goel · 2024
This paper attempts to provide a new technique in this research that builds on the meta-heuristic approaches already in use to tackle optimization problems in various disciplines. Our suggested framework aims to advance optimization methods, taking cues from the Evolutionary Rao Algorithm (ERA) and its counterparts. Although meta-heuristic algorithms have demonstrated efficacy across several domains, they frequently display constraints in catering to all issue scenarios. Our approach aims to get over these limitations by improving upon the fundamentals of ERA and adding creative design improvements. We seek to prove the superiority and efficacy of our method by thorough experimentation and comparative analysis. The paper opens with a summary of ERA and its evolutionary forerunners, then delves into a thorough explanation of our methodology with a focus on improvements designed to lessen ERA's drawbacks. Next, we demonstrate the experimental configuration and assessment standards that were employed to evaluate our suggested algorithm's performance in comparison to other methods. Our research aims to develop more robust and dependable optimization tools by improving the capabilities of existing algorithms, which will stimulate innovation in a variety of industries.