Handling Local Optima Trapped Situation by Improving the BAT Algorithm

This research proposes a chaos-enhanced bat algorithm to address global optimization problems.The Bat Algorithm (BA) is a popular swarm intelligence algorithms known for its effectiveness in solving optimization problems in both academic and real life scenarios.However, the classical approach is often susceptible to getting trapped in local optima.The current study presents an improved version of the Bat algorithm to overcome these limitations.We have enhanced the Bat Algorithm to boost its local search capabilities mitigate the issue of premature convergence.An improved search equation, incorporating additional valuable information, is introduced to generate better solutions.Additionally, a random walk Levy Flight mechanism is integrated into the Bat Algorithm to prevent it from being trapped in local optima.The proposed Bat Algorithm is applied to 10 benchmark functions within metaheuristic algorithms.While these algorithms have not been thoroughly investigated or manipulated in the past they prove useful in exploring the search space for optimal solutions.Experimental results demonstrate that the proposed algorithm outperforms established methods, including both original and enhanced approaches.