Enhancing Unimodal Optimization: A Hybrid Algorithm based on CGJO

The Chaotic Golden Jackal Optimization (CGJO) algorithm is inspired by the intelligent andcooperative hunting strategies of wild golden jackals. In nature, these animals rely onteamwork, adaptability, and precise coordination to track and capture their prey. CGJO mirrorsthis behavior in its search process, allowing the algorithm to effectively explore a wide rangeof potential solutions. This collaborative approach helps it avoid one of the common pitfallsin optimization—getting stuck too early in suboptimal results, known as local optima. Whatsets CGJO apart is its integration of chaos theory into this natural framework. By introducingchaotic patterns into the algorithm, CGJO adds an element of controlled randomness thatimproves both the diversity of initial solutions and the algorithm’s adaptability throughout itsrun. This chaotic influence ensures that CGJO doesn’t just start strong, but continues to finetune and explore effectively as it progresses. The result is an optimization method that findsbetter solutions faster and with more consistency. In rigorous testing across a range of standardbenchmark functions and real-world applications, CGJO has demonstrated superior accuracy,quicker convergence, and more robust performance compared to both the original GoldenJackal Optimization and several other leading optimization techniques.