TIGWO-DLH: A Chaotic Distributed Hybrid Grey Wolf Framework for Accelerated and Stable Global Optimization

The objective of optimization in nonlinear, high-dimensional, and multimodal search space continues to be an outstanding challenge when dealing with engineering, data science, and intelligent systems. Traditional optimization techniques often suffer from premature convergence or reduced search efficiency. This study presents a Hybrid TIGWO-DLH algorithm, which integrates the Tent-Improved Grey Wolf Optimizer (TIGWO) with Distributed Local Heuristic (DLH) strategies to overcome these limitations. The TIGWO-DLH framework is proposed, which combines chaotic initialization, adaptive convergence control, and distributed cooperative local learning, which can be used to achieve a better balance between exploration and exploitation. The performance of the algorithm is determined with reference to five standard benchmark functions Sphere, Rastrigin, Rosenbrock, Griewank, Ackley. The results of the experiments prove that TIGWO-DLH shows the best convergence rate of up to 12 percent and solution accuracy of up to 9 percent in comparison to baseline TIGWO and PSO models, making it robust and efficient in solving complex optimization problems.