Improved Hybrid Grey Wolf Optimization Algorithm Based on Dimension Learning-Based Hunting Search Strategy

An improved hybrid grey wolf optimization algorithm (IHGWO) is proposed to solve the problem of population diversity, imbalance exploration and development capabilities, premature convergence. The benefits from particle swarm a dimension learning-based hunting search strategy. In strategy, linear variable social learning self-learning are introduced improve population’s ability communicate information. individual position, current iteration optimal position wolves combined update information, thus strengthening communication between individuals population. neighborhoods built for each member, neighborhood members can share balance global local searches, maintain diversity. To validate algorithm, 23 typical benchmark functions, CEC2022 engineering sinusoidal low-order-polynomial prediction positioning error numerical control machine tools used optimize algorithm’s parameters. Results compared with those four other algorithms analyzed using Friedman’s statistical test. Experimental tests reveal that IHGWO has best overall function rating, an effectiveness 87.23%. parameter problem, mean square error, root goodness fit equation after 95.3761, 9.7661, 97.47%, respectively. These values superior algorithms, effectively demonstrating comprehensive performance applicability algorithm.