An improved hybrid grey wolf optimization algorithm (IHGWO) is proposed to solve the problem of population diversity, imbalance of exploration and development capabilities, and premature convergence. The algorithm ben...
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An improved hybrid grey wolf optimization algorithm (IHGWO) is proposed to solve the problem of population diversity, imbalance of exploration and development capabilities, and premature convergence. The algorithm benefits from particle swarm optimization and a dimension learning-based hunting search strategy. In the particle swarm algorithm search strategy, linear variable social learning and self-learning are introduced to improve the population's ability to communicate information. The individual position, current iteration optimal position, and optimal population position of grey wolves are combined to update the individual position information, thus strengthening the communication between individuals and the population. In the dimension learning-based hunting search strategy, neighborhoods are built for each search member, and neighborhood members can share information, balance global and local searches, and maintain diversity. To validate the algorithm, 23 typical benchmark functions, CEC2022 benchmark functions, and engineering problem sinusoidal low-order-polynomial prediction of positioning error of numerical control machine tools are used to optimize the algorithm's parameters. Results are compared with those from four other algorithms and analyzed using Friedman's statistical test. Experimental and statistical tests reveal that the IHGWO algorithm has the best overall benchmark function rating, with an overall effectiveness of 87.23%. In the engineering parameter optimization problem, the mean square error, root mean square error, and goodness of fit of the prediction equation after IHGWO algorithmoptimization are 95.3761, 9.7661, and 97.47%, respectively. These numerical values are superior to those of the compared algorithms, effectively demonstrating the comprehensive performance and applicability of the algorithm.
For the problem of geometric error prediction of CNC machine tools, an improvedhybridgreywolfoptimization (IHGWO) algorithm is proposed to optimize the geometric error modeling scheme of the support vector regress...
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For the problem of geometric error prediction of CNC machine tools, an improvedhybridgreywolfoptimization (IHGWO) algorithm is proposed to optimize the geometric error modeling scheme of the support vector regression machine (SVR). The predicted and measured values of the geometric error are combined to construct the fitness function. In IHGWO, principles of particle swarm optimization (PSO) algorithm and dimension learning-based hunting (DLH) search strategies are introduced while retaining the excellent greywolf position of the basic greywolfoptimization (GWO) algorithm. IHGWO algorithm uses Euclidean distance to construct the neighborhood of individual grey wolves, which enhances the ability to communicate between individual grey wolves and improves the convergence speed and accuracy of the algorithm. Predictive performance of SVR models using sum squared residual to quantify geometric error. Based on the screw theory, space models of geometric errors of CNC machine tools are established and combined with SVR models of geometric errors for compensation. Empirical evidence proves that the proposed method surpasses current error modeling methods in terms of precision and efficiency, as evidenced by a minimum reduction of 9% in circular trajectory error and a reduction to two overruns in S-shaped test pieces after error compensation. This research contributes to the field of CNC machine tool error modeling and has practical implications for manufacturing industries.
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