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Non-dominated sorted genetic algorithm-II algorithm-based multi-objective layout optimization of solid wood panels

Common solid wood panel defects affect the appearance of timber products and reduce their value for use. It is necessary to remove defects from solid wood panels to achieve a panel layout. A whole wooden beam column is cut into solid wood panels of different sizes, according to the requirements. Aiming to overcome problems of weak convergence ability, single-objective optimization, and the poor optimization effect of solid wood panel layout optimization based on a traditional genetic algorithm, an improved multi-objective solid wood panel layout optimization based on NSGA-II (Non-dominated sorted genetic algorithm-II) algorithm was proposed. Reverse learning was used to generate a reverse population to increase the search capability of the algorithm and to solve the problem of insufficient population diversity in the genetic algorithms. A combination of directional variation and uniform variation was used to improve the optimization effect and solve the problem of small individual differences in the evolution of the algorithm. The improved multi-objective optimization algorithm showed better optimization and stability than the NSGA-II algorithm. The number of convergence iterations was reduced and simultaneous optimization of multiple objectives can be realized.