Optimization of furniture combination design and space configuration based on graph theory

Abstract

This paper is based on graph theory, combined with a particle swarm optimization algorithm to find the optimal solution in the whole particle swarm through continuous iteration and obtain the sum of squares of each variable in the particle cognitive domain. Searching for the global optimal position calculates the inertia weights in the particle swarm algorithm and changes the velocity and position of the particles based on the motion properties of graph theory to improve the global search ability. The domain competition operator is executed after particle swarm initialization, and a hybrid crossover strategy is used to select a crossover point, which improves the algorithm’s performance by exchanging information with the global optimum. Based on graph theory, the spatial configuration optimization of furniture combinations is scored, in which the average score of furniture space ranges from 3.3 to 6.2, and the average score of spatial configuration optimization ranges from 4.5 to 8. Therefore, based on graph theory, the reasonable furniture form for spatial layout optimization can fully reflect the practical function of the space, comfort, and the creation of a spatial atmosphere.

Keywords

• graph theory
• particle swarm optimization algorithm
• inertia weights
• spatial configuration optimization
• hybrid crossover strategy
• 65d17