In public environment design, landscape path planning and facility layout directly affect spatial function and aesthetic experience. Traditional methods are difficult to realize the optimal configuration in complex environments, which restricts the quality of environmental design. In this study, a landscape path planning and facility layout method based on the Improved Artificial Fish Swarm Optimization Algorithm (MCP-AFSA) for public environments is proposed. The search efficiency and path quality of the traditional artificial fish school algorithm in an obstacle environment are optimized by introducing two-way search and path smoothing strategies. Meanwhile, a public environment facility layout optimization model considering multiple objectives, service supply differentiation, demand precision and spatial condition refinement is constructed. The experimental results show that under the same accuracy requirements, the iteration number of MCP-AFSA algorithm is significantly lower than that of the standard PSO and GA algorithms, e.g., in the multi-peak function test, the minimum value of MCP-AFSA algorithm is infinitely close to 0 after 2,000 iterations, while PSO and GA algorithms stay at about 0.18 and 0.8, respectively. In the 150×150 simulation map, the average path score of the improved algorithm reaches 825.59, which is 9% higher than that of the traditional algorithm’s 757.45, and the path smoothing is significantly improved. In addition, in the significance test of public environment design indicators, the mean value of the proposed method is as high as 83.67%, which is much better than the comparison methods based on distance measurement (22.82%) and spatial growth simulation (45.55%). The synthesis shows that the proposed method has high practical value and application prospect in solving landscape path planning and facility layout problems in public environment design.
