利用群體智慧演算法來模擬控制人群的移動

Abstract

本論文以粒子最佳化演算法(Particle Swarm Optimization)為原型，提出了一個統一概念的模型，來模擬電腦圖學中人群的移動。根據粒子最佳化演算法的機制，一個人物(Particle)在群體(Swarm)中，可取得周圍環境的資訊，自動地尋得一條前往特定目標(Optimum)的路徑，然而，粒子最佳化演算法傳統上用以獲取最佳解，而非如本論文著重於產生粒子的路徑，因此，為了能夠讓人群中的個體有合適的路徑，我們提出了一個方法與粒子最佳化演算法的固有功能結合。我們所提出了模型簡單、統一且容易實做，不管是有障礙物的環境、動態目標物的環境、非單一群體的模擬或是不同地形的變化，利用此模型裡，我們皆可產生合理的、非決定性的(Non-deterministic)且無碰撞(Non-colliding)的路徑。

This paper presents a uniform conceptual model based on the particle swarm optimization (PSO) paradigm to simulate crowds in computer graphics. According to the mechanisms of PSO, each person (particle) in the crowd (swarm) can adopt the information to search a path from the initial position to the specified target (optimum) automatically. However, PSO aims to obtain the optimal solution, while the purpose of this study concentrates on the generated paths of particles. Hence, in order to generate appropriate paths of people in a crowd, we propose a method to employ the computational facilities provided in PSO. The proposed model is simple, uniform, and easy to implement. The results of simulations demonstrate that using PSO with the proposed technique can generate appropriate non-deterministic, non-colliding paths in several different scenarios, including static obstacles, moving targets, and multiple crowds.