CAJ | 학술논문

为避免早熟收敛和提升粒子在高维空间的搜索能力，文章提出了一种“自我”感知的高维混沌群体智能算法。首先，采用 pBest 和 gBest 混沌双扰动来增强粒子的搜索能力；其次，提出一种“自我”感知策略来帮助种群避免早熟收敛；最后，将三种不同微粒群优化(Particle Swarm Optimization，PSO)算法在旅行推销员问题(Traveling Salesman Problem，TSP)上进行了对比实验。实验结果显示“自我”感知的高维混沌群体智能算法简单、有效可行，值得推荐。
위피면조숙수렴화제승입자재고유공간적수색능력，문장제출료일충“자아”감지적고유혼돈군체지능산법。수선，채용 pBest 화 gBest 혼돈쌍우동래증강입자적수색능력；기차，제출일충“자아”감지책략래방조충군피면조숙수렴；최후，장삼충불동미립군우화(Particle Swarm Optimization，PSO)산법재여행추소원문제(Traveling Salesman Problem，TSP)상진행료대비실험。실험결과현시“자아”감지적고유혼돈군체지능산법간단、유효가행，치득추천。
To avoid the premature convergence and enhance the search capability of the high-dimensional space, a novel self-perception high-dimensional chaotic particle swarm algorithm was presented. Firstly, a double perturbation of pBest and gBest was used to enhance the searching capability of particles. Secondly, self-perception approach was proposed to help the particle swarm to avoid the premature convergence. Lastly, three discrete PSO variants were tested on the traveling salesman problem (TSP). Experimental results show that the self-perception high-dimensional chaotic particle swarm algorithm is simple, effective and promoting in a high-dimensional space.