粒子群算法及其哲学启示

Particle Swarm Optimization and Its Philosophical Implications

粒子群优化(PSO)算法是一种模拟自然生物群体(swarm)行为的优化技术。PSO算法源于对鸟群觅食行为的研究,该算法简单易实现,可调参数少,已得到广泛研究和应用。PSO算法不仅仅是种算法,更是一种学习和思维的创新,体现出学科之间交互所发生的一些突破。它不但是计算机理论上极大的理论创新,而且在哲学上也具有丰富的内涵。对此进行了论述。

Particle Swarm Optimization （PSO） algorithm is a simulation of natural biological groups （swarm） behavior of the optimization techniques. PSO algorithm is derived from the study on the foraging behavior of the flock, the algorithm is simple and easy to implement, less adjustable parameters, has been extensively studied and applied. PSO algorithm is not only algorithms, but also a learning and thinking, innovation, and reflects the interdisciplinary interaction between a numbers of breakthroughs has occurred. It is not only a theoretical computer, a great theoretical innovation, but also in philosophy also has a wealth of connotation.