中文摘要：

仿生优化算法是模拟自然界中生物行为的随机搜索算法，可以用来解决现实中的许多优化问题。简要介绍了目前比较流行的四种新型仿生优化算法（蚁群算法、微粒群算法、人工免疫算法以及人工鱼群算法）的基本原理；然后深入分析了这些仿生优化算法的异同之处：这些算法都是一类不确定的算法，都是一类概率型的全局优化算法，都不依赖于优化问题本身的严格数学性质，都是一种基于多个智能体的智能算法，都具有本质并行性、突现性、进化性和稳健性，其不同性则主要体现在算法本身上；最后对这些仿生优化算法今后的发展方向进行了评述与展望。

英文摘要：

Bionic optimization algorithms are stochastic search methods that mimic the natural biological behavior of species. They are mainly applied to solve various optimization problems. This paper proposes the formulation of four recent biology - based algorithms ： ant colony algorithm, particle swarm optimization algorithm, artificial immune algorithm, and artificial fish - swarm algorithm. A brief description of each algorithm is presented firstly. Then, a detailed comparison and analysis of these bionic optimization algorithms are conducted. These bionic optimization algorithms are a kind of uncertain algorithm with great probability, and independent from the mathematical characters of different problems. These algorithms also have parallelism and robustness. The different points are embedded in the algorithms. Finally, some remarks on the further research contents and directions are discussed.