基于仿生学的优化算法及其在信号处理中的应用

仿生学优化算法是一类模仿生物行为和自然界现象的仿生算法,其目的是求解优化问题的全局最优解。本文首先介绍了各种仿生学优化算法的起源和基本原理,主要包括蚁群优化算法、粒子群优化算法、细菌觅食优化算法、蜂群优化算法、鱼群优化算法、萤火虫群优化算法、狼群优化算法、蝙蝠算法、鸡群优化算法、进化算法、免疫算法、克隆选择算法和小世界网络等。然后总结了仿生优化算法的研究现状,并给出了仿生优化算法在信号处理、图像处理、语音处理和通信网络等领域中的典型应用。最后,归纳了仿生学优化算法的特点,并对如何扩展其适用范围、探索新的仿生学优化算法提出了基本思路,对其发展进行了展望。

Bionic Optimization Algorithms and Their Applications in Signal Processing

Bionic optimization algorithm is a kind of bionic algorithm that imitates biological behaviors and natural phenomenon, whose aim is to find out the optimal solution of the target problem. In the paper, the origin and basic principles of various bionic optimization algorithms are first introduced, including ant colony algorithm, particle swarm algorithm, bacteria foraging algorithm, bee colony algorithm, fish swarm algorithm, glowworm swarm algorithm, wolf pack algorithm, bat algorithm, chicken swarm algorithm, evolutionary algorithm, immune algorithm, clonal selection algorithm, small-world network, and so on. Then, the state of the art of bionic optimization algorithms is reviewed, and some typical applications of bionic optimization algorithms in signal processing, image processing, speech processing and communication network are given. Finally, the features of bionic optimization algorithms are summarized, the basic ideas on how to extend their applications and explore new bionic optimization algorithms are presented, and the future developments of bionic optimization algorithms are also prospected.