Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms.

中文翻译：

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生物启发算法的回顾与分类及其应用

科学家长期以来一直关注自然和生物学，以便了解和模拟复杂的现实世界问题的解决方案。仿生学的研究将自然界中发现的功能，生物学结构和功能以及组织原理与我们的现代技术联系起来，已经开发了许多数学和超启发式算法，以及从生命形式到人类技术的知识转移过程。仿生学研究的成果不仅包括物理产品，而且还包括可应用于不同领域的各种优化计算方法。相关算法大致可分为四类：基于进化的生物启发式算法，基于群体智能的生物启发式算法，基于生态的生物启发式算法和多目标生物启发式算法。生物启发算法，例如神经网络，蚁群算法，粒子群优化等已在科学，工程和业务管理的几乎每个领域中得到应用，相关出版物的数量急剧增加。本文对基于进化的生物启发算法，基于群体智能的生物启发算法，基于生态的生物启发算法和多目标生物启发算法的最新发展进行了系统，实用和全面的回顾。