In today's day and time solving real-world complex problems has become fundamentally vital and critical task. Many of these are combinatorial problems, where optimal solutions are sought rather than exact solutions. Traditional optimization methods are found to be effective for small scale problems. However, for real-world large scale problems, traditional methods either do not scale up or fail to obtain optimal solutions or they end-up giving solutions after a long running time. Even earlier artificial intelligence based techniques used to solve these problems could not give acceptable results. However, last two decades have seen many new methods in AI based on the characteristics and behaviors of the living organisms in the nature which are categorized as bio-inspired or nature inspired optimization algorithms. These methods, are also termed meta-heuristic optimization methods, have been proved theoretically and implemented using simulation as well used to create many useful applications. They have been used extensively to solve many industrial and engineering complex problems due to being easy to understand, flexible, simple to adapt to the problem at hand and most importantly their ability to come out of local optima traps. This local optima avoidance property helps in finding global optimal solutions. This paper is aimed at understanding how nature has inspired many optimization algorithms, basic categorization of them, major bio-inspired optimization algorithms invented in recent time with their applications.

中文翻译：

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仿生优化：用于优化的元启发式算法

在当今时代，解决现实世界中的复杂问题已成为根本上至关重要的任务。其中许多是组合问题，其中寻求最佳解决方案而不是精确解决方案。发现传统的优化方法对于小规模问题是有效的。然而，对于现实世界中的大规模问题，传统方法要么无法扩展，要么无法获得最佳解决方案，要么在长时间运行后最终给出解决方案。甚至用于解决这些问题的早期基于人工智能的技术也无法给出可接受的结果。然而，在过去的二十年里，人工智能中出现了许多基于自然界中生物体的特征和行为的新方法，这些方法被归类为生物启发式或自然启发式优化算法。这些方法，也称为元启发式优化方法，已在理论上得到证明并使用模拟实现，也用于创建许多有用的应用程序。由于它们易于理解、灵活、易于适应手头的问题，最重要的是它们能够摆脱局部最优陷阱，因此它们已被广泛用于解决许多工业和工程复杂问题。这种局部最优避免特性有助于找到全局最优解。本文旨在了解大自然如何启发许多优化算法、它们的基本分类、近期发明的主要仿生优化算法及其应用。由于它们易于理解、灵活、易于适应手头的问题，最重要的是它们能够摆脱局部最优陷阱，因此它们已被广泛用于解决许多工业和工程复杂问题。这种局部最优避免特性有助于找到全局最优解。本文旨在了解大自然如何启发许多优化算法、它们的基本分类、近期发明的主要仿生优化算法及其应用。由于它们易于理解、灵活、易于适应手头的问题，最重要的是它们能够摆脱局部最优陷阱，因此它们已被广泛用于解决许多工业和工程复杂问题。这种局部最优避免特性有助于找到全局最优解。本文旨在了解大自然如何启发许多优化算法、它们的基本分类、近期发明的主要仿生优化算法及其应用。